demo_on_benchmark_functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Demo on benchmark functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0,'..')\n",
    "sys.path.insert(0,'../..')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from bayes_opt import BayesOpt_KnownOptimumValue,BayesOpt\n",
    "import numpy as np\n",
    "from bayes_opt import vis_ERM,functions\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specify the test function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select the function to be optimized\n",
    "#myfunction=functions.branin(sd=0)\n",
    "myfunction=functions.hartman_3d()\n",
    "#myfunction=functions.hartman_6d()\n",
    "#myfunction=functions.ackley(input_dim=5)\n",
    "#myfunction=functions.gSobol(a=np.array([1,1,1,1,1]))\n",
    "#myfunction=functions.gSobol(a=np.array([1,1,1,1,1,1,1,1,1,1]))\n",
    "\n",
    "\n",
    "func=myfunction.func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Print the black-box function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method hartman_3d.func of <bayes_opt.functions.hartman_3d object at 0x0000029052CE1D88>>\n",
      "f([0,0,0])= [[0.06797412]]\n",
      "f([1,1,1])= [[0.30047607]]\n",
      "Search space: [(0, 1), (0, 1), (0, 1)]\n",
      "f* known optimum value is  3.86278\n"
     ]
    }
   ],
   "source": [
    "# myfunction.func: contains the black-box function\n",
    "print(myfunction.func)\n",
    "\n",
    "print(\"f([0,0,0])=\",myfunction.func([0,0,0]))\n",
    "print(\"f([1,1,1])=\",myfunction.func([1,1,1]))\n",
    "\n",
    "\n",
    "# myfunction.bounds: contains the SearchSpace\n",
    "print(\"Search space:\",myfunction.bounds)\n",
    "\n",
    "# myfunction.fstar: contains the known optimum value\n",
    "print(\"f* known optimum value is \", myfunction.fstar)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specify the acquisition function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#acq_name='ei'\n",
    "#acq_name='ucb'\n",
    "acq_name='erm'\n",
    "#acq_name='cbm'\n",
    "#acq_name='kov_ei'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specify the surrogate model either tgp or gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#IsTGP=0 # using vanilla GP \n",
    "IsTGP=1 # using Transformed GP "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set up the BO class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using transform GP with the known optimum value\n"
     ]
    }
   ],
   "source": [
    "if 'kov' in acq_name or acq_name == 'erm' or acq_name == 'cbm':\n",
    "    bo=BayesOpt_KnownOptimumValue(myfunction.func,myfunction.bounds,fstar=myfunction.fstar, \\\n",
    "                              acq_name=acq_name,IsTGP=1,verbose=0)\n",
    "else:\n",
    "    bo=BayesOpt(myfunction.func,myfunction.bounds,acq_name=acq_name,verbose=0)\n",
    "            \n",
    "if IsTGP==1: \n",
    "    print(\"using transform GP with the known optimum value\")\n",
    "else:\n",
    "    print(\"using vanilla GP without the known optimum value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize BO using 3*dim number of observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "bo.init(n_init_points=3*myfunction.input_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run for 10*dim iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recommended x=[0.384657   0.3368261  0.75005615] current y=-2.095, ymin=-2.095\n",
      "recommended x=[0.43159522 0.36379535 0.73276084] current y=-2.110, ymin=-2.110\n",
      "recommended x=[0.41619775 0.3544163  0.73834301] current y=-2.108, ymin=-2.110\n",
      "recommended x=[0.42887602 0.37820349 0.74077231] current y=-2.282, ymin=-2.282\n",
      "recommended x=[0.40362062 0.44553293 0.78575834] current y=-3.136, ymin=-3.136\n",
      "recommended x=[0.38349598 0.4992977  0.82128287] current y=-3.641, ymin=-3.641\n",
      "recommended x=[0.36415738 0.55131567 0.85536154] current y=-3.826, ymin=-3.826\n",
      "recommended x=[0.35480693 0.57682775 0.87192066] current y=-3.779, ymin=-3.826\n",
      "recommended x=[0.36000329 0.54980969 0.85844176] current y=-3.824, ymin=-3.826\n",
      "recommended x=[0.37093805 0.55803245 0.85171427] current y=-3.824, ymin=-3.826\n",
      "recommended x=[0.36855172 0.552578   0.85883905] current y=-3.822, ymin=-3.826\n",
      "recommended x=[0.34843778 0.56015269 0.83572862] current y=-3.801, ymin=-3.826\n",
      "recommended x=[0.36212322 0.55499744 0.85140145] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.49334596 0.19321409 0.62146789] current y=-0.483, ymin=-3.827\n",
      "recommended x=[0.36249882 0.55454473 0.85177087] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.36252407 0.55498718 0.85190499] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.36248743 0.55473539 0.85181354] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.3625371  0.55469467 0.8518277 ] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.36252537 0.55480723 0.85177649] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.36242922 0.55473456 0.85195101] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.36192545 0.55462275 0.85216125] current y=-3.827, ymin=-3.827\n",
      "recommended x=[0.35416078 0.55330322 0.85270328] current y=-3.829, ymin=-3.829\n",
      "recommended x=[0.27429889 0.55221508 0.84479213] current y=-3.842, ymin=-3.842\n",
      "recommended x=[0.27403626 0.55221404 0.84471887] current y=-3.841, ymin=-3.842\n",
      "recommended x=[0.28064188 0.55838759 0.85059844] current y=-3.845, ymin=-3.845\n",
      "recommended x=[0.27632285 0.56210616 0.85131018] current y=-3.845, ymin=-3.845\n",
      "recommended x=[0.28270766 0.55872616 0.85331348] current y=-3.845, ymin=-3.845\n",
      "recommended x=[0.28550892 0.56001739 0.85162158] current y=-3.844, ymin=-3.845\n",
      "recommended x=[0.26029522 0.557922   0.85513025] current y=-3.849, ymin=-3.849\n",
      "recommended x=[0.21746732 0.55512481 0.86471462] current y=-3.842, ymin=-3.849\n"
     ]
    }
   ],
   "source": [
    "NN=10*myfunction.input_dim\n",
    "for index in range(0,NN):\n",
    "\n",
    "    bo.select_next_point()\n",
    "    \n",
    "    if myfunction.ismax==1:\n",
    "        print(\"recommended x={} current y={:.3f}, ymax={:.3f}\".format(bo.X_ori[-1],bo.Y_ori[-1],bo.Y_ori.max()))\n",
    "    else:\n",
    "        print(\"recommended x={} current y={:.3f}, ymin={:.3f}\".format(bo.X_ori[-1],myfunction.ismax*bo.Y_ori[-1],myfunction.ismax*bo.Y_ori.max()))\n",
    "    sys.stdout.flush()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Performance')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(6, 3))\n",
    "myYbest=[bo.Y_ori[:idx+1].max()* myfunction.ismax for idx,val in enumerate(bo.Y_ori)]\n",
    "plt.plot(range(len(myYbest)),myYbest,linewidth=2,color='m',linestyle='-',marker='o')\n",
    "if myfunction.ismax==1:\n",
    "    plt.hlines(y=myfunction.fstar,xmin=0,xmax=len(myYbest),label='f*')\n",
    "else:\n",
    "    plt.hlines(y=-myfunction.fstar,xmin=0,xmax=len(myYbest),label='f*')\n",
    "\n",
    "plt.xlabel('Iteration',fontsize=14)\n",
    "plt.ylabel('Best Found Value',fontsize=14)\n",
    "plt.legend(prop={'size': 16})\n",
    "plt.title('Performance',fontsize=16)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}