Traveling Salesman Problem.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As someone whose primarily education was statistics and research, in transition to data science and development, my main insight was the limitation of the computational power of the machine and the need for optimisation. Working in a field where very serious state policy were made base on the research on several thousands of carefully sampled respondents, the questions of computational limitation and time complexity was never raised. Preparing myself for the job interviews, one of the first exercises I have done was a simple plotting of time complexity in the most simple task. I just wanted to see time of the execution with raising iterations when we have exponential versus linear and logarithmic complexity. I did the most simple execution task. With the 100k of iteration with the most simple exponenctial function, waiting time dramatically extended. I believe that noise blue line captures some kind of the oscilation in the cpu stress. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:23:33.313073Z",
     "start_time": "2022-02-07T09:37:03.648793Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import math\n",
    "from matplotlib import pylab as plt\n",
    "import seaborn as sns\n",
    "\n",
    "linear_complexity=[]\n",
    "exponential_complexity=[]\n",
    "logarithmic_complexity=[]\n",
    "\n",
    "for n in range(1,100000):\n",
    "    start=time.time()\n",
    "    zbir=n+n\n",
    "    end=time.time()\n",
    "    linear_complexity.append(end-start)\n",
    "for n in range(1,100000):\n",
    "    start=time.time()\n",
    "    stepen=n**n\n",
    "    end=time.time()\n",
    "    exponential_complexity.append(end-start)\n",
    "for n in range(1,100000):\n",
    "    start=time.time()\n",
    "    logaritam=math.log2(n)\n",
    "    end=time.time()\n",
    "    logarithmic_complexity.append(end-start)\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize=(20, 10))\n",
    "ax.plot(linear_complexity, label='Time needed for calculation n+n O (n)', color='red')\n",
    "ax.plot(exponential_complexity, label='Time needed for calculation $n^2$ O($n^2$)', color='blue')\n",
    "ax.plot(logarithmic_complexity, label='Time needed for calculation $log_{n}$ O($log_{n}$)', color='green')\n",
    "plt.xlabel('n')\n",
    "plt.ylabel('time')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another big issue that was raised was the mathematical formalism and theory behind NP-complete problems and NP-hard problems. The way of verification of some problems is very easy but finding the solution for them is in some cases very computational demanding and almost impossible in practise. For example, you can easily check whether the computer found your neighbour in some search by address and the floor of leaving, but actual finding your neighboor in the population of habitants could be very demanding and can depend on the size of population of habitants if you just take a data of the door to door distance. But could we predict the function of time that depends of the number of habitants? \n",
    "\n",
    "Let's try define the concepts (if somebody reads this, correct me if I am wrong somewhere). The class of questions for which an answer can be verified in polynomial time is NP, which stands for \"nondeterministic polynomial time\". An answer to the P versus NP question would determine whether problems that can be verified in polynomial time can also be solved in polynomial time. Polynomial time simply means that times could be exponentialy raised in corespodence with the task complexity demand but that we can predict this time, as much long as it is. \"NP-hard\" does not stand for \"non-polynomial\" when in fact it stands for \"non-deterministic polynomial acceptable problems\", which means that we could not predict how much the time need will raised according to the changes in the input task.\n",
    "\n",
    "If it turns out that P ≠ NP, which is widely believed, it would mean that there are problems in NP that are harder to compute than to verify: they could not be solved in polynomial time, but the answer could be verified in polynomial time. Here we will illustrate one famous NP-hard problem, Traveling Salesman Problem. It is said that this is NP-complete and NP-hard problem. \n",
    "\n",
    "A problem is NP-complete (definition from Wikipedia) when:\n",
    "\n",
    "a) it is a problem for which the correctness of each solution can be verified quickly (namely, in polynomial time) and a brute-force search algorithm can find a solution by trying all possible solutions.\n",
    "\n",
    "b)the problem can be used to simulate every other problem for which we can verify quickly that a solution is correct. In this sense, NP-complete problems are the hardest of the problems to which solutions can be verified quickly. If we could find solutions of some NP-complete problem quickly, we could quickly find the solutions of every other problem to which a given solution can be easily verified.\n",
    "\n",
    "A problem(1) is NP-hard when every other similar problem (x) in NP can be reduced in polynomial time to the our previous proiblem; that is, assuming a solution for problem(1) takes 1 unit time, problem(1)'s solution can be used to solve problem x in polynomial time. \n",
    "\n",
    "Ok, let's try to be a little bit more pragmatical and related to more concrete experience. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine you are a salesman and you should visit the x number of cities. You want to make the shortest possible tour. How would you do this? I beleive that you would rely on the computational powers of computer and that you calculate all possible routes and than choose the shortest one (brute force approach).\n",
    "\n",
    "With this short example we want to illustrate need for optimisation and heruistics. Brute force approach in Traveller Salesman problem results in huge computational time, while simple greedy algorithm gives unbelievable improvement. This is NP-hard problem is very easily understand. In the next code which is basically the code from the very good book of Felix Bittmann (Python for Scientifinc and Engeneering Applications) which I recommend, we will see how simple optimisation strategy could gives us not 100% accurate but adequate solution in reasonable time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:47:05.598711Z",
     "start_time": "2022-02-07T10:47:05.595234Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "from itertools import permutations\n",
    "alltours = permutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First the most simplest function calculates absolute distance between two cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:47:07.151970Z",
     "start_time": "2022-02-07T10:47:07.148557Z"
    }
   },
   "outputs": [],
   "source": [
    "def distance_points(first, second):\n",
    "    return abs(first-second)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The other function calculates the tour distance accross all cities, using the previous function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:47:08.512858Z",
     "start_time": "2022-02-07T10:47:08.508989Z"
    }
   },
   "outputs": [],
   "source": [
    "def distance_tour(aTour):\n",
    "    return sum(distance_points(aTour[i-1], aTour[i])\n",
    "              for i in range(len(aTour)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple function generates the desired number of cities by random generation of geografic width and height. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:47:11.822321Z",
     "start_time": "2022-02-07T10:47:11.818036Z"
    }
   },
   "outputs": [],
   "source": [
    "aCity=complex\n",
    "def generate_cities(number_of_cities):\n",
    "    seed=111;width=500;height=300 \n",
    "    random.seed((number_of_cities, seed))\n",
    "    return frozenset(aCity(random.randint(1, width), random.randint(1, height)) for c in range(number_of_cities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:47:13.162359Z",
     "start_time": "2022-02-07T10:47:13.156366Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frozenset({(12+295j),\n",
       "           (155+96j),\n",
       "           (175+62j),\n",
       "           (187+262j),\n",
       "           (23+142j),\n",
       "           (29+215j),\n",
       "           (299+92j),\n",
       "           (442+24j),\n",
       "           (444+157j),\n",
       "           (467+141j)})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "created_cities = generate_cities(10)\n",
    "created_cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:47:15.573149Z",
     "start_time": "2022-02-07T10:47:15.564752Z"
    }
   },
   "outputs": [],
   "source": [
    "created_cities_a_lot =generate_cities(2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next functions finds a minimum of all tours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:15:09.711513Z",
     "start_time": "2022-02-07T11:15:09.706978Z"
    }
   },
   "outputs": [],
   "source": [
    "def shortest_tour(tours):\n",
    "    return min(tours, key=distance_tour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T10:50:23.994704Z",
     "start_time": "2022-02-07T10:50:23.981109Z"
    }
   },
   "source": [
    "The brute force function finds the shortest tour accross all generated tours accross all cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:15:11.332004Z",
     "start_time": "2022-02-07T11:15:11.328779Z"
    }
   },
   "outputs": [],
   "source": [
    "def brute_force(gradovi):\n",
    "    return shortest_tour(alltours(gradovi))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next function illustrates the starting point and all cities as spots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:15:12.822375Z",
     "start_time": "2022-02-07T11:15:12.767447Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def visualize_tour(tour, style='bo-'):\n",
    "    if len(tour)>1000:\n",
    "        plt.figure(figsize=(15,10))\n",
    "    start=tour[0:1]\n",
    "    visualize_segment(tour + start, style)\n",
    "    visualize_segment(start, 'rD')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function actually makes a line between two cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:15:14.628209Z",
     "start_time": "2022-02-07T11:15:14.623983Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def visualize_segment(segment, style='bo-'):\n",
    "    plt.plot([X(c) for c in segment], [Y(c) for c in segment], style, clip_on=False)\n",
    "    plt.axis('scaled')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:15:16.010865Z",
     "start_time": "2022-02-07T11:15:16.007558Z"
    }
   },
   "outputs": [],
   "source": [
    "def X(city): 'X axis'; return city.real\n",
    "def Y(city): 'Y axis'; return city.imag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next function integrates all previous ones, allowing the change of the algorithm for finding the shortest route and calculating time of execution and distance length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:31:53.725821Z",
     "start_time": "2022-02-07T11:31:53.720343Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "from collections import Counter\n",
    "def salesman(algorithm, cities):\n",
    "    t0=time.process_time() \n",
    "    tour = algorithm(cities)\n",
    "    t1=time.process_time() \n",
    "    assert Counter(tour)==Counter(cities)\n",
    "    visualize_tour(tour)\n",
    "    print('{}: {} cities = tour length {:.0f} (in {:.3f} sec)'.format(name(algorithm), len(tour), distance_tour(tour),t1-t0))\n",
    "\n",
    "def name(algoritam): return algoritam.__name__.replace('_tsp', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:32:05.233978Z",
     "start_time": "2022-02-07T11:31:55.822157Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brute_force: 10 cities = tour length 1224 (in 9.258 sec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salesman(brute_force, created_cities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define the greedy algorithm for selection of the next city in the tour. The principle is simple: algorithm finds the next the closest city (with the helper function neares_neighboor) and visits it, making not shortest but we hope optimal tour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:32:41.930020Z",
     "start_time": "2022-02-07T11:32:41.924633Z"
    }
   },
   "outputs": [],
   "source": [
    "def nearest_neighboor(A, gradovi):\n",
    "    return min(gradovi, key=lambda C: distance_points(C,A))\n",
    "\n",
    "def first(collection): \n",
    "    return next(iter(collection))\n",
    "\n",
    "def greedy_algorithm(cities, start=None):\n",
    "    C=start or first(cities)\n",
    "    tour=[C]\n",
    "    unvisited = set(cities-{C})\n",
    "    while unvisited:\n",
    "        C=nearest_neighboor(C, unvisited)\n",
    "        tour.append(C)\n",
    "        unvisited.remove(C)\n",
    "    return tour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:32:43.769490Z",
     "start_time": "2022-02-07T11:32:43.656696Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "greedy_algorithm: 10 cities = tour length 1224 (in 0.000 sec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salesman(greedy_algorithm, created_cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T11:37:14.727229Z",
     "start_time": "2022-02-07T11:37:14.625797Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "greedy_algorithm: 20 cities = tour length 1480 (in 0.000 sec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salesman(greedy_algorithm, generate_cities(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T13:10:37.353703Z",
     "start_time": "2022-02-07T13:10:28.381219Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brute_force: 10 cities = tour length 1224 (in 8.811 sec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salesman(brute_force, generate_cities(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-02-07T13:41:18.873137Z",
     "start_time": "2022-02-07T13:18:15.884135Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "brute_force: 12 cities = tour length 1256 (in 1376.280 sec)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "salesman(brute_force, generate_cities(12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can play with the number of cities. With the 100 cities the time drammatically increases (I could not wait to tell you the time) as the number of possible combinations. This is begginner exercise but it can show you the beaty of computing. If it is begginner exercice, it touches the problem whether there is difference between NP and P problem, which depicts boundaries of the human mind. Maybe this trivial problem could illustrate the need for quantum computing."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}