AICup/Lectures/Student_lecture 3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Third Lab\n",
    "\n",
    "What we are going to do today:\n",
    "- Introduce two optimizers for local search methods\n",
    "- Use the optimizer with the methods defined before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This cell below is simply importing some useful stuff for later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from time import time as t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizers\n",
    "### 2opt\n",
    "As we saw last time, we have 12 problems and two have an optimal solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from src.utils import compute_length\n",
    "\n",
    "\n",
    "def step2opt(solution, matrix_dist, distance):\n",
    "    seq_length = len(solution) - 1\n",
    "    tsp_sequence = np.array(solution)\n",
    "    uncrosses = 0\n",
    "    # TODO\n",
    "    for i in range(1, seq_length - 1):\n",
    " \n",
    "    # END TODO\n",
    "    return tsp_sequence, distance, uncrosses\n",
    "\n",
    "\n",
    "def swap2opt(tsp_sequence, i, j):\n",
    "    # TODO\n",
    "  \n",
    "    # END TODO\n",
    "    return new_tsp_sequence\n",
    "\n",
    "\n",
    "def gain(i, j, tsp_sequence, matrix_dist):\n",
    "    old_link_len = (matrix_dist[tsp_sequence[i], tsp_sequence[i - 1]] + matrix_dist[\n",
    "        tsp_sequence[j], tsp_sequence[j + 1]])\n",
    "    changed_links_len = (matrix_dist[tsp_sequence[j], tsp_sequence[i - 1]] + matrix_dist[\n",
    "        tsp_sequence[i], tsp_sequence[j + 1]])\n",
    "    return - old_link_len + changed_links_len\n",
    "\n",
    "\n",
    "def loop2opt(solution, instance, max_num_of_uncrosses=10000):\n",
    "    matrix_dist = instance.dist_matrix\n",
    "    new_len = compute_length(solution, matrix_dist)\n",
    "    new_tsp_sequence = np.copy(np.array(solution))\n",
    "    uncross = 0\n",
    "    # TODO\n",
    "  \n",
    "    # END TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's test it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "#############################\n",
      "name: eil76\n",
      "nPoints: 76\n",
      "best_sol: 538.0\n",
      "exist optimal: True\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from src.io_tsp import ProblemInstance\n",
    "example_problem = \"../problems/eil76.tsp\"\n",
    "p_inst = ProblemInstance(example_problem)\n",
    "p_inst.print_info()\n",
    "p_inst.plot_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.TSP_solver import TSPSolver\n",
    "from src.constructive_algorithms import (\n",
    "    random_method,\n",
    "    nearest_neighbor,\n",
    "    best_nearest_neighbor,\n",
    "    multi_fragment_mf\n",
    ")\n",
    "\n",
    "available_solvers = {\n",
    "    \"random\": random_method,\n",
    "    \"nn\":nearest_neighbor,\n",
    "    \"best_nn\":best_nearest_neighbor,\n",
    "    \"multi_fragment\": multi_fragment_mf\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###  solving with ['nn'] ####\n",
      "Error the solution of nn for problem eil76 is not valid\n",
      "best solution with len 680.0 \n",
      "with a total time of 0.0 seconds\n",
      "solution found has a 26.39 % gap\n",
      "0.08463191986083984\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 7.43 % gap\n",
      "with 23 number of uncrossed edges\n"
     ]
    }
   ],
   "source": [
    "solver = TSPSolver(\"nn\", p_inst, available_solvers)\n",
    "start = t()\n",
    "solver.compute_solution(return_value=False, verbose=True)\n",
    "end = t()\n",
    "\n",
    "solver.plot_solution()\n",
    "print(f\"best solution with len {solver.found_length} \\nwith a total time of {np.round(end - start, 5)} seconds\")\n",
    "print(f\"solution found has a {solver.gap} % gap\")\n",
    "\n",
    "start = t()\n",
    "solution, new_length, uncross = loop2opt(solver.solution, p_inst)\n",
    "end = t()\n",
    "print(end -  start)\n",
    "solver.method = \"nn folowed by 2 opt\"\n",
    "\n",
    "assert solver.pass_and_check_if_solution_is_valid(solution), \"Solution non valid\"\n",
    "solver.solved = True\n",
    "solver.solution = solution\n",
    "solver.plot_solution()\n",
    "solver._gap()\n",
    "print(f\" {solver.gap} % gap\")\n",
    "print(f\"with {uncross} number of uncrossed edges\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5opt\n",
    "We will see it in the next Lecture"
   ]
  }
 ],
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