selection-problem.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "selection-problem.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOWvi0SVg-2R"
      },
      "source": [
        "Selection Problem"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnpqYxrW9GY1"
      },
      "source": [
        "* Set use_input_txt = 1 if you want to use input from file, or set use_input_txt = 0 to auto generate input data.\n",
        "* Upload input.txt to current directory at the \"Files\" bar on the left\n",
        "* Click \"Runtime\" and \"Run all\" and see the output file QS.txt and PS.txt in current directory\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYoojZe9hZAl"
      },
      "source": [
        "use_input_txt = 0#@param\n",
        "min_input_size = 10000#@param\n",
        "max_input_size = 30000#@param\n",
        "step = 1000#@param\n",
        "sample_size = 3#@param"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G3k1sW3hIBt"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import timeit"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHa4XLARWfny"
      },
      "source": [
        "def partition(mlist, low, high):\n",
        "  i = low\n",
        "  pivot = mlist[high]\n",
        "  for j in range(low, high):\n",
        "    if mlist[j] <= pivot:\n",
        "      mlist[i], mlist[j] = mlist[j], mlist[i]\n",
        "      i = i + 1\n",
        "  mlist[i], mlist[high] = mlist[high], mlist[i]\n",
        "  return i\n",
        "\n",
        "def quick_sort(mlist, low, high):\n",
        "  if len(mlist) == 1:\n",
        "    return mlist\n",
        "  if low < high:\n",
        "    index = partition(mlist, low, high)\n",
        "    quick_sort(mlist, low, index - 1)\n",
        "    quick_sort(mlist, index + 1, high)\n",
        "\n",
        "def selection_by_sorting(mlist, k):\n",
        "  # mlist.sort()\n",
        "  quick_sort(mlist, 0, len(mlist) - 1)\n",
        "  return mlist[k-1]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "skZhQRgQghQs"
      },
      "source": [
        "def selection_by_prune_and_search(mlist, k):\n",
        "  # print(\"k:\", k)\n",
        "  # print(\"mlist:\", mlist)\n",
        "  if len(mlist) < 5:\n",
        "    return selection_by_sorting(mlist, k)\n",
        "  # print(5 - len(mlist) % 5)\n",
        "  mlist = np.append(mlist, [np.inf] * (5 - len(mlist) % 5))\n",
        "  # print(mlist)\n",
        "  matrix = np.reshape(mlist, (-1, 5))\n",
        "  # print(matrix)\n",
        "  subset_median = []\n",
        "  for subset in matrix:\n",
        "    # print(subset)\n",
        "    subset_median.append(selection_by_sorting(subset, 3))\n",
        "  # print(\"subset_median:\", subset_median)\n",
        "  # subset_median.sort()\n",
        "  # subset_median_median = subset_median[(len(subset_median) - 1) // 2]\n",
        "  subset_median_median = selection_by_prune_and_search(subset_median, (len(subset_median) + 1) // 2)\n",
        "  # print(\"subset_median_median:\", subset_median_median)\n",
        "  S1 = mlist[mlist < subset_median_median]\n",
        "  S2 = mlist[mlist == subset_median_median]\n",
        "  S3 = mlist[mlist > subset_median_median]\n",
        "  # print(S1, S2, S3)\n",
        "  if len(S1) >= k:\n",
        "    return selection_by_prune_and_search(S1, k)\n",
        "  elif len(S1) + len(S2) >= k:\n",
        "    return subset_median_median\n",
        "  else:\n",
        "    return selection_by_prune_and_search(S3, k - len(S1) - len(S2))"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5SntVTpbcQm",
        "outputId": "5fd9d803-c7d5-4023-a546-ffb7055763dc"
      },
      "source": [
        "def print_answer(answer, time, file_name):\n",
        "  print(f\"Answer: {answer}\")\n",
        "  print(f\"Time: {time} s\")\n",
        "  with open(file_name, \"w\") as text_file:\n",
        "    print(f\"{answer}\", file=text_file)\n",
        "    print(f\"{time:.6f}\", file=text_file)\n",
        "\n",
        "\n",
        "def gen_input(n):\n",
        "  mlist = np.random.randint(1, 20001, size=n)#.astype(np.float32)\n",
        "  # some test case\n",
        "  # cost_matrix[0,:] = np.inf\n",
        "  # cost_matrix[0,1] = -1\n",
        "  # cost_matrix = np.ones((n, n))\n",
        "  k = np.random.randint(1, n + 1)\n",
        "  return mlist, k\n",
        "\n",
        "selection_by_sorting_time = []\n",
        "selection_by_prune_and_search_time = []\n",
        "np.set_printoptions(formatter={'float': '{: 0.0f}'.format})\n",
        "\n",
        "if use_input_txt:\n",
        "  # input = np.loadtxt('input.txt')\n",
        "  # print(input)\n",
        "  input_k = np.loadtxt('input.txt', dtype=int, max_rows=1)[1]\n",
        "  input_list = np.loadtxt('input.txt', dtype=int, skiprows=1)\n",
        "  # print(input)\n",
        "  min_input_size = input_list.shape[0]\n",
        "  max_input_size = min_input_size\n",
        "\n",
        "for n in range(min_input_size, max_input_size + 1, step):\n",
        "  print(\"_____________________________________\")\n",
        "  print(\"n:\", n)\n",
        "  if use_input_txt:\n",
        "    mlist, k = input_list, input_k\n",
        "  else:\n",
        "    mlist, k = gen_input(n)\n",
        "  print(f\"input: {mlist}\")\n",
        "  print(f\"k: {k}\")\n",
        "  print(\"\")\n",
        "  print(\"selection by sorting\")\n",
        "  start = timeit.default_timer()\n",
        "  selection_by_sorting_answer = selection_by_sorting(mlist, k)\n",
        "  stop = timeit.default_timer()\n",
        "  if use_input_txt:\n",
        "    time = stop - start\n",
        "  else:\n",
        "    run_time_samples = timeit.repeat(stmt='selection_by_sorting(t_mlist, t_k)', setup='from __main__ import gen_input, selection_by_sorting, n; t_mlist, t_k = gen_input(n)', repeat=sample_size, number=1)\n",
        "    time = np.average(run_time_samples)\n",
        "    print(\"run_time_samples: \", run_time_samples)\n",
        "  print_answer(selection_by_sorting_answer, time, \"QS.txt\")\n",
        "\n",
        "  selection_by_sorting_time.append(time)\n",
        "  print(\"\")\n",
        "  print(\"selection by prune and search\")\n",
        "  start = timeit.default_timer()\n",
        "  selection_by_prune_and_search_answer = selection_by_prune_and_search(mlist, k).astype(int)\n",
        "  stop = timeit.default_timer()\n",
        "  if use_input_txt:\n",
        "    time = stop - start\n",
        "  else:\n",
        "    run_time_samples = timeit.repeat(stmt='selection_by_prune_and_search(t_mlist, t_k)', setup='from __main__ import gen_input, selection_by_prune_and_search, n; t_mlist, t_k = gen_input(n)', repeat=sample_size, number=1)\n",
        "    time = np.average(run_time_samples)\n",
        "    print(\"run_time_samples: \", run_time_samples)\n",
        "  print_answer(selection_by_prune_and_search_answer, time, \"PS.txt\")\n",
        "\n",
        "  selection_by_prune_and_search_time.append(time)\n",
        "\n",
        "  assert selection_by_sorting_answer == selection_by_prune_and_search_answer, \"Answer is different!\""
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "_____________________________________\n",
            "n: 10000\n",
            "input: [ 1173 10167 10359 ...  9740 16438 11206]\n",
            "k: 5075\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.1263820270000906, 0.13055782900028134, 0.11892000299985739]\n",
            "Answer: 9916\n",
            "Time: 0.12528661966674312 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.13655947799998103, 0.12540158199999496, 0.11221503100023256]\n",
            "Answer: 9916\n",
            "Time: 0.12472536366673619 s\n",
            "_____________________________________\n",
            "n: 11000\n",
            "input: [13205  6931  7742 ...  4120 16130 13537]\n",
            "k: 4956\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.12798654900007023, 0.14104582299978574, 0.18218635599987465]\n",
            "Answer: 9158\n",
            "Time: 0.15040624266657687 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.07522446199982369, 0.05763340999965294, 0.06830572799981383]\n",
            "Answer: 9158\n",
            "Time: 0.06705453333309681 s\n",
            "_____________________________________\n",
            "n: 12000\n",
            "input: [16228 10788   955 ... 18116 12230  9650]\n",
            "k: 6248\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.09015442400004758, 0.08306240500041895, 0.08421831799978463]\n",
            "Answer: 10322\n",
            "Time: 0.08581171566675039 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.06554650999987643, 0.07836970099970131, 0.06875786499995229]\n",
            "Answer: 10322\n",
            "Time: 0.07089135866651002 s\n",
            "_____________________________________\n",
            "n: 13000\n",
            "input: [ 3174  3333  4746 ...   485 18762  2791]\n",
            "k: 6873\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.10347058900015327, 0.09487321400001747, 0.08810071200014136]\n",
            "Answer: 10599\n",
            "Time: 0.09548150500010404 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.07243213299989293, 0.07470759400030147, 0.1654837440000847]\n",
            "Answer: 10599\n",
            "Time: 0.1042078236667597 s\n",
            "_____________________________________\n",
            "n: 14000\n",
            "input: [17030  8330  8479 ...  4247  4057  8680]\n",
            "k: 7396\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.23264339200022732, 0.239709447000223, 0.2353009500002372]\n",
            "Answer: 10595\n",
            "Time: 0.2358845963335625 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.18458745400039334, 0.1398043150002195, 0.18112041299991688]\n",
            "Answer: 10595\n",
            "Time: 0.16850406066684323 s\n",
            "_____________________________________\n",
            "n: 15000\n",
            "input: [ 5594 13746 14318 ...  3386 12698  1280]\n",
            "k: 9461\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.3456484600001204, 0.27165662600009455, 0.35202853100008724]\n",
            "Answer: 12558\n",
            "Time: 0.3231112056667674 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.1597035820000201, 0.1936173310000413, 0.1451424990000305]\n",
            "Answer: 12558\n",
            "Time: 0.1661544706666973 s\n",
            "_____________________________________\n",
            "n: 16000\n",
            "input: [13462 11394 19407 ...  2958 11134 11026]\n",
            "k: 4234\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.24688657500018962, 0.329691319000176, 0.23252661900005478]\n",
            "Answer: 5284\n",
            "Time: 0.26970150433347345 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.1898498550003751, 0.23643328099979044, 0.21526419100018757]\n",
            "Answer: 5284\n",
            "Time: 0.2138491090001177 s\n",
            "_____________________________________\n",
            "n: 17000\n",
            "input: [ 9895  3545 10561 ... 11862 19313  7019]\n",
            "k: 11204\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.37411385599989444, 0.29770707599982416, 0.335845075999714]\n",
            "Answer: 13096\n",
            "Time: 0.3358886693331442 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.18621477000033337, 0.2727815470002497, 0.2802999639998234]\n",
            "Answer: 13096\n",
            "Time: 0.24643209366680216 s\n",
            "_____________________________________\n",
            "n: 18000\n",
            "input: [ 1686 16122   529 ...  1643 12008  8672]\n",
            "k: 7139\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [1.0756459449999056, 0.9571215430000848, 0.8898784699999851]\n",
            "Answer: 7818\n",
            "Time: 0.9742153193333252 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.3074264749998292, 0.2788642890000119, 0.2205224989997987]\n",
            "Answer: 7818\n",
            "Time: 0.26893775433321326 s\n",
            "_____________________________________\n",
            "n: 19000\n",
            "input: [13556 12634 15415 ...  3119 17512 13082]\n",
            "k: 11340\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.7200117390002561, 0.6008929139998145, 0.6935520930001076]\n",
            "Answer: 11991\n",
            "Time: 0.6714855820000594 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.5470656999996208, 0.5152027069998439, 0.21496619700019437]\n",
            "Answer: 11991\n",
            "Time: 0.42574486799988637 s\n",
            "_____________________________________\n",
            "n: 20000\n",
            "input: [   62  7446  5293 ...  4231 19115 16495]\n",
            "k: 9469\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.4904445189999933, 0.3779427209997266, 0.4524892210001781]\n",
            "Answer: 9446\n",
            "Time: 0.44029215366663266 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.28364595599987297, 0.3547027159997924, 0.2606018370001948]\n",
            "Answer: 9446\n",
            "Time: 0.29965016966662006 s\n",
            "_____________________________________\n",
            "n: 21000\n",
            "input: [19056 15761 16532 ...  3908 16219  6090]\n",
            "k: 20673\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.9287682499998482, 0.8324210999999195, 0.9741732560000855]\n",
            "Answer: 19715\n",
            "Time: 0.9117875353332844 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.35295610100001795, 0.2364701649999006, 0.11111230200003774]\n",
            "Answer: 19715\n",
            "Time: 0.23351285599998542 s\n",
            "_____________________________________\n",
            "n: 22000\n",
            "input: [ 8661 18003  7337 ... 14342  6320 16224]\n",
            "k: 7304\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.16203022399986367, 0.14431096799989973, 0.15327610200029085]\n",
            "Answer: 6626\n",
            "Time: 0.15320576466668476 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.14897156100005304, 0.11140202300020974, 0.1203077990003294]\n",
            "Answer: 6626\n",
            "Time: 0.12689379433353074 s\n",
            "_____________________________________\n",
            "n: 23000\n",
            "input: [ 3793  7558  3395 ... 17622  1762   985]\n",
            "k: 2459\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.16907976499987853, 0.18612518400004774, 0.1758956609996858]\n",
            "Answer: 2160\n",
            "Time: 0.17703353666653734 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.12403268499974729, 0.11884264099990105, 0.12815310799987856]\n",
            "Answer: 2160\n",
            "Time: 0.12367614466650896 s\n",
            "_____________________________________\n",
            "n: 24000\n",
            "input: [  652 10882  6534 ... 10616  5068 15930]\n",
            "k: 7843\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.1732980500000849, 0.19548834199986231, 0.17191386800004693]\n",
            "Answer: 6421\n",
            "Time: 0.18023341999999806 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.12585669000009148, 0.12112377000039487, 0.1339512439999453]\n",
            "Answer: 6421\n",
            "Time: 0.12697723466681055 s\n",
            "_____________________________________\n",
            "n: 25000\n",
            "input: [ 8563   821  1590 ...  1164  7954 16213]\n",
            "k: 9275\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.1968914180001775, 0.19795216200009236, 0.1725990160002766]\n",
            "Answer: 7390\n",
            "Time: 0.18914753200018217 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.1384970419999263, 0.13257694100002482, 0.12428546699993603]\n",
            "Answer: 7390\n",
            "Time: 0.1317864833332957 s\n",
            "_____________________________________\n",
            "n: 26000\n",
            "input: [12106  6613  4275 ... 16351  8165  7311]\n",
            "k: 22493\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.19803368500015495, 0.19866779099993437, 0.21282382099980168]\n",
            "Answer: 17230\n",
            "Time: 0.20317509899996367 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.1402345910000804, 0.1355769430001601, 0.12404014300000199]\n",
            "Answer: 17230\n",
            "Time: 0.13328389233341417 s\n",
            "_____________________________________\n",
            "n: 27000\n",
            "input: [ 7189  5778  8383 ... 15628  8669 12731]\n",
            "k: 25617\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.20594949300038934, 0.21611540499998227, 0.18806965999965541]\n",
            "Answer: 18994\n",
            "Time: 0.203378186000009 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.14206564999994953, 0.13691030700010742, 0.1527306220000355]\n",
            "Answer: 18994\n",
            "Time: 0.14390219300003082 s\n",
            "_____________________________________\n",
            "n: 28000\n",
            "input: [12491  6214  9080 ... 12594 10096 18990]\n",
            "k: 25018\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.20213491300000896, 0.2244974129998809, 0.2549791810001807]\n",
            "Answer: 17851\n",
            "Time: 0.2272038356666902 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.3123154219997559, 0.3010426179998831, 0.29959809599995424]\n",
            "Answer: 17851\n",
            "Time: 0.30431871199986443 s\n",
            "_____________________________________\n",
            "n: 29000\n",
            "input: [12715 10322 18936 ... 15435 12151  7739]\n",
            "k: 3697\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.42511628100010057, 0.45736314899977515, 0.2717588410000644]\n",
            "Answer: 2573\n",
            "Time: 0.38474609033331336 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.1613694040001974, 0.13362584700007574, 0.15893689099993935]\n",
            "Answer: 2573\n",
            "Time: 0.15131071400007082 s\n",
            "_____________________________________\n",
            "n: 30000\n",
            "input: [17639 13774  3532 ...  6490 10082  7493]\n",
            "k: 23897\n",
            "\n",
            "selection by sorting\n",
            "run_time_samples:  [0.24663505600028657, 0.25013153999998394, 0.2268622729998242]\n",
            "Answer: 15856\n",
            "Time: 0.24120962300003157 s\n",
            "\n",
            "selection by prune and search\n",
            "run_time_samples:  [0.1611884040003133, 0.16224015500029054, 0.14736534199982998]\n",
            "Answer: 15856\n",
            "Time: 0.15693130033347794 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgX7qZCQ4szk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "02fff937-cdaa-433f-b748-23d8d482a7eb"
      },
      "source": [
        "x = np.arange(min_input_size, max_input_size + 1, step)\n",
        "# bf_time = brute_force_time[:max_brute_force_input_size - min_input_size + 1]\n",
        "plt.plot(x, selection_by_sorting_time, marker='o')\n",
        "plt.plot(x, selection_by_prune_and_search_time, marker='o')\n",
        "plt.xlabel('n')\n",
        "plt.ylabel('run time(sec)')\n",
        "plt.title('run time vs n')\n",
        "plt.legend(['brute force', 'branch and bound'])\n",
        "# plt.xticks(x)\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}