ec_lab2_Zakharov_Denis.ipynb

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    "> <picture>\n",
    ">   <source media=\"(prefers-color-scheme: light)\" srcset=\"https:\/\/raw.githubusercontent.com\/Mqxx\/GitHub-Markdown\/main\/blockquotes\/badge\/light-theme\/issue.svg\">\n",
    ">   <img alt=\"Issue\" src=\"https:\/\/raw.githubusercontent.com\/Mqxx\/GitHub-Markdown\/main\/blockquotes\/badge\/dark-theme\/issue.svg\">\n",
    "> <\/picture><br>\n",
    ">\n",
    "> **Issue**\n",
    "> \n",
    ">  Implement solving of Queens Puzzle with evolutionary algorithm. For example, [genetic algorithm](https:\/\/en.wikipedia.org\/wiki\/Eight_queens_puzzle).\n",
    "> \n",
    "> The goal is to design algorithm that would solve queens puzzle with at least 8x8 board. But you can try bigger sizes. As a pattern you can use the same script as for 1st assignment, but modify all required things to work with discrete problem. It is important to design how you will encode solution (how you represent a solution in a code)  and design correspond mutation\\crossover operators. \n",
    "Try to print in console your best found solution at the end of your script."
   ],
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   "cell_type":"code",
   "source":[
    "!pip install deap"
   ],
   "execution_count":29,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Requirement already satisfied: deap in \/opt\/python\/envs\/default\/lib\/python3.8\/site-packages (1.4.1)\r\n",
      "Requirement already satisfied: numpy in \/opt\/python\/envs\/default\/lib\/python3.8\/site-packages (from deap) (1.24.3)\r\n",
      "\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\r\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
     ],
     "output_type":"stream"
    }
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  {
   "cell_type":"code",
   "source":[
    "from itertools import cycle\n",
    "\n",
    "import math\n",
    "import random\n",
    "\n",
    "from multiprocessing import Pool\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy.random as rnd\n",
    "\n",
    "import deap\n",
    "from deap import tools, base, creator, algorithms\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ],
   "execution_count":30,
   "outputs":[
    
   ],
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   "cell_type":"code",
   "source":[
    "creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n",
    "creator.create(\"Individual\", list, fitness=creator.FitnessMin)"
   ],
   "execution_count":31,
   "outputs":[
    {
     "name":"stderr",
     "text":[
      "\/opt\/python\/envs\/default\/lib\/python3.8\/site-packages\/deap\/creator.py:185: RuntimeWarning:\n",
      "\n",
      "A class named 'FitnessMin' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
      "\n",
      "\/opt\/python\/envs\/default\/lib\/python3.8\/site-packages\/deap\/creator.py:185: RuntimeWarning:\n",
      "\n",
      "A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
      "\n"
     ],
     "output_type":"stream"
    }
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   "source":[
    "class QueensExperiment:\n",
    "    def __init__(self, queens, fitness_evaluation, population_size, generations, crossover_rate, mutation_rate, mutation_function):\n",
    "        self.individual_generator = lambda: random.choices(range(queens**2), k=queens)\n",
    "        self.fitness_evaluation = fitness_evaluation\n",
    "        self.population_size = population_size\n",
    "        self.generations = generations\n",
    "        self.crossover_rate = crossover_rate\n",
    "        self.mutation_rate  = mutation_rate\n",
    "        self.mutation_function  = mutation_function\n",
    "\n",
    "        self.toolbox = base.Toolbox()\n",
    "        self.toolbox.register(\"individual\", tools.initIterate, creator.Individual, self.individual_generator)\n",
    "        self.toolbox.register(\"population\", tools.initRepeat, list, self.toolbox.individual)\n",
    "\n",
    "        # fitness evaluation function\n",
    "        self.toolbox.register(\"evaluate\", lambda individual: (fitness_evaluation(individual),))\n",
    "\n",
    "        # mutators \n",
    "        self.toolbox.register(\"mate\", tools.cxTwoPoint)\n",
    "        self.toolbox.register(\"mutate\", mutation_function[0], **mutation_function[1])\n",
    "\n",
    "        # selection function\n",
    "        self.toolbox.register(\"select\", tools.selTournament, tournsize=4)\n",
    "\n",
    "\n",
    "    def run(self):\n",
    "        pop = self.population_size\n",
    "        # Fittest individual with a hall of fame record\n",
    "        hof = tools.HallOfFame(3, np.array_equal)\n",
    "        \n",
    "        # generation statistics logging\n",
    "        stats = tools.Statistics(lambda ind: ind.fitness.values[0])\n",
    "        \n",
    "        stats = tools.Statistics(key=lambda individual: individual.fitness.values)\n",
    "        stats.register(\"std\", lambda population: np.std([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "        stats.register(\"avg\", lambda population: np.mean([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "        stats.register(\"min\", lambda population: np.min([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "        stats.register(\"max\", lambda population: np.max([fitness for fitness in population if fitness[0] != math.inf]))\n",
    "\n",
    "        \n",
    "\n",
    "        _, log = algorithms.eaSimple(\n",
    "            self.toolbox.population(n=pop),\n",
    "            self.toolbox,\n",
    "            ngen=generations,\n",
    "            cxpb=crossover_rate, \n",
    "            mutpb=mutation_rate,\n",
    "            stats=stats, \n",
    "            halloffame=hof, \n",
    "            verbose=False,\n",
    "        )\n",
    "\n",
    "        print(\"Best = {}\".format(hof[0]))\n",
    "        print(\"Best fit = {}\".format(hof[0].fitness.values[0]))\n",
    "        return log, hof[0]"
   ],
   "execution_count":32,
   "outputs":[
    
   ],
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    "def starigin(individual):\n",
    "    #  removed duplicate values \n",
    "    if len(individual) != len(set(individual)):\n",
    "        return math.inf\n",
    "\n",
    "    dimension = len(individual)\n",
    "    # count all pairs of conflicts\n",
    "    fitness: float = 0\n",
    "    for x in range(len(individual)):\n",
    "        x_row, x_column = individual[x] \/\/ dimension, individual[x] % dimension\n",
    "        for y in range(x + 1, len(individual)):\n",
    "            y_row, y_column = individual[y] \/\/ dimension, individual[y] % dimension\n",
    "            if x_row == y_row or x_column == y_column or abs(x_row - y_row) == abs(x_column - y_column):\n",
    "                fitness += 1\n",
    "    \n",
    "    return fitness"
   ],
   "execution_count":33,
   "outputs":[
    
   ],
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   "source":[
    "QUEENS_NUMBER = 8    \n",
    "generations = 100 # set it to 100\n",
    "pop_size = 2500      # lets say max is 100\n",
    "crossover_rate=0.5\n",
    "mutation_rate=0.5\n",
    "mutation_function= (\n",
    "    tools.mutUniformInt, \n",
    "    {\"low\": 0, \"up\": 8**2 - 1, \"indpb\": 1\/4}\n",
    ")\n",
    "\n",
    "scenario = QueensExperiment(QUEENS_NUMBER, starigin, pop_size, generations, crossover_rate, mutation_rate, mutation_function)\n",
    "log, fittest = scenario.run()\n",
    "# draw_log(log)"
   ],
   "execution_count":34,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Best = [26, 57, 46, 52, 13, 32, 3, 23]\n",
      "Best fit = 0.0\n"
     ],
     "output_type":"stream"
    }
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   "cell_type":"code",
   "source":[
    "def plot_curves(generation, deviation, average, minimum, maximum):\n",
    "    fig = go.Figure()\n",
    "    palette = cycle(px.colors.qualitative.Plotly)\n",
    "\n",
    "    # Add traces\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=generation, y=deviation,\n",
    "        name=\"Deviation\",\n",
    "        marker_color=next(palette),\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=generation, y=average,\n",
    "        name=\"Average\",\n",
    "        marker_color=next(palette),\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=generation, y=minimum,\n",
    "        name=\"Min\",\n",
    "        marker_color=next(palette),\n",
    "    ))\n",
    "\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=generation, y=maximum,\n",
    "        name=\"Max\",\n",
    "        marker_color=next(palette),\n",
    "    ))\n",
    "\n",
    "    fig.update_layout(\n",
    "            title=\"Solving of Queens Puzzle\",\n",
    "            xaxis_title=\"Generation\",\n",
    "            yaxis_title=\"Fitness\",\n",
    "            legend_title=\"Who 🗿?\",\n",
    "    )\n",
    "    fig.show()\n",
    "\n",
    "\n",
    "plot_curves(*log.select(\"gen\", \"std\", \"avg\", \"min\", \"max\"))"
   ],
   "execution_count":36,
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    "print(f\"Pos: {fittest}\")\n",
    "print(f\"Dups: {len(fittest) - len(set(fittest))}\")"
   ],
   "execution_count":49,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "Pos: [26, 57, 46, 52, 13, 32, 3, 23]\n",
      "Dups: 0\n"
     ],
     "output_type":"stream"
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    "    dimension = len(individual)\n",
    "    board = pd.DataFrame(\"\", index=range(1, dimension+1), columns=range(1, dimension+1))\n",
    "\n",
    "    for x in range(dimension):\n",
    "        x_row, x_col = individual[x] \/\/ dimension, individual[x] % dimension\n",
    "        for y in range(x+1, dimension):\n",
    "            y_row, y_col = individual[y] \/\/ dimension, individual[y] % dimension\n",
    "            if (x_row == y_row and x_col == y_col) or (abs(x_row-y_row) == abs(x_col-y_col)):\n",
    "                board[1+x_col][1+x_row] = \"❌\"\n",
    "\n",
    "    for queen in individual:\n",
    "        row, col = queen \/\/ dimension, queen % dimension\n",
    "        board[1+col][1+row] = \"🗿\" if board[1+col][1+row] == \"\" else \"🗿\"\n",
    "\n",
    "    print(board)"
   ],
   "execution_count":47,
   "outputs":[
    
   ],
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   "cell_type":"markdown",
   "source":[
    "> <picture>\n",
    ">   <source media=\"(prefers-color-scheme: light)\" srcset=\"https:\/\/raw.githubusercontent.com\/Mqxx\/GitHub-Markdown\/main\/blockquotes\/badge\/light-theme\/complete.svg\">\n",
    ">   <img alt=\"Complete\" src=\"https:\/\/raw.githubusercontent.com\/Mqxx\/GitHub-Markdown\/main\/blockquotes\/badge\/dark-theme\/complete.svg\">\n",
    "> <\/picture><br>\n",
    ">\n",
    "> Solved Queens Puzzle"
   ],
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   },
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     "node_id":"RNUwH93giYkbcGe8UmDnvf",
     "type":"MD",
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  },
  {
   "cell_type":"code",
   "source":[
    "display_grid(fittest)"
   ],
   "execution_count":48,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "   1  2  3  4  5  6  7  8\n",
      "1           🗿            \n",
      "2                 🗿      \n",
      "3                       🗿\n",
      "4        🗿               \n",
      "5  🗿                     \n",
      "6                    🗿   \n",
      "7              🗿         \n",
      "8     🗿                  \n"
     ],
     "output_type":"stream"
    }
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