07_Graph_Optimization_Problems_TSP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "f0F8I8wnm5gX",
    "outputId": "a4c4b069-041a-4636-cfd5-40c61263f83f"
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "try:\n",
    "  import osmnx as ox\n",
    "except:\n",
    "  # osmnx depends on the system package libspatialindex\n",
    "  !apt install libspatialindex-dev\n",
    "  !pip install osmnx\n",
    "  import osmnx as ox\n",
    "\n",
    "try: \n",
    "  import geopandas as gpd\n",
    "except: \n",
    "  !pip install geopandas \n",
    "  import geopandas as gpd\n",
    "  \n",
    "try:\n",
    "  import contextily as ctx \n",
    "except:\n",
    "  # install dependencies for contextily\n",
    "  !apt install libproj-dev proj-data proj-bin\n",
    "  !apt install libgeos-dev\n",
    "  !pip install cython\n",
    "  !pip install cartopy\n",
    "  # install contextily\n",
    "  !pip install contextily==1.0rc1 --no-use-pep517 --no-cache-dir\n",
    "  import contextily as ctx\n",
    "  \n",
    "import fiona\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "  \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pathlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ejhrKBqPng9F"
   },
   "source": [
    "# Traveling Salesman Problem\n",
    "The canonical Traveling Salesman Problem is stated as:\n",
    ">  \"Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?\"\n",
    "\n",
    "This is generalizable to finding the shortest [Hamiltonian cycle](http://mathworld.wolfram.com/HamiltonianCycle.html) on a fully connected graph (i.e. all nodes can be reached from all other nodes).\n",
    "\n",
    "This problem is [NP-hard](https://en.wikipedia.org/wiki/P_versus_NP_problem), meaning it is not possible for an algorithm to solve all instances of the problem quickly (i.e. in polynomial time). However, there are many approximate and heuristic approaches which can give reasonable solutions in shorter time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C6JmtPFto13K"
   },
   "outputs": [],
   "source": [
    "place_name = 'New York City, NY, United States'\n",
    "place_roads = ox.graph_from_place(place_name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k5W0RTsawXlE"
   },
   "outputs": [],
   "source": [
    "place_roads_nodes, place_roads_edges = ox.graph_to_gdfs(place_roads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "colab_type": "code",
    "id": "Hnp2qXbMss49",
    "outputId": "08d8d5d5-600f-482e-9623-e7691cf4e06c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd02d79edd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[10,10])\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "place_roads_edges.plot(ax=ax, color=[0.8, 0.8, 0.8], alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IoiI3SS1pIIs"
   },
   "source": [
    "Let's say you wanted to do a ice cream crawl: you want to visit every ice cream shop in a city. What is the shortest route that you would take that takes you to every ice cream shop in a city and brings you back to your starting point?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 779
    },
    "colab_type": "code",
    "id": "tAQK62C7pB7V",
    "outputId": "8323c432-4da4-4e10-bd43-8a9534da31da"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/osmnx/pois.py:370: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  gdf = gdf.loc[gdf[\"geometry\"].centroid.within(polygon)]\n"
     ]
    },
    {
     "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>osmid</th>\n",
       "      <th>geometry</th>\n",
       "      <th>addr:housenumber</th>\n",
       "      <th>addr:postcode</th>\n",
       "      <th>addr:street</th>\n",
       "      <th>amenity</th>\n",
       "      <th>name</th>\n",
       "      <th>opening_hours</th>\n",
       "      <th>outdoor_seating</th>\n",
       "      <th>element_type</th>\n",
       "      <th>...</th>\n",
       "      <th>contact:twitter</th>\n",
       "      <th>payment:contactless</th>\n",
       "      <th>payment:visa</th>\n",
       "      <th>drink:beer</th>\n",
       "      <th>drink:wine</th>\n",
       "      <th>diet:regular</th>\n",
       "      <th>nodes</th>\n",
       "      <th>building</th>\n",
       "      <th>height</th>\n",
       "      <th>nycdoitt:bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>419360637</th>\n",
       "      <td>419360637</td>\n",
       "      <td>POINT (-73.98941 40.73099)</td>\n",
       "      <td>95</td>\n",
       "      <td>10003</td>\n",
       "      <td>East 10th Street</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Sundaes and Cones</td>\n",
       "      <td>Mo-Th 11:00-22:30; Fr-Sa 11:00-23:30; Su 11:00...</td>\n",
       "      <td>bench</td>\n",
       "      <td>node</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419364720</th>\n",
       "      <td>419364720</td>\n",
       "      <td>POINT (-73.99015 40.68670)</td>\n",
       "      <td>81</td>\n",
       "      <td>11201</td>\n",
       "      <td>Bergen Street</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Van Leeuwen</td>\n",
       "      <td>Mo-Th 07:00-24:00; Fr 07:00-01:00; Sa 09:00-01...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>node</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>475485737</th>\n",
       "      <td>475485737</td>\n",
       "      <td>POINT (-73.99475 40.70321)</td>\n",
       "      <td>1</td>\n",
       "      <td>11201</td>\n",
       "      <td>Water Street</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Brooklyn Ice Cream Factory</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>node</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832243111</th>\n",
       "      <td>832243111</td>\n",
       "      <td>POINT (-73.96690 40.67500)</td>\n",
       "      <td>186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Underhill Avenue</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Blue Marble Ice Cream</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>node</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270769388</th>\n",
       "      <td>1270769388</td>\n",
       "      <td>POINT (-73.98710 40.72222)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Laboratorio del Gelato</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>node</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248496458</th>\n",
       "      <td>248496458</td>\n",
       "      <td>POINT (-73.97889 40.57344)</td>\n",
       "      <td>1023</td>\n",
       "      <td>11224</td>\n",
       "      <td>Boardwalk</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Coney's Cones</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>way</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2552762271, 2385856434, 2552762280, 238585640...</td>\n",
       "      <td>yes</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269318160</th>\n",
       "      <td>269318160</td>\n",
       "      <td>POINT (-73.97803 40.78405)</td>\n",
       "      <td>420</td>\n",
       "      <td>10024</td>\n",
       "      <td>Amsterdam Avenue</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Yogurtland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>way</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2745620618, 2745620615, 2745620614, 274562061...</td>\n",
       "      <td>yes</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1032663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280470545</th>\n",
       "      <td>280470545</td>\n",
       "      <td>POINT (-73.85515 40.74309)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>Lemon Ice King of Corona</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>way</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[2845825762, 2845825886, 2845826004, 284582590...</td>\n",
       "      <td>yes</td>\n",
       "      <td>6.8</td>\n",
       "      <td>4047812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576014904</th>\n",
       "      <td>576014904</td>\n",
       "      <td>POINT (-74.04642 40.68968)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>way</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[5525750139, 5525750140, 5525750141, 552575014...</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724196597</th>\n",
       "      <td>724196597</td>\n",
       "      <td>POINT (-73.99090 40.73015)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ice_cream</td>\n",
       "      <td>La Newyorkina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>way</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[6791755732, 6791755733, 6791755734, 679175573...</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>138 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 osmid                    geometry addr:housenumber  \\\n",
       "419360637    419360637  POINT (-73.98941 40.73099)               95   \n",
       "419364720    419364720  POINT (-73.99015 40.68670)               81   \n",
       "475485737    475485737  POINT (-73.99475 40.70321)                1   \n",
       "832243111    832243111  POINT (-73.96690 40.67500)              186   \n",
       "1270769388  1270769388  POINT (-73.98710 40.72222)              NaN   \n",
       "...                ...                         ...              ...   \n",
       "248496458    248496458  POINT (-73.97889 40.57344)             1023   \n",
       "269318160    269318160  POINT (-73.97803 40.78405)              420   \n",
       "280470545    280470545  POINT (-73.85515 40.74309)              NaN   \n",
       "576014904    576014904  POINT (-74.04642 40.68968)              NaN   \n",
       "724196597    724196597  POINT (-73.99090 40.73015)              NaN   \n",
       "\n",
       "           addr:postcode       addr:street    amenity  \\\n",
       "419360637          10003  East 10th Street  ice_cream   \n",
       "419364720          11201     Bergen Street  ice_cream   \n",
       "475485737          11201      Water Street  ice_cream   \n",
       "832243111            NaN  Underhill Avenue  ice_cream   \n",
       "1270769388           NaN               NaN  ice_cream   \n",
       "...                  ...               ...        ...   \n",
       "248496458          11224         Boardwalk  ice_cream   \n",
       "269318160          10024  Amsterdam Avenue  ice_cream   \n",
       "280470545            NaN               NaN  ice_cream   \n",
       "576014904            NaN               NaN  ice_cream   \n",
       "724196597            NaN               NaN  ice_cream   \n",
       "\n",
       "                                  name  \\\n",
       "419360637            Sundaes and Cones   \n",
       "419364720                  Van Leeuwen   \n",
       "475485737   Brooklyn Ice Cream Factory   \n",
       "832243111        Blue Marble Ice Cream   \n",
       "1270769388      Laboratorio del Gelato   \n",
       "...                                ...   \n",
       "248496458                Coney's Cones   \n",
       "269318160                   Yogurtland   \n",
       "280470545     Lemon Ice King of Corona   \n",
       "576014904                          NaN   \n",
       "724196597                La Newyorkina   \n",
       "\n",
       "                                                opening_hours outdoor_seating  \\\n",
       "419360637   Mo-Th 11:00-22:30; Fr-Sa 11:00-23:30; Su 11:00...           bench   \n",
       "419364720   Mo-Th 07:00-24:00; Fr 07:00-01:00; Sa 09:00-01...             NaN   \n",
       "475485737                                                 NaN             NaN   \n",
       "832243111                                                 NaN             yes   \n",
       "1270769388                                                NaN             NaN   \n",
       "...                                                       ...             ...   \n",
       "248496458                                                 NaN              no   \n",
       "269318160                                                 NaN             NaN   \n",
       "280470545                                                 NaN             NaN   \n",
       "576014904                                                 NaN             NaN   \n",
       "724196597                                                 NaN             NaN   \n",
       "\n",
       "           element_type  ... contact:twitter payment:contactless payment:visa  \\\n",
       "419360637          node  ...             NaN                 NaN          NaN   \n",
       "419364720          node  ...             NaN                 NaN          NaN   \n",
       "475485737          node  ...             NaN                 NaN          NaN   \n",
       "832243111          node  ...             NaN                 NaN          NaN   \n",
       "1270769388         node  ...             NaN                 NaN          NaN   \n",
       "...                 ...  ...             ...                 ...          ...   \n",
       "248496458           way  ...             NaN                 NaN          NaN   \n",
       "269318160           way  ...             NaN                 NaN          NaN   \n",
       "280470545           way  ...             NaN                 NaN          NaN   \n",
       "576014904           way  ...             NaN                 NaN          NaN   \n",
       "724196597           way  ...             NaN                 NaN          NaN   \n",
       "\n",
       "           drink:beer drink:wine diet:regular  \\\n",
       "419360637         NaN        NaN          NaN   \n",
       "419364720         NaN        NaN          NaN   \n",
       "475485737         NaN        NaN          NaN   \n",
       "832243111         NaN        NaN          NaN   \n",
       "1270769388        NaN        NaN          NaN   \n",
       "...               ...        ...          ...   \n",
       "248496458         NaN        NaN          NaN   \n",
       "269318160         NaN        NaN          NaN   \n",
       "280470545         NaN        NaN          NaN   \n",
       "576014904         NaN        NaN          NaN   \n",
       "724196597         NaN        NaN          NaN   \n",
       "\n",
       "                                                        nodes building height  \\\n",
       "419360637                                                 NaN      NaN    NaN   \n",
       "419364720                                                 NaN      NaN    NaN   \n",
       "475485737                                                 NaN      NaN    NaN   \n",
       "832243111                                                 NaN      NaN    NaN   \n",
       "1270769388                                                NaN      NaN    NaN   \n",
       "...                                                       ...      ...    ...   \n",
       "248496458   [2552762271, 2385856434, 2552762280, 238585640...      yes    7.4   \n",
       "269318160   [2745620618, 2745620615, 2745620614, 274562061...      yes   17.5   \n",
       "280470545   [2845825762, 2845825886, 2845826004, 284582590...      yes    6.8   \n",
       "576014904   [5525750139, 5525750140, 5525750141, 552575014...      yes    NaN   \n",
       "724196597   [6791755732, 6791755733, 6791755734, 679175573...      yes    NaN   \n",
       "\n",
       "           nycdoitt:bin  \n",
       "419360637           NaN  \n",
       "419364720           NaN  \n",
       "475485737           NaN  \n",
       "832243111           NaN  \n",
       "1270769388          NaN  \n",
       "...                 ...  \n",
       "248496458       3000000  \n",
       "269318160       1032663  \n",
       "280470545       4047812  \n",
       "576014904           NaN  \n",
       "724196597           NaN  \n",
       "\n",
       "[138 rows x 70 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "place_ice_cream = ox.pois_from_place(place_name, tags={\"amenity\":\"ice_cream\"})\n",
    "\n",
    "#some of the ice cream shops return polygons instead of points, so we need to take their centroids\n",
    "place_ice_cream = place_ice_cream.to_crs(\"epsg:3857\") #projecting to Web-Mercator for more accurate centroids\n",
    "place_ice_cream[\"geometry\"] = place_ice_cream[\"geometry\"].centroid\n",
    "place_ice_cream = place_ice_cream.to_crs(\"epsg:4326\") #projecting back to lat/long\n",
    "\n",
    "place_ice_cream"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "id": "DSEkty3Lr0AX",
    "outputId": "5fbf5594-6005-4b2c-93a2-0854624c22bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  42430886,   42492239,  999184395,   42474968, 7465391419,\n",
       "       3004844986, 2698702581,   42854333, 6406731704,   42428799,\n",
       "       4205565536, 5115916405, 5482687360, 4376122105,   42875051,\n",
       "       4216045804,   42870183,   42466073, 6638433920, 3891969471,\n",
       "         42469645, 4964127740,   42434959, 6210777837,   42435663,\n",
       "       6263083540, 6597498400, 5396215013,   42807319,   42876552,\n",
       "         42439406,   42466011, 7654949115, 7492443638, 7570756477,\n",
       "         42732035, 4544455461, 3834459546, 5510458199, 4320028830,\n",
       "         42827392, 5490750794,   42906454, 2111006220, 6406731684,\n",
       "         42469492, 4115968602,   42506318,   42438034,   42431712,\n",
       "         42453226, 4708375542,   42826882, 7728427256, 7229660637,\n",
       "         42491627, 5159433792,   42807083,   42807108,   42487567,\n",
       "       5482727326,   42827669,   42885444, 2484549752, 5471772227,\n",
       "         42497261, 5482174044,   42495404,   42512343, 6369613026,\n",
       "       3891969467,   42843615, 6814288111, 1372888911, 6816458072,\n",
       "         42487583, 6450635184,   42487597,   42497631,   42433551,\n",
       "         42848654,   42442848, 1815133244, 3783144655,   42438034,\n",
       "       4235734223, 7641718028, 7641718028, 7113914110, 7134855841,\n",
       "         42492250, 3832902959, 5813416225, 5391643301, 5288127945,\n",
       "       4918486816, 2823833443,   42443680,   42469637,   42432066,\n",
       "         42469329, 6683030360,   42475807, 7103815237,   42445413,\n",
       "       6703945639, 6710581359, 4407705329, 4407735207, 4407735215,\n",
       "         42817136, 6774630482, 6656645223,   42464864, 4548586067,\n",
       "       5515778108,   42479281,   42468965, 6980049855,   42428807,\n",
       "       5829779508,   42899537, 5036727638,   42428385, 4300912707,\n",
       "         42930068, 5488535945,   42430253, 6207264927, 7346811818,\n",
       "       2504466035,   42847684,   42844568, 2385856616, 6207264916,\n",
       "         42874993,   42471915, 5784518510])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ice_cream_nodes = ox.get_nearest_nodes(place_roads, place_ice_cream.geometry.x, place_ice_cream.geometry.y)\n",
    "ice_cream_nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7vjZXdo4wwx0"
   },
   "source": [
    "## Exercise\n",
    "Plot the locations of the ice cream shops on the map of the roads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wUuhBee2w9Bd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qF1NSIerw-j4"
   },
   "source": [
    "## Compute shortest path matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 399
    },
    "colab_type": "code",
    "id": "q2c8QobPsq8e",
    "outputId": "86cd1497-74cc-4b3e-f8e2-9d1155adecd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.   ,  5703.569,  4755.273, ..., 13386.123,  8574.68 ,\n",
       "          243.69 ],\n",
       "       [ 5291.002,     0.   ,  2185.151, ..., 14342.929,  3436.057,\n",
       "         5194.866],\n",
       "       [ 4694.963,  2337.929,     0.   , ..., 14078.81 ,  4085.641,\n",
       "         4598.827],\n",
       "       ...,\n",
       "       [13421.936, 14433.879, 14049.068, ...,     0.   , 17526.58 ,\n",
       "        13661.445],\n",
       "       [ 8455.659,  3341.345,  4167.891, ..., 17540.166,     0.   ,\n",
       "         8359.523],\n",
       "       [  243.69 ,  5607.433,  4659.137, ..., 13629.813,  8478.544,\n",
       "            0.   ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shortest_path_matrix = np.zeros([len(ice_cream_nodes),len(ice_cream_nodes)])\n",
    "for idx_i, orig in enumerate(ice_cream_nodes):\n",
    "    shortest_paths = nx.single_source_dijkstra_path_length(place_roads, orig, weight='length')\n",
    "    for idx_j, dest in enumerate(ice_cream_nodes):\n",
    "        shortest_path_matrix[idx_i, idx_j] = shortest_paths[dest]\n",
    "shortest_path_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dx6x4175wv90"
   },
   "outputs": [],
   "source": [
    "ice_cream_graph = nx.from_numpy_matrix(shortest_path_matrix, create_using=nx.MultiDiGraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "QXZedXvnzGX3",
    "outputId": "6927e94d-aa98-453a-f714-1cfdf650f7a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new graph indexes from 0\n",
    "ice_cream_graph.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "rUufs51xteVN",
    "outputId": "75cdee08-8b22-4f0f-a9da-ab1dfb64676a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((42430886, 42492239, 999184395, 42474968, 7465391419, 3004844986, 2698702581, 42854333, 6406731704, 42428799, 4205565536, 5115916405, 5482687360, 4376122105, 42875051, 4216045804, 42870183, 42466073, 6638433920, 3891969471, 42469645, 4964127740, 42434959, 6210777837, 42435663, 6263083540, 6597498400, 5396215013, 42807319, 42876552, 42439406, 42466011, 7654949115, 7492443638, 7570756477, 42732035, 4544455461, 3834459546, 5510458199, 4320028830, 42827392, 5490750794, 42906454, 2111006220, 6406731684, 42469492, 4115968602, 42506318, 42438034, 42431712, 42453226, 4708375542, 42826882, 7728427256, 7229660637, 42491627, 5159433792, 42807083, 42807108, 42487567, 5482727326, 42827669, 42885444, 2484549752, 5471772227, 42497261, 5482174044, 42495404, 42512343, 6369613026, 3891969467, 42843615, 6814288111, 1372888911, 6816458072, 42487583, 6450635184, 42487597, 42497631, 42433551, 42848654, 42442848, 1815133244, 3783144655, 4235734223, 7641718028, 7113914110, 7134855841, 42492250, 3832902959, 5813416225, 5391643301, 5288127945, 4918486816, 2823833443, 42443680, 42469637, 42432066, 42469329, 6683030360, 42475807, 7103815237, 42445413, 6703945639, 6710581359, 4407705329, 4407735207, 4407735215, 42817136, 6774630482, 6656645223, 42464864, 4548586067, 5515778108, 42479281, 42468965, 6980049855, 42428807, 5829779508, 42899537, 5036727638, 42428385, 4300912707, 42930068, 5488535945, 42430253, 6207264927, 7346811818, 2504466035, 42847684, 42844568, 2385856616, 6207264916, 42874993, 42471915, 5784518510))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename node labels using original labels\n",
    "ice_cream_graph = nx.relabel_nodes(ice_cream_graph,{k:v for k, v in zip(ice_cream_graph.nodes, ice_cream_nodes)})\n",
    "ice_cream_graph.nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IkkkXc4YzRX4"
   },
   "source": [
    "## Exercise\n",
    "Implement each of the following methods to see how good of a TSP path you can obtain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s5iJS8jbyBFu"
   },
   "source": [
    "## Method 1: Random\n",
    "Let's start by setting a baseline; how well can we do by starting at a random node and choosing a random node out of the ones remaining each time? \n",
    "\n",
    "After you find the path, draw it on the map and print its length. (You don't need to draw the actual roads taken, just draw lines between the nodes.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 5510458199]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path  = list(ice_cream_graph.nodes)\n",
    "np.random.shuffle(path)\n",
    "path.append(path[0])\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2026463.8889999997"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_length = 0\n",
    "\n",
    "for idx in range(len(path) - 1):\n",
    "    orig = path[idx]\n",
    "    dest = path[idx + 1]\n",
    "    length = ice_cream_graph.get_edge_data(orig, dest, 0)['weight']\n",
    "    total_length += length \n",
    "total_length  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Method 2: Greedy\n",
    "Now, let's try to choose nodes more intelligently: start at a random node again, but instead of choosing a random node each time, always choose the node closest to the current node each time.\n",
    "\n",
    "Again, draw the path on the map and print its length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weight': 3086.632}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_dict = ice_cream_graph.get_edge_data(42497631, 42471915, 0)\n",
    "dist_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42439406\n"
     ]
    }
   ],
   "source": [
    "test_path = list(ice_cream_graph.nodes)\n",
    "np.random.shuffle(test_path)\n",
    "test_path.append(path[0])\n",
    "print(test_path[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_closest(start, path, path_result):\n",
    "    max_dist = float('inf')\n",
    "    best_dist = 0\n",
    "    closest_dest = 0.0\n",
    "    for idx, d in enumerate(path):\n",
    "        if d not in path_result:\n",
    "            dist_dict = ice_cream_graph.get_edge_data(start, d, 0)\n",
    "            if(dist_dict is not None and dist_dict['weight'] < max_dist):\n",
    "                dist = dist_dict['weight']\n",
    "                closest_dest = d\n",
    "                max_dist = dist\n",
    "                best_dist = dist_dict['weight']\n",
    "                \n",
    "    return closest_dest, best_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_path_length(path): \n",
    "    orig = path[0]\n",
    "    path_result = [orig]\n",
    "    total_length = 0\n",
    "\n",
    "    dest, dist = get_closest(orig, path, path_result)\n",
    "    total_length += dist\n",
    "    path_result.append(dest)\n",
    "\n",
    "    while(len(path_result) < len(path)):\n",
    "        dest, dist = get_closest(dest, path, path_result)\n",
    "        total_length += dist\n",
    "        path_result.append(dest)\n",
    "\n",
    "    #path\n",
    "    #path_result   \n",
    "    return total_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_path(path): \n",
    "    orig = path[0]\n",
    "    path_result = [orig]\n",
    "\n",
    "    dest, dist = get_closest(orig, path, path_result)\n",
    "    path_result.append(dest)\n",
    "\n",
    "    while(len(path_result) < len(path)):\n",
    "        dest, dist = get_closest(dest, path, path_result)\n",
    "        path_result.append(dest)\n",
    "\n",
    "    #path\n",
    "    #path_result   \n",
    "    return path_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "333441.1689999999\n"
     ]
    }
   ],
   "source": [
    "print(get_path_length(test_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printRoutes(path, ogNetwork):\n",
    "    routes = []\n",
    "    for idx in range(len(path)-1):\n",
    "        routes.append(nx.shortest_path(ogNetwork, path[idx], path[idx+1], weight='length'))\n",
    "    fig, ax = ox.plot_graph_routes(ogNetwork, routes, route_color='red', node_size=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'is_multigraph'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-786734c4b65e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprintRoutes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-22-b10a865fcc54>\u001b[0m in \u001b[0;36mprintRoutes\u001b[0;34m(path, ogNetwork)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mroutes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mroutes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshortest_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mogNetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_graph_routes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mogNetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroutes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mroute_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/generic.py\u001b[0m in \u001b[0;36mshortest_path\u001b[0;34m(G, source, target, weight, method)\u001b[0m\n\u001b[1;32m    170\u001b[0m                 \u001b[0mpaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbidirectional_shortest_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'dijkstra'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 172\u001b[0;31m                 \u001b[0mpaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdijkstra_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# method == 'bellman-ford':\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m                 \u001b[0mpaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbellman_ford_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/weighted.py\u001b[0m in \u001b[0;36mdijkstra_path\u001b[0;34m(G, source, target, weight)\u001b[0m\n\u001b[1;32m    165\u001b[0m     \"\"\"\n\u001b[1;32m    166\u001b[0m     (length, path) = single_source_dijkstra(G, source, target=target,\n\u001b[0;32m--> 167\u001b[0;31m                                             weight=weight)\n\u001b[0m\u001b[1;32m    168\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/weighted.py\u001b[0m in \u001b[0;36msingle_source_dijkstra\u001b[0;34m(G, source, target, cutoff, weight)\u001b[0m\n\u001b[1;32m    478\u001b[0m     \"\"\"\n\u001b[1;32m    479\u001b[0m     return multi_source_dijkstra(G, {source}, cutoff=cutoff, target=target,\n\u001b[0;32m--> 480\u001b[0;31m                                  weight=weight)\n\u001b[0m\u001b[1;32m    481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/weighted.py\u001b[0m in \u001b[0;36mmulti_source_dijkstra\u001b[0;34m(G, sources, target, cutoff, weight)\u001b[0m\n\u001b[1;32m    735\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msources\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m     \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_weight_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    738\u001b[0m     \u001b[0mpaths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msource\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msources\u001b[0m\u001b[0;34m}\u001b[0m  \u001b[0;31m# dictionary of paths\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m     dist = _dijkstra_multisource(G, sources, weight, paths=paths,\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/networkx/algorithms/shortest_paths/weighted.py\u001b[0m in \u001b[0;36m_weight_function\u001b[0;34m(G, weight)\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0;31m# string representing the edge attribute containing the weight of\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \u001b[0;31m# the edge.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_multigraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'is_multigraph'"
     ]
    }
   ],
   "source": [
    "printRoutes(get_path(test_path), path)"
   ]
  },
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    "## Method 3: Random with 2-opt swapping\n",
    "\n",
    "You may have noticed that both paths contain a lot of edges that cross each other, which is nonideal. However, there exists an algorithm to remove all the paths that cross each other from a Hamiltonian cycle: the [2-opt](https://en.wikipedia.org/wiki/2-opt) algorithm. We can use that to our advantage here.\n",
    "\n",
    "Start by generating a random Hamiltonian cycle like in method 1, but this time, use the 2-opt algorithm to optimize it further. Again, draw it on the map and print its length."
   ]
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    "## Method 4: Open-ended\n",
    "\n",
    "Although the 2-opt swaps reduce the length of the Hamiltonian cycle by quite a lot, they almost never provide the optimal solution. See if you can use another method to produce a Hamiltonian cycle shorter than the one you got with method 3. Some options to explore include:\n",
    "\n",
    "- [3-opt](https://en.wikipedia.org/wiki/3-opt)\n",
    "- [Multi-fragment algorithm](https://en.wikipedia.org/wiki/Multi-fragment_algorithm) with 2- or 3-opt swapping\n",
    "- [Simulated annealing](https://en.wikipedia.org/wiki/Simulated_annealing)\n",
    "\n",
    "The [TSP Wikipedia page](https://en.wikipedia.org/wiki/Travelling_salesman_problem) has many other algorithms that could be of use to you as well.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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