{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os.path\n",
    "import scipy.linalg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Genetic Algorithm \n",
    "\n",
    "In this notebook, we write all the code we need to run a genetic algorithm. We build it from the bottom up.\n",
    "\n",
    "### Support Code\n",
    "\n",
    "We begin by implementing a class called GASsetting that will store all the settings for the algorithm. Fields include\n",
    "\n",
    "<ul>\n",
    "<li>numParents: number of members of breeding pool\n",
    "<li>numChildren: number of individuals in each generation\n",
    "<li>numGenerations: number of generations\n",
    "<li>selection: which selection algorithm to use \n",
    "<li>mutation: scale for std. devation of normal distribution used to randomly sample points for mutation. We pull from a normal distribution centered at the parameter value with std. deviation mutation*value\n",
    "<li>eliteCount: number of individuals from previous generation that get to live into the current generation.\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GASettings:\n",
    "    def __init__( self, numParents = 10, numChildren = 50 ):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write select_pool\n",
    "\n",
    "We will build the genetic algorith bottom-up. First, write the select_pool function, with the members of the previous generation and their costs as input (along with the settings). \n",
    "\n",
    "Select pool should return a tuple (P, Pcost)\n",
    "\n",
    "where P is the numParents x numParameter ndarray of individuals in the breeding pool and Pcost is the array of their costs.\n",
    "\n",
    "If settings.selection is 'uniform', then each member of P is chosen randomly from G (where each invidivual in G is equally likely to be chosen).\n",
    "\n",
    "If settings.selection is 'truncation', then each member of P is chosen randomly from settings.numParents lowest cost individuals in G (where each individual in that set is equally likely to be chosen). We can assume that G is sorted by cost, so this means we choose from the first settings.numParents individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_pool( G, Gcost, settings ):\n",
    "    \"\"\" Select a breding pool from the previous generation.\n",
    "    G is numChildren x numParameters\n",
    "    Gcost is numChildren long and is sorted in ascending order.\n",
    "    Returns (P,Pcost)\n",
    "    where \n",
    "    P is numParents x numParameters\n",
    "    Pcost is numParents long.\n",
    "    \"\"\"\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the selection operators\n",
    "\n",
    "Create individuals with costs 0 to 500, then create breeding pools using each of the selection operators. Plot the costs as histograms. The histogram from the uniform operator should have a uniform distribution of values from 0 to 500. The historgram from the truncation operator should have a uniform distribution of values from 0 to 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test the selection operators\n",
    "# Make fake parameters, using the index as the values (so we can differentiate them)\n",
    "lam = 10000 # number of individuals in generation\n",
    "mu = 1000 # number of individuals in breeding pool\n",
    "NP = 5 # num of parameters per individual\n",
    "G = np.zeros( (lam,NP) )\n",
    "for i in range(lam):\n",
    "    G[i,:] = i\n",
    "Gcost = np.linspace(0.1,500,lam)\n",
    "settings = GASettings(numParents=mu, numChildren=lam)\n",
    "\n",
    "settings.selection = 'uniform'\n",
    "(P, Pcost) = select_pool(G, Gcost, settings)\n",
    "plt.figure( figsize=(8,4) )\n",
    "plt.hist( Pcost );\n",
    "plt.title( \"Breeding Pool Chosen Uniformly\")\n",
    "plt.xlabel( 'Cost')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "settings.selection = 'truncation'\n",
    "(P, Pcost) = select_pool(G, Gcost, settings)\n",
    "plt.figure( figsize=(8,4) )\n",
    "plt.hist( Pcost );\n",
    "plt.title( \"Breeding Pool Chosen with Truncation Selection\")\n",
    "plt.xlabel( 'Cost')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write generateIndividual\n",
    "\n",
    "Write the code that will generate a random individual in parameter space. The individual should have entries between the lower bound and upper bound and should have finite cost. We make 1000 attempts to find a parameter set that leads to a finite code before giving up and just returning np.inf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateIndividual(cost_fcn, lb,ub):\n",
    "    '''\n",
    "    Generate a viable individual (i.e one whos cost function is finite)\n",
    "    and return it and its cost.\n",
    "    If 1000 tries fails to yield an individual, then the paramets and an infinite cost\n",
    "    are returned.\n",
    "    Each individual is generated by randomly chosing each entry params[i] \n",
    "    so that lb[i] <= params[i] <= ub[i]\n",
    "    Input:\n",
    "        cost_fcn: handle to the cost function (which should take a candidate as input)\n",
    "        lb: lower bounds for parameters (array of length NP)\n",
    "        ub: upper bounds for parameters (array of length NP)\n",
    "    Returns:\n",
    "        (params, cost)\n",
    "        where params is the same shape as the lower bounds\n",
    "        and cost is the cost for that set of parameters\n",
    "    '''\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test generateIndividual\n",
    "\n",
    "Run generateIndividual with the pattern cost function to generate 1000 individuals, storing each one as a row in a 1000x7 ndarray. Use [0,4,1,1,2,2,0] as the lower bounds and [5,6,3,2,4,4,2] as the upper bounds.\n",
    "\n",
    "Test it by plotting the result as 5 columns of values (i.e. x-axis is 1:7 and the y-axis is the parameter value). This isn't much of a test, but it makes sure that the points are in the expected ranges. We wil also plot the costs (individual's index on x-axis, cost on y-axis) just to see that it was computed within a range that makes sense. It should look like a busy rectangle full or asterisks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pattern_cost_function( params ):\n",
    "    return scipy.linalg.norm( params-np.array([1,5,2.5,2,4,3,0]) )\n",
    "\n",
    "lam = 1000 # number of individuals in generation\n",
    "mu = 100 # number of individuals in breeding pool\n",
    "NP = 7 # num of parameters per individual\n",
    "settings = GASettings(numParents=mu, numChildren=lam)\n",
    "\n",
    "lb = np.array([0,4,1,1,2,2,0])\n",
    "ub = np.array([5,6,3,2,4,4,2])\n",
    "G = np.zeros( (lam,NP) )\n",
    "Gcost = np.zeros( (lam,))\n",
    "for i in range(lam):\n",
    "    (G[i,:], Gcost[i]) = generateIndividual(pattern_cost_function,lb,ub)\n",
    "    \n",
    "plt.subplot( 2, 1, 1 )\n",
    "plt.plot( np.arange(1,NP+1), G.T, '*');\n",
    "plt.subplot( 2, 1, 2 )\n",
    "plt.plot( np.arange(1,lam+1), Gcost, '*');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write generateChild\n",
    "\n",
    "Write a function that take the cost function, breeding pool, lower bounds, upper bounds, and settings as input. It should generate children from the breeding pool using cross-over and mutation. \n",
    "\n",
    "<ul>\n",
    "    <li>Initialize the return values (params is an np.array the same shape as lb and cost is np.inf).\n",
    "    <li>For each attempt at making a child with finite code (allow 1000 attempts)\n",
    "        <ul>\n",
    "        <li>Choose a first parent from the breeding pool (choose an index between 0 and the number of rows in the breeding pool P).\n",
    "        <li>Choose a second parent from the breeding pool (choose an index between 0 and the number of rows in the breeding pool P).    \n",
    "        <li>Loop through the elements of the new child, randomly choosing one of the parents to inherit from.\n",
    "        <li>Mutate each of the values in the child by adding a number that is chosen from a normal distribution centered at zero, with a standard deviation of settings.mutation*the_value_in_the_child\n",
    "        <li>Make sure all of the values in the child are in bounds (>= lb and <= ub).\n",
    "        <li>Compute the cost of the child.\n",
    "        <li>If the cost is finite, return the child and its cost\n",
    "    </ul>\n",
    "    <li>If we tried 1000 times to create a child and never found one with a finite cost, just give up and return (np.zeros((lb.size)), np.inf)\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generateChild(cost_fcn, P, lb, ub, settings):\n",
    "    \"\"\"\n",
    "    Generate a viable child (i.e one whos cost function is finite)\n",
    "    and return it and its cost function.\n",
    "    If 1000 tries fails to yield a parent, then an infinite cost\n",
    "    is returned.\n",
    "    Input Parameters:\n",
    "        cost_fcn: handle to the cost function (which should take a candidate as input)\n",
    "        P: parents (lam x NP ndarray of parameter values)\n",
    "        lb: lower bounds for parameters (array of length NP)\n",
    "        ub: upper bounds for parameters (array of length NP)\n",
    "        settings: a GASettings object\n",
    "    Returns:\n",
    "        (params, cost)\n",
    "        where params is the same shape as the lower bounds\n",
    "        and cost is the cost for that set of parameters\n",
    "    \"\"\"\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test generateChild\n",
    "\n",
    "We will test it with the pattern_cost_function, so there are 7 parameters in each individual. Randomly create 2 members of the breeding pool and make their entries values between 0 and 4. Then generate a child. Print out the breeding pool and the child. Read the entries to make sure it looks like each one came from one parent and that it is slightly mutated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breeding Pool\n",
      "[[0. 0. 0. 0. 0. 0. 0.]\n",
      " [1. 1. 1. 1. 1. 1. 1.]]\n",
      "Child\n",
      "[0.         1.01173377 0.         1.01692256 1.04487793 1.00201595\n",
      " 1.05352062]\n"
     ]
    }
   ],
   "source": [
    "lam = 50 # number of individuals in generation\n",
    "mu = 2 # number of individuals in breeding pool\n",
    "NP = 7 # num of parameters per individual\n",
    "P = np.zeros( (mu,NP) )\n",
    "for i in range(mu):\n",
    "    P[i,:] = i % 5\n",
    "settings = GASettings(numParents=mu, numChildren=lam)\n",
    "\n",
    "lb = np.zeros((7,))\n",
    "ub = 5 + np.zeros((7,))\n",
    "(params,cost) = generateChild( pattern_cost_function, P, lb, ub, settings )\n",
    "\n",
    "print( \"Breeding Pool\")\n",
    "print( P )\n",
    "print( \"Child\")\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write the genetic algorithm\n",
    "\n",
    "<ul>\n",
    "    <li>Create an initial population of settings.numChildren individuals in a (settings.numChildren x NP) ndarray and generate their costs (putting them into an array called Gcost). (Call generateIndividual in a loop.)\n",
    "    <li>Sort the individuals by cost (in preparation for selection). This means use argsort to get the indices and use the same set of indices to reorder G and Gcost.\n",
    "    <li>Create lists (all_params and all_costs) to store the G and Gcost arrays for each generation and put G and Gcost from this initial generation into the lists.\n",
    "    <li>For each generation (range(settings.numGenerations))\n",
    "        <ul>\n",
    "        <li>Create an (settings.numChildren x NP) ndarray to store the individuals from the current generation and a (NP,) array to store their costs.\n",
    "        <li>Take the settings.eliteCount best children from the previous generation and put them into this generation.\n",
    "        <li>Select the breeding pool P of settings.numParents individuals from the previous generation.\n",
    "        <li>For i in range(settings.eliteCount,settings.numChildren)\n",
    "            <ul>\n",
    "            <li>Call generateChild and put the results into the G and Gcost arrays for the current generation.\n",
    "            </ul>\n",
    "        <li>Sort the individuals in G by cost (in preparation for selection)\n",
    "        <li>Append G and Gcost ndarrays to all_params and all_costs, respectively.\n",
    "        </ul>\n",
    "    <li>Assign to params the first (lowest cost) individual of the last generation.\n",
    "    <li>Assign to cost the first (lowest cost) cost of the last generation.\n",
    "    <li>Return (params, cost, all_params, all_costs)\n",
    "</ul>   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def GA(cost_fcn, lb, ub, settings):\n",
    "    \"\"\" Run the algorithm described in the notes to find a low-cost\n",
    "        population of parameter sets.\n",
    "        Input:\n",
    "            cost_fcn: handle to cost function (should take a paramer set as input)\n",
    "            lb: lower bounds for parameters (numpy array of length NP)\n",
    "            ub: lower bounds for parameters (numpy array of length NP)\n",
    "            settings: GASettings object\n",
    "        Returns\n",
    "            (params, cost, all_params, all_costs)\n",
    "            where \n",
    "            params is a 1xNP vector with the best parameter set found\n",
    "            cost is a scalar with the cost of the best parameter set\n",
    "            all_params is a list with a (settings.numChildren x NP) ndarray of parameters for each generation\n",
    "            all_costs is a list with an NP-length array of costs for each generation\n",
    "    \"\"\"\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the GA\n",
    "\n",
    "Test it with the pattern cost function. Plot the costs for all individuals in all the generations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best of generation  1  has cost  2.367095806882881\n",
      "Best of generation  2  has cost  1.7856015557357314\n",
      "Best of generation  3  has cost  1.7856015557357314\n",
      "Best of generation  4  has cost  1.4204049340723046\n",
      "Best of generation  5  has cost  1.0467206560778475\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PN = 7\n",
    "lb = np.zeros( (PN,) );\n",
    "ub = 5 + np.zeros( (PN,) );\n",
    "cost_fcn = pattern_cost_function;\n",
    "settings = GASettings(numParents=10, numChildren=50)\n",
    "settings.numGenerations = 5;\n",
    "settings.eliteCount = 1;\n",
    "settings.selection = 'truncation';\n",
    "\n",
    "(params, cost, all_params, all_costs) = GA(pattern_cost_function, lb, ub, settings);\n",
    "\n",
    "plt.figure( figsize=(8,4) )\n",
    "legs = []\n",
    "for i in range(len(all_costs)):\n",
    "    plt.plot( np.arange(all_costs[i].size), all_costs[i], '*' )\n",
    "    legs.append( \"Generation\" + str(i) )\n",
    "plt.legend( legs );"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}