CellFormation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os \n",
    "import imp\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import importlib as imp\n",
    "import itertools\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "from utils import tools\n",
    "warnings.simplefilter('ignore')\n",
    "pd.options.display.max_columns = 100\n",
    "\n",
    "PATH_TO_BENCHMARKS = './/data//'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Задача**\n",
    "$asd$\n",
    "*Ваша четвертая лабораторная работа будет заключаться в реализации одного из двух алгоритмов: <br>\n",
    "метод имитации обжига или генетический алгоритм со встроенной эвристикой в виде локального поиска.  <br>\n",
    "Алгоритмы реализуются для задачи Cell formation problem.* <br>\n",
    "\n",
    "Реализовать можно на любом языке. <br>\n",
    "Также нужно будет сравнить время работы алгоритма, а так же показать лучшее решение алгоритма, которое удалось найти вам. <br>\n",
    "\n",
    "**Данные:**\n",
    "\n",
    "Данные для работы прикрепил архивом к письму. **Структура такая:** <br>\n",
    "\n",
    "m p (number of machines and parts) <br>\n",
    "Next m rows: <br>\n",
    "m(row number) list of parts processed by machine m separated by space <br>\n",
    "e.g: <br>\n",
    "1 9 17 19 31 33\n",
    "means machine 1 processes parts 9 17 19 31 33 <br>\n",
    "\n",
    "**Выходной формат для ответов:** <br>\n",
    "Output file: instancename.sol (e.g. 20x20.sol) <br>\n",
    "Output file format: <br>\n",
    "m1_clusterId m2_clusterId ... - machines to clusters mapping <br>\n",
    "p1_clusterId p2_clusterId ... - parts to clusters mapping <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['20x20.txt', '24x40.txt', '30x50.txt', '30x90.txt', '37x53.txt']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txtfiles = [f for f in os.listdir(PATH_TO_BENCHMARKS) if os.path.isfile(os.path.join(PATH_TO_BENCHMARKS, f))]\n",
    "txtfiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20x20.txt\n",
      "              f: {'current': 0.3053097345132743, 'neigh': 0, 'best': 0}\n",
      "temperature    : {'current': 10, 'final': 0.002}\n",
      "number of cells: {'initial': 2, 'optimal': 0} cool_rate: 0.7\n",
      "\n",
      "\n",
      "              f: {'current': 0.32903225806451614, 'neigh': 0.32903225806451614, 'best': 0.3973509933774834}\n",
      "temperature    : {'current': 0.0019158123138056616, 'final': 0.002}\n",
      "number of cells: {'initial': 6, 'optimal': 5} cool_rate: 0.7\n",
      "\n",
      "\n",
      "time: 0:00:26.113015\n",
      "24x40.txt\n",
      "              f: {'current': 0.1790744466800805, 'neigh': 0, 'best': 0}\n",
      "temperature    : {'current': 10, 'final': 0.002}\n",
      "number of cells: {'initial': 2, 'optimal': 0} cool_rate: 0.7\n",
      "\n",
      "\n",
      "              f: {'current': 0.22321428571428573, 'neigh': 0.22321428571428573, 'best': 0.26881720430107525}\n",
      "temperature    : {'current': 0.0019158123138056616, 'final': 0.002}\n",
      "number of cells: {'initial': 5, 'optimal': 4} cool_rate: 0.7\n",
      "\n",
      "\n",
      "time: 0:01:17.337328\n",
      "30x50.txt\n",
      "              f: {'current': 0.10413476263399694, 'neigh': 0, 'best': 0}\n",
      "temperature    : {'current': 10, 'final': 0.002}\n",
      "number of cells: {'initial': 2, 'optimal': 0} cool_rate: 0.7\n",
      "\n",
      "\n",
      "              f: {'current': 0.11436950146627566, 'neigh': 0.11436950146627566, 'best': 0.21350364963503649}\n",
      "temperature    : {'current': 0.0019158123138056616, 'final': 0.002}\n",
      "number of cells: {'initial': 4, 'optimal': 3} cool_rate: 0.7\n",
      "\n",
      "\n",
      "time: 0:01:17.533570\n",
      "30x90.txt\n",
      "              f: {'current': 0.12310151878497202, 'neigh': 0, 'best': 0}\n",
      "temperature    : {'current': 10, 'final': 0.002}\n",
      "number of cells: {'initial': 2, 'optimal': 0} cool_rate: 0.7\n",
      "\n",
      "\n",
      "              f: {'current': 0.08877284595300261, 'neigh': 0.08877284595300261, 'best': 0.1236220472440945}\n",
      "temperature    : {'current': 0.011398895185373131, 'final': 0.002}\n",
      "number of cells: {'initial': 3, 'optimal': 2} cool_rate: 0.7\n",
      "\n",
      "\n",
      "time: 0:01:10.091942\n",
      "37x53.txt\n",
      "              f: {'current': 0.45412490362374713, 'neigh': 0, 'best': 0}\n",
      "temperature    : {'current': 10, 'final': 0.002}\n",
      "number of cells: {'initial': 2, 'optimal': 0} cool_rate: 0.7\n",
      "\n",
      "\n",
      "              f: {'current': 0.36056338028169016, 'neigh': 0.36056338028169016, 'best': 0.4941599281221923}\n",
      "temperature    : {'current': 0.0019158123138056616, 'final': 0.002}\n",
      "number of cells: {'initial': 4, 'optimal': 3} cool_rate: 0.7\n",
      "\n",
      "\n",
      "time: 0:01:02.315459\n"
     ]
    }
   ],
   "source": [
    "from algorithm import SimulatedAnnealing\n",
    "from datetime import datetime\n",
    "tools = imp.reload(tools)\n",
    "\n",
    "SimulatedAnnealing = imp.reload(SimulatedAnnealing)\n",
    "params = {\n",
    "    'initial_temperature' : 10,\n",
    "    'final_temperature'   : 0.002,\n",
    "    'chain_len'           : 4,\n",
    "    'len_of_period'       : 6,\n",
    "    'numb_of_cells'       : 2,\n",
    "    'check'               : 5,\n",
    "    'cooling_rate'        : 0.7,\n",
    "}\n",
    "solutions = {}\n",
    "for txt in txtfiles:\n",
    "    print(txt)\n",
    "    machine_part_matrix = tools.get_data(PATH_TO_BENCHMARKS + txt)\n",
    "    cells_p, cells_m = tools.get_solution(machine_part_matrix, 2)\n",
    "\n",
    "    SA = SimulatedAnnealing.SimulatedAnnealing(machine_part_matrix)\n",
    "    SA.set_params(params)\n",
    "    start = datetime.now()\n",
    "    SA.solve()\n",
    "    solutions[txt] = SA.S['best']\n",
    "    print('time:', datetime.now() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "for sol in solutions:\n",
    "    ret_p, ret_m = {}, {}\n",
    "    for ch in ['p', 'm']:\n",
    "        for ind in solutions[sol][ch]:\n",
    "            list_of_cluster = [ind] * len(solutions[sol][ch][ind])\n",
    "            list_of_val = solutions[sol][ch][ind]\n",
    "            if ch == 'p':\n",
    "                ret_p.update(dict(zip(list_of_val, list_of_cluster)))\n",
    "            else:\n",
    "                ret_m.update(dict(zip(list_of_val, list_of_cluster)))\n",
    "\n",
    "    ret_p = collections.OrderedDict(sorted(ret_p.items()))\n",
    "    ret_m = collections.OrderedDict(sorted(ret_m.items()))\n",
    "    with open('solutions/' + sol, 'w') as the_file:\n",
    "        line_m, line_p = \"\", \"\"\n",
    "        for m in ret_m:\n",
    "            line_m += str(m) + \"_\" + str(ret_m[m]) + \" \"\n",
    "        for p in ret_p:\n",
    "            line_p += str(p) + \"_\" + str(ret_p[p]) + \" \"\n",
    "        the_file.write(line_m + '\\n')\n",
    "        the_file.write(line_p + '\\n')"
   ]
  }
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