{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial 3: solving your first optimization problem with ABC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "# Set Plotly to render plots as interactive in notebooks (this cell will be hidden: I set \"nbsphinx\": \"hidden\" in the cell metadata)\n",
    "import plotly.io as pio\n",
    "pio.renderers.default = \"notebook\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, you will learn how to use the *Artificial Bee Colony (ABC)* algorithm to optimize a complex objective function. You will get through the process of setting up the `ArtificialBeeColony` class and the configuration of its `optimize` method. As an example, we will consider the *Eggholder* function, a 2D function defined as follows:\n",
    "\n",
    "$$f((x_1,x_2)) = -(x_2 + 47) \\sin\\left(\\sqrt{\\left|x_2 + \\frac{x_1}{2} + 47\\right|}\\right) - x_1 \\sin\\left(\\sqrt{|x_1 - (x_2 + 47)|}\\right)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to define a `BenchmarkFunction` object that represents the *Eggholder* function. We can either define it from scratch or we can import it directly from the `beeoptimal.benchmarks` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Benchmark:\n",
      " Eggholder\n",
      "\n",
      "Default Bounds:\n",
      " [[-512  512]\n",
      " [-512  512]]\n",
      "\n",
      "Optimal Solution:\n",
      " [512.     404.2319]\n",
      "\n",
      "Optimal Value:\n",
      " -959.6406627106155\n"
     ]
    }
   ],
   "source": [
    "from beeoptimal.benchmarks import Eggholder\n",
    "\n",
    "print(f\"\\nBenchmark:\\n {Eggholder.name}\")\n",
    "print(f\"\\nDefault Bounds:\\n {Eggholder.bounds}\")\n",
    "print(f\"\\nOptimal Solution:\\n {Eggholder.optimal_solution}\")\n",
    "print(f\"\\nOptimal Value:\\n {Eggholder.optimal_value}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to import and instanciate an `ArtificialBeeColony` object. Its attributes are the following:\n",
    "\n",
    "- `colony_size`: the number of bees in the colony (at least 10 in order to ensure compatibility with all the implemented mutation strategies)\n",
    "- `function`: the function to optimize\n",
    "- `bounds`: the search space bounds\n",
    "- `n_employed_bees`: the number of employed bees\n",
    "- `max_scouts`: the maximum number of scout bees per iteration\n",
    "\n",
    "The most simple way to initialize it is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from beeoptimal import ArtificialBeeColony\n",
    "\n",
    "ABC = ArtificialBeeColony(\n",
    "    colony_size = 100,\n",
    "    function    = Eggholder.fun,\n",
    "    bounds      = Eggholder.bounds,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. note::\n",
    "    If the attributes `n_employed_bees` and `max_scouts` are not provided, they will be both set to `colony_size//2`.\n",
    "    In case you want to use a different number of employed bees or max scouts, just set them manually.\n",
    "\n",
    "\n",
    "When specifying `n_employed_bees` and `max_scouts`, make sure to respect the following constraints:\n",
    "        \n",
    "- 5 $<$ `n_employed_bees` $\\leq$ `colony_size`\n",
    "- 0 $<$ `max_scouts` $\\leq$ `n_employed_bees`\n",
    "    \n",
    "where the condition `n_employed_bees` > 5 is needed to ensure compatibility with all the implemented mutation strategies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are finally ready to call the `optimize` method. In this example, we will set up a standard configuration for the ABC algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running Optimization: 100%|██████████|[00:05<00:00]\n"
     ]
    }
   ],
   "source": [
    "ABC.optimize(\n",
    "    max_iters        = 1000,\n",
    "    limit            = 'default',\n",
    "    selection        = 'RouletteWheel',\n",
    "    mutation         = 'StandardABC',\n",
    "    initialization   = 'random',\n",
    "    stagnation_tol   = 1e-6,\n",
    "    random_seed      = 1234,\n",
    "    verbose          = True \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************\n",
      "Optimal Solution:\n",
      "\tExpected : [512.     404.2319]\n",
      "\tFound    : [512.         404.23180504]\n",
      "Optimal Value:\n",
      "\tExpected : -959.6406627106155\n",
      "\tFound    : -959.640662720851\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "print('*'*50)\n",
    "print(f\"Optimal Solution:\")\n",
    "print(f\"\\tExpected : {Eggholder.optimal_solution}\")\n",
    "print(f\"\\tFound    : {ABC.optimal_bee.position}\")\n",
    "print(f\"Optimal Value:\")\n",
    "print(f\"\\tExpected : {Eggholder.optimal_value}\")\n",
    "print(f\"\\tFound    : {ABC.optimal_bee.value}\")\n",
    "print('*'*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to experiment with another configuration, you can just reset the `ArtificialBeeColony` object and call the `optimize` method again, without the need of instantiating a new object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ABC.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another possible configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Running Optimization:  86%|████████▌ |[00:05<00:00]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Early termination: Optimization stagnated at iteration 861 / 1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ABC.optimize(\n",
    "    max_iters        = 1000,\n",
    "    limit            = 'default',\n",
    "    selection        = 'Tournament',\n",
    "    tournament_size  = 20,\n",
    "    mutation         = 'ABC/best/1',\n",
    "    initialization   = 'cahotic',\n",
    "    sf               = 1.0,\n",
    "    self_adaptive_sf = False,\n",
    "    stagnation_tol   = 1e-6,\n",
    "    random_seed      = 1234,\n",
    "    verbose          = True \n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**************************************************\n",
      "Optimal Solution:\n",
      "\tExpected : [512.     404.2319]\n",
      "\tFound    : [512.         404.23180513]\n",
      "Optimal Value:\n",
      "\tExpected : -959.6406627106155\n",
      "\tFound    : -959.640662720851\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "print('*'*50)\n",
    "print(f\"Optimal Solution:\")\n",
    "print(f\"\\tExpected : {Eggholder.optimal_solution}\")\n",
    "print(f\"\\tFound    : {ABC.optimal_bee.position}\")\n",
    "print(f\"Optimal Value:\")\n",
    "print(f\"\\tExpected : {Eggholder.optimal_value}\")\n",
    "print(f\"\\tFound    : {ABC.optimal_bee.value}\")\n",
    "print('*'*50)"
   ]
  }
 ],
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