ILS_for_the_TSP.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# First Assignment\n",
        "## MSc course in Artificial Intelligence\n",
        "\n"
      ],
      "metadata": {
        "collapsed": false,
        "id": "l0hW9aFKxS11"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'AI_USI_MA'...\n",
            "remote: Enumerating objects: 385, done.\u001b[K\n",
            "remote: Counting objects: 100% (120/120), done.\u001b[K\n",
            "remote: Compressing objects: 100% (96/96), done.\u001b[K\n",
            "remote: Total 385 (delta 44), reused 92 (delta 22), pack-reused 265\u001b[K\n",
            "Receiving objects: 100% (385/385), 13.83 MiB | 16.47 MiB/s, done.\n",
            "Resolving deltas: 100% (174/174), done.\n"
          ]
        }
      ],
      "source": [
        "# Initially, we clone the course repository\n",
        "!rm -rf AI_USI_MA\n",
        "!git clone https://github.com/UmbertoJr/AI_USI_MA.git"
      ],
      "metadata": {
        "id": "bJAy1-3axS2H",
        "outputId": "763433e0-2a80-4cd7-acb5-05d4a7cf51ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's prepare the environment by importing necessary libraries.\n",
        "import warnings\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from time import time as t\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "################################################\n",
        "# Depending on your execution environment, the way you import the required module will differ.\n",
        "\n",
        "# If you are running this script locally on your machine, use the following import:\n",
        "# from IO_manager.io_tsp import TSP_Instance_Creator\n",
        "# from solvers.local_search import TwoOpt\n",
        "# from solvers.TSP_solver import Solver_TSP\n",
        "# from solvers.constructive_algorithms import Random_Initializer, Nearest_Neighbor\n",
        "# from solvers.constructive_algorithms import compute_length\n",
        "# from solvers.local_search import TwoOpt\n",
        "# from solvers.two_opt_with_candidate import twoOpt_with_cl\n",
        "\n",
        "# However, if you are executing this notebook within Google Colab and the libraries are stored\n",
        "# in a folder named 'AI_USI_MA', you should use the import statement below:\n",
        "from AI_USI_MA.IO_manager.io_tsp import TSP_Instance_Creator\n",
        "from AI_USI_MA.solvers.local_search import TwoOpt\n",
        "from AI_USI_MA.solvers.TSP_solver import Solver_TSP\n",
        "from AI_USI_MA.solvers.constructive_algorithms import Random_Initializer, Nearest_Neighbor\n",
        "from AI_USI_MA.solvers.constructive_algorithms import compute_length\n",
        "from AI_USI_MA.solvers.local_search import TwoOpt\n",
        "from AI_USI_MA.solvers.two_opt_with_candidate import twoOpt_with_cl\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "kIZcgC927Y0J"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## Iterated Local Search (ILS) for the Traveling Salesman Problem (TSP)\n",
        "\n",
        "The Iterated Local Search (ILS) is a metaheuristic that seeks to improve the quality of solutions by repeatedly applying a local search and introducing slight modifications (perturbations) to escape from local optima. This approach proves beneficial, especially for combinatorial problems like the Traveling Salesman Problem (TSP).\n",
        "\n",
        "In this assignment, we will be focusing on solving the TSP using ILS for three benchmark instances:\n",
        "\n",
        "1. **d198**: A standard instance known as 'd198.tsp' in TSP literature.\n",
        "2. **pr439**: Another well-studied instance that will test the efficiency and efficacy of our ILS approach.\n",
        "3. **u1060**: A relatively larger instance, serving as a comprehensive test for the scalability of our method.\n",
        "\n",
        "Let's begin by loading and inspecting the 'd198.tsp' instance.\n"
      ],
      "metadata": {
        "collapsed": false,
        "id": "-FkOeOqgxS2J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "name: d198\n",
            "nPoints: 198\n",
            "best_sol: 15780.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ic = TSP_Instance_Creator(\"standard\", 'd198.tsp')\n",
        "ic.print_info()\n",
        "ic.plot_data()"
      ],
      "metadata": {
        "id": "hsZ_7dVgxS2K",
        "outputId": "6cb8aa1f-251b-43b1-c2a9-f0c84893a525",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "## 2-opt Algorithm for TSP\n",
        "\n",
        "The 2-opt algorithm is a renowned local search method, predominantly utilized for refining solutions in the Traveling Salesman Problem (TSP). At its core, the algorithm takes a route and systematically swaps pairs of cities to ascertain if the change results in a shorter tour.\n",
        "\n",
        "In this notebook, we furnish a black-box version of the 2-opt. For those embarking on its usage for the first time, we suggest revisiting our [\"Introduction to the TSP\"](https://colab.research.google.com/github/UmbertoJr/AI_USI_MA/blob/master/introduction_to_the_TSP.ipynb#scrollTo=4aGR4Nus83wa). It's paramount that any meta-heuristic, including 2-opt, complies with the specific input and output conventions laid out in the primary notebook.\n",
        "\n",
        "For a tangible example, glance over the **naive_heuristic**. Within, the `TwoOpt.local_search` method is invoked to iteratively enhance the TSP solution.\n",
        "\n",
        "Two instrumental aspects to note in our design are:\n",
        "1. The `yield` statement: Serving as a generator, it permits the algorithm to produce solutions in phases, thus allowing exploration of a plethora of potential solutions without exhaustive execution.\n",
        "2. The `ending_cond` (Ending Condition): Crafted to ensure the TSP Solver either ceases after 3 minutes or concludes when the local search achieves an optimum state via the heuristic.\n",
        "\n",
        "---\n"
      ],
      "metadata": {
        "collapsed": false,
        "id": "DPsCARBixS2M"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "best gap with NN 20.25% on 1 trial,\n",
            "with a total time of 0.007 seconds\n",
            "\n",
            "best gap with random + 2opt 4.21% on 5 trials,\n",
            "with a total time of 17.35 seconds\n"
          ]
        }
      ],
      "source": [
        "def naive_heuristic(solution, cost_sol, dist_matrix):\n",
        "  for data in TwoOpt.local_search(solution, cost_sol, dist_matrix):\n",
        "    new_solution, new_cost, number_calls_ls, ending_cond = data\n",
        "    yield  new_solution, new_cost, number_calls_ls, ending_cond\n",
        "\n",
        "number_of_trials = 5\n",
        "found_lens = []\n",
        "start = t()\n",
        "solver = Solver_TSP(('random', Random_Initializer.random_method))\n",
        "solver.bind(('naive heuristic', naive_heuristic ))\n",
        "\n",
        "for _ in range(number_of_trials):\n",
        "  solver(ic)\n",
        "  found_lens.append(solver.found_length)\n",
        "end = t()\n",
        "\n",
        "solver = Solver_TSP(('nearest neighbor', Nearest_Neighbor.nn))\n",
        "solver(ic)\n",
        "\n",
        "plt.plot(found_lens, \"*\")\n",
        "plt.plot(np.ones(number_of_trials)*ic.best_sol, \"r-\")\n",
        "plt.plot(np.ones(number_of_trials)*solver.found_length, \"b-\")\n",
        "plt.show()\n",
        "print(f\"best gap with NN {solver.gap}% on 1 trial,\\nwith a total time of {np.round(solver.time_to_solve, 5)} seconds\")\n",
        "print()\n",
        "print(f\"best gap with random + 2opt {np.round(100*(min(found_lens) - ic.best_sol)/ic.best_sol, 2)}% on {number_of_trials} trials,\\nwith a total time of {np.round(end - start, 2)} seconds\")"
      ],
      "metadata": {
        "id": "G6TFytxRxS2O",
        "outputId": "71db0abb-f2c6-4c70-f18a-f2817079263a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Double Bridge\n",
        "\n",
        "Here you need to implement your own Double Bridge permutation!"
      ],
      "metadata": {
        "collapsed": false,
        "id": "yVjsvkI6xS2P"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "outputs": [],
      "source": [
        "class DoubleBridge:\n",
        "  @staticmethod\n",
        "  def difference_cost(solution, a, b, c, d, matrix):\n",
        "    return\n",
        "\n",
        "  @staticmethod\n",
        "  def perturb_solution(solution, actual_cost, matrix):\n",
        "    return solution, actual_cost"
      ],
      "metadata": {
        "id": "mu4XKossxS2Q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0  1  2  3  4  5  6  7  8  9 10 11] 6.326443922062321\n",
            "[ 0  1  2  3  4  5  6  7  8  9 10 11] 6.326443922062321\n"
          ]
        }
      ],
      "source": [
        "# example perturbation\n",
        "case_solution = np.arange(12)\n",
        "matrix = np.random.rand(12, 12)\n",
        "cost = compute_length(case_solution, matrix)\n",
        "print(case_solution, cost)\n",
        "\n",
        "new_sol, new_length = DoubleBridge.perturb_solution(case_solution, cost, matrix)\n",
        "print(new_sol, new_length)"
      ],
      "metadata": {
        "id": "C74Oh4pKxS2S",
        "outputId": "d59a56de-2050-4e8a-b93a-f5682d969bf5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Iterated Local Search\n",
        "\n",
        "\n",
        "Here you need to implement the three variants of the ILS we saw earlier in class.\n",
        "\n",
        "To boost the time performances please use the cython class **two_opt_with_candidate**."
      ],
      "metadata": {
        "collapsed": false,
        "id": "GIzZsoPhxS2T"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "outputs": [],
      "source": [
        "def my_local_search(solution, cost_sol, dist_matrix):\n",
        "  new_solution, new_cost = solution, cost_sol\n",
        "  for data in TwoOpt.local_search(solution, cost_sol, dist_matrix):\n",
        "    new_solution, new_cost, number_calls_ls, ending_cond = data\n",
        "    if ending_cond == True:\n",
        "      break\n",
        "  return new_solution, new_cost\n",
        "\n",
        "def generate_cl(dist_matrix):\n",
        "  n = dist_matrix.shape[0]\n",
        "  cl = np.zeros((n, 20))\n",
        "  for i in range(n):\n",
        "    cl[i] = np.argsort(dist_matrix[i, :])[1: 21]\n",
        "  return cl\n",
        "\n",
        "class ILS_Better:\n",
        "  @staticmethod\n",
        "  def solve(solution, actual_cost, matrix):\n",
        "    global cost_sol_better\n",
        "    new_sol, new_cost = my_local_search(solution, actual_cost, matrix)\n",
        "    \"\"\"\n",
        "    #  in case you want to use twoOpt_with_cl\n",
        "    cl = generate_cl(matrix) # remember you don't need to compute the cl each time\n",
        "    new_sol, new_cost = twoOpt_with_cl(solution, actual_cost, matrix, cl)\n",
        "    \"\"\"\n",
        "    best_sol,best_cost = new_sol, new_cost\n",
        "    cost_sol_better.append(new_cost)\n",
        "    ls_calls = 1\n",
        "    while True:\n",
        "      # implement perturbation\n",
        "\n",
        "      # implement acceptance criterion\n",
        "\n",
        "      yield best_sol, best_cost, ls_calls, False\n",
        "\n",
        "class ILS_RW:\n",
        "  @staticmethod\n",
        "  def solve(solution, actual_cost, matrix):\n",
        "    global cost_sol_RW\n",
        "    new_sol, new_cost = my_local_search(solution, actual_cost, matrix)\n",
        "    best_sol,best_cost = new_sol, new_cost\n",
        "    cost_sol_RW.append(new_cost)\n",
        "    ls_calls = 1\n",
        "    while True:\n",
        "      # implement perturbation\n",
        "\n",
        "      # implement acceptance criterion\n",
        "\n",
        "      yield best_sol, best_cost, ls_calls, False\n",
        "\n",
        "class ILS_LSMC:\n",
        "  @staticmethod\n",
        "  def acceptance_criterion(best_cost, new_cost, temperature):\n",
        "    return\n",
        "\n",
        "  @staticmethod\n",
        "  def solve(solution, actual_cost, matrix):\n",
        "    global cost_sol_LSMC\n",
        "    new_sol, new_cost = my_local_search(solution, actual_cost, matrix)\n",
        "    best_sol,best_cost = new_sol, new_cost\n",
        "    cost_sol_LSMC.append(new_cost)\n",
        "    ls_calls = 1\n",
        "    while True:\n",
        "      # implement perturbation\n",
        "\n",
        "      # implement acceptance criterion\n",
        "\n",
        "      yield best_sol, best_cost, ls_calls, False"
      ],
      "metadata": {
        "id": "mEmOP4hNxS2T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Example of Table\n",
        "\n",
        "Take care that you need to change the code in order to satisfy the requirements for the task!"
      ],
      "metadata": {
        "collapsed": false,
        "id": "peExJgf-xS2W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                tour length  \\\n",
              "problem  optimal length method                                            seed                \n",
              "d198.tsp 15780.0        initialized with random, improved with ILS-better 0         16646.0   \n",
              "                        initialized with random, improved with ILS-RW     0         16396.0   \n",
              "                        initialized with random, improved with ILS-LSMC   0         16389.0   \n",
              "                        initialized with random, improved with ILS-better 123       16420.0   \n",
              "                        initialized with random, improved with ILS-RW     123       16420.0   \n",
              "                        initialized with random, improved with ILS-LSMC   123       16420.0   \n",
              "                        initialized with random, improved with ILS-better 333       16617.0   \n",
              "                        initialized with random, improved with ILS-RW     333       16617.0   \n",
              "                        initialized with random, improved with ILS-LSMC   333       16617.0   \n",
              "\n",
              "                                                                                 gap  \\\n",
              "problem  optimal length method                                            seed         \n",
              "d198.tsp 15780.0        initialized with random, improved with ILS-better 0     5.49   \n",
              "                        initialized with random, improved with ILS-RW     0     3.90   \n",
              "                        initialized with random, improved with ILS-LSMC   0     3.86   \n",
              "                        initialized with random, improved with ILS-better 123   4.06   \n",
              "                        initialized with random, improved with ILS-RW     123   4.06   \n",
              "                        initialized with random, improved with ILS-LSMC   123   4.06   \n",
              "                        initialized with random, improved with ILS-better 333   5.30   \n",
              "                        initialized with random, improved with ILS-RW     333   5.30   \n",
              "                        initialized with random, improved with ILS-LSMC   333   5.30   \n",
              "\n",
              "                                                                                time to solve  \\\n",
              "problem  optimal length method                                            seed                  \n",
              "d198.tsp 15780.0        initialized with random, improved with ILS-better 0             3.349   \n",
              "                        initialized with random, improved with ILS-RW     0             1.549   \n",
              "                        initialized with random, improved with ILS-LSMC   0             2.452   \n",
              "                        initialized with random, improved with ILS-better 123           1.471   \n",
              "                        initialized with random, improved with ILS-RW     123           1.508   \n",
              "                        initialized with random, improved with ILS-LSMC   123           1.462   \n",
              "                        initialized with random, improved with ILS-better 333           1.537   \n",
              "                        initialized with random, improved with ILS-RW     333           2.380   \n",
              "                        initialized with random, improved with ILS-LSMC   333           1.275   \n",
              "\n",
              "                                                                                calls Local Search  \n",
              "problem  optimal length method                                            seed                      \n",
              "d198.tsp 15780.0        initialized with random, improved with ILS-better 0                      1  \n",
              "                        initialized with random, improved with ILS-RW     0                      1  \n",
              "                        initialized with random, improved with ILS-LSMC   0                      1  \n",
              "                        initialized with random, improved with ILS-better 123                    1  \n",
              "                        initialized with random, improved with ILS-RW     123                    1  \n",
              "                        initialized with random, improved with ILS-LSMC   123                    1  \n",
              "                        initialized with random, improved with ILS-better 333                    1  \n",
              "                        initialized with random, improved with ILS-RW     333                    1  \n",
              "                        initialized with random, improved with ILS-LSMC   333                    1  "
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "seeds= [0, 123, 333]\n",
        "time_to_solve = 1 # in seconds\n",
        "\n",
        "cost_sol_better = []\n",
        "cost_sol_RW = []\n",
        "cost_sol_LSMC = []\n",
        "\n",
        "names_instances = [\"d198.tsp\"] #, \"pr439.tsp\", \"u1060.tsp\"\n",
        "\n",
        "initializers = [\"random\"]\n",
        "init_functions = [Random_Initializer.random_method]\n",
        "\n",
        "improvements = [\"ILS-better\",\"ILS-RW\",\"ILS-LSMC\"]\n",
        "improve_functions = [ILS_Better.solve, ILS_RW.solve, ILS_LSMC.solve]\n",
        "\n",
        "results = []\n",
        "index = []\n",
        "samples = {name: {improve: {seed: [] for seed in seeds} for improve in improvements} for name in names_instances}\n",
        "\n",
        "for s_ in seeds:\n",
        "  collectors = [cost_sol_better, cost_sol_RW, cost_sol_LSMC]\n",
        "  for i, init in enumerate(initializers):\n",
        "    for j, improve in enumerate(improvements):\n",
        "      solver = Solver_TSP((init, init_functions[i]), seed_=s_, stop_run_after=time_to_solve)\n",
        "      solver.bind((improve, improve_functions[j]))\n",
        "      for name in names_instances:\n",
        "        instance = TSP_Instance_Creator(\"standard\", name)\n",
        "        solver(instance)\n",
        "        index.append((name, instance.best_sol, solver.name_method, s_))\n",
        "        results.append([solver.found_length, solver.gap, solver.time_to_solve, solver.ls_calls])\n",
        "        samples[name][improve][s_] = np.round(np.abs(np.array(collectors[j]) - instance.best_sol)/instance.best_sol * 100, 2)\n",
        "        if j == 0:\n",
        "          cost_sol_better = []\n",
        "        elif j == 1:\n",
        "          cost_sol_RW = []\n",
        "        else:\n",
        "          cost_sol_LSMC = []\n",
        "\n",
        "index = pd.MultiIndex.from_tuples(index, names=['problem','optimal length', 'method', 'seed'])\n",
        "\n",
        "pd.DataFrame(results, index=index, columns=[\"tour length\", \"gap\", \"time to solve\", \"calls Local Search\"])"
      ],
      "metadata": {
        "id": "ZRWOiI4sxS2Y",
        "outputId": "75864aaa-ba6d-4ff2-b056-cc5c35dabf51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "with warnings.catch_warnings():\n",
        "  warnings.simplefilter(\"ignore\")\n",
        "  for instance in names_instances:\n",
        "    fig = plt.figure(figsize=(10,6))\n",
        "    for j, improve in enumerate(improvements):\n",
        "      data = pd.DataFrame.from_dict(samples[instance][improve], orient='index').transpose()\n",
        "      data_avg = data.mean(axis = 0, skipna = True)\n",
        "      sns.distplot(data_avg, rug=True, hist=False)\n",
        "\n",
        "    fig.legend(labels=improvements)\n",
        "    plt.title(instance)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "eli-hbAJxS2Z",
        "outputId": "da010e6b-cda1-4fa2-f34d-76702867856a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        }
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QrHzDiTH-YoH"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}