asdf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Michael_wang/Documents/venkat/dragonfly/dragonfly/utils/oper_utils.py:30: UserWarning: cannot import name 'direct' from 'dragonfly.utils.direct_fortran' (/Users/Michael_wang/Documents/venkat/dragonfly/dragonfly/utils/direct_fortran/__init__.py)\n",
      "Could not import Fortran direct library. Dragonfly can still be used, but might be slightly slower. To get rid of this warning, install a numpy compatible Fortran compiler (e.g. gfortran) and the python-dev package and reinstall Dragonfly.\n",
      "  warn('%s\\n%s'%(e, fortran_err_msg))\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "from dragonfly import minimise_function, maximise_function\n",
    "from dragonfly.exd.domains import Domain, EuclideanDomain, DiscreteDomain, DiscreteNumericDomain, CartesianProductDomain\n",
    "# from examples.synthetic.park1_3 import park1_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "ParkTmp = [31.571042083475817, 22.2388920869964, 18.224354456520338, 20.356787418961623, 22.301787997804727, 24.37569313605084, 32.010607091070334, 11.869673004028902, 19.155932991981768, 30.061664871315752, 31.59250103739458, 24.761818737836972, 27.411511055694465, 27.498012039401225, 13.219726050891389, 35.527344747679436, 18.78212236312347, 36.945923810941295, 47.20201347437688, 45.922158654958494, 39.897805231798344, 21.08616919908061, 40.57384199762706, 48.14994821463779, 51.869270703427745, 31.510009711644205, 49.2826013877295, 41.07641972522012, 50.16916051992802, 39.4132900866027, 56.3302562521023, 36.14247827152049, 31.416380001145725, 65.49748732543097, 69.12334322170797, 66.34343253271214, 66.75924183427492, 60.90879268595862, 52.475325119929096, 58.885266500894815, 49.91768180896703, 68.18527597640451, 65.7302493768162, 72.30731931555964, 67.5576496387277, 56.61926886136498, 51.71586944148392, 68.5811863776474, 59.7398106632574, 24.32550466459052, 40.58134813429151, 66.17280457216762, 51.49435822460447, 28.41540169045412, 47.66046535559914, 66.53225982393141, 65.69657100075202, 60.75893547384831, 58.32883462471646, 41.93153559099463, 54.12097503503854, 65.21389742840232, 13.427655120612421, 9.459670876929755, 45.50647254566187, 57.43430615794723, 59.742057622640544, 41.62593122752227, 45.910795084710315, 53.46416178857494, 27.213588293905595, 33.43127204170706, 65.9945400469483, 60.14745380604782, 45.15825967482705, 42.77877501919476, 40.888358806335866, 69.84649592994687, 56.01743231219919, 41.9881603577615, 51.745632265060365, 57.92288602602065, 38.50809699678859, 43.49506081517939, 60.470527161657756, 69.18158041920485, 42.734312739542375, 49.06713567443559, 47.821241178254496, 66.79571896095202, 62.66225970389804, 56.64005643627392, 58.331840899264165, 61.73361832196541, 65.41152052564625, 56.213220089162846, 66.35148135638438, 68.85435805793549, 63.56853692935865, 65.3402205469685, 58.234413241464765, 52.03176310579388, 38.91558236897265, 59.2367252828555, 52.90347842655364, 64.89446667246884, 51.46017457167819, 65.64148436867336, 61.622108583535336, 66.82036535860675, 60.17943739350481, 61.089003193025775, 46.76835639780716, 27.428867691151414, 59.985661798378885, 64.88288722889769, 63.52223944526649, 46.508546105013124, 62.48563662556499, 18.734845988804867, 66.18173345347049, 25.396821363428934, 66.87959814160033, 44.76283755857267, 56.54560040182633, 66.92979186284154, 64.77949852115016, 54.15545024362084, 34.772834960190195, 56.71131557707247, 48.80467237980226, 61.81167291296322, 60.0048394805942, 52.6227247306807, 48.59755856258701, 39.607569941252954, 53.06611743921093, 63.68073425708543, 28.029429942087603, 61.42359132493742, 50.01951474552523, 65.17168173569713, 66.44389993106745, 67.09651336300354, 65.35306200984597, 61.86864316216648, 67.33034031952809, 62.79440197559937, 52.6664125287334, 21.36742996170994, 13.834293894210555, 60.406719424297705, 66.91516507625482, 61.74327706992674, 60.74099935158508, 54.71783475344486, 35.488324086518936, 51.09079381723357, 65.88274615510824, 42.22603250061302, 31.111141219206388, 60.81454521622575, 64.69277506409443, 64.6771622886248, 65.00877283482448, 63.92630365288913, 53.868387899884, 27.297582904185553, 65.64713842048236, 21.51260959635021, 65.24379366136458, 65.41966087148516, 64.30480127134346, 62.29804835588163, 39.419291370497746, 20.347114550732172, 33.85454362192723, 34.76545355888314, 41.76227915391964, 62.79353483781539, 61.4905683324111, 39.46584975269419, 43.958169776567665, 47.42669768323628, 26.725805104797058, 64.97546063479771, 63.93932279986685, 58.5817394993668, 67.09630350139544, 21.985932081320904, 56.5161939383635, 37.999939505454364, 44.86997270530278, 26.266685524074305, 40.777110449745024, 60.99295091547842, 66.16162429624646, 48.931032095172135, 65.44305885786054, 48.90183491113858, 35.014174334771454]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "HartmannTmp = [0.009961279103757551, 0.009080869078187698, 0.4100093706123535, 0.056283715967204696, 0.1273169626456759, 0.32534426391837584, 8.254240331661957e-05, 0.04901653309473311, 0.2867248649023161, 0.07150396078462033, 0.5698012140773883, 0.3242260140122053, 0.07315897490729545, 0.0157459608688191, 0.5071488002897708, 0.4138378065208358, 0.061037222507224514, 0.15316080062326545, 0.04131694907418375, 0.41379261129906975, 0.07555376822967877, 0.17041022828268113, 0.3730076046330886, 0.07678151998544525, 0.012258282101074233, 0.6965797351094867, 0.5424691744868904, 2.3123320149759593e-05, 0.4339925475117694, 0.12404744169886829, 0.11429578442244576, 0.6541054245403558, 0.09517203383183472, 0.668245661404521, 0.04135888358456952, 0.7054675941922556, 0.3904366079724312, 0.11953282317812075, 0.4677267649975694, 0.3904366079724312, 0.7477055670174223, 1.2718779874932475, 0.3082107961752474, 0.008131117956021821, 1.1677637693920448, 1.5964761397548342, 0.3521316849054991, 0.2404559089533357, 0.05086716996939685, 0.05086716996939685, 0.5253117929690532, 0.5499027415623274, 2.406967291861301, 0.6436454942837935, 0.7054675941922556, 0.6207880295112693, 0.7054675941922556, 0.7054675941922556, 2.406967291861301, 2.406967291861301, 0.03154736200820294, 2.406967291861301, 0.7054675941922556, 0.05086716996939685, 0.7054675941922556, 0.9704513486842862, 1.2102829613832529, 2.406967291861301, 2.406967291861301, 0.265434263748869, 2.3646488728063697, 2.406967291861301, 2.406967291861301, 2.406967291861301, 1.2667929923341017, 0.02186418424137665, 0.015762274089210698, 0.0027454706579197567, 2.406967291861301, 0.6065498700246778, 2.570944516195897, 2.406967291861301, 2.1230045972663087, 1.1362201477211042, 2.4934155359558794, 0.4567858735414934, 2.570944516195897, 2.570944516195897, 2.0599784030787185, 2.406967291861301, 2.570944516195897, 1.9612130482152985, 2.3646488728063697, 2.610619638683059, 2.570944516195897, 0.19454123815365545, 2.570944516195897, 2.570944516195897, 2.570944516195897, 0.1374303573148255, 2.7832741882058483, 2.1692442659934805, 2.3577064651066983, 1.1509758395210392, 0.3276024833988021, 1.7306002161239085, 0.15141112804835924, 2.4934155359558794, 2.0801170662319395, 2.9212960354015007, 0.016939922911341485, 0.016939922911341485, 0.2277139863896705, 0.04005850163965837, 0.09511461969098743, 0.016939922911341485, 1.650679500384437, 0.20387580927872595, 0.01666260477852286, 3.2519826854978433, 0.7212985686212204, 0.13544346987655853, 0.13544346987655853, 2.68611968263253, 1.1332160708034777, 3.0659787150482423, 2.4507403741703677, 0.01666260477852286, 2.4617447596847772, 3.289874817422669, 1.1574757586673665, 3.2582882447195316, 1.4760610720708194, 3.2530283995476936, 0.1119616809714454, 1.1332160708034777, 2.7840473615046313, 3.0372831849858337, 2.2732694674915415, 0.3594323087949351, 2.841232716147028, 2.0679594243368875, 0.6866793613654965, 2.58034337272188, 1.8796713916004901, 2.406967291861301, 2.724483478192966, 0.3872045088142194, 0.6156408827532064, 0.20387580927872595, 2.7321453251648435, 0.043749444887314866, 0.00665154193519014, 2.300536183389814, 3.219382919610214, 3.2861507786839423, 0.7346877225306118, 0.7346877225306118, 0.04493419665990739, 0.04493419665990739, 0.38018592552033653, 1.6232794066398737, 2.2143097643794625, 0.6866793613654965, 0.04307076266111809, 3.2623517778722513, 2.9876732327951183, 0.13544346987655853, 2.7856410451881017, 0.38018592552033653, 0.18095104370286988, 2.3141589153408355, 1.4669308160802197, 0.007849800697490401, 2.770850073408867, 0.38018592552033653, 1.0491832627703808, 0.09959497810197723, 0.019412047158619588, 0.009691501695329641, 0.04553000144633329, 0.5603403935796208, 0.31443309184970425, 0.31443309184970425, 0.21228862639208906, 2.3141589153408355, 2.570944516195897, 2.68611968263253, 2.798021039377726, 2.68611968263253, 0.092989161004796, 3.054458208179067, 0.092989161004796, 3.0918472076017838, 2.570944516195897, 0.7487348018433607, 0.38018592552033653, 3.088304188419846, 0.13805899109993575, 2.9790686508545257, 3.248657320873616]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.6394906869904958e-06\n"
     ]
    }
   ],
   "source": [
    "print(np.min(tmp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp2 = []\n",
    "tmpMax = 0\n",
    "for t in ParkTmp:\n",
    "    if t > tmpMax:\n",
    "        tmp2.append(t)\n",
    "        tmpMax = t\n",
    "    else:\n",
    "        tmp2.append(tmp2[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'evaluations')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tmp2)\n",
    "plt.title(\"Park1_3x3 (12d) with Dragonfly\")\n",
    "# plt.title(\"Hartmann_6 (6d) with Dragonfly\")\n",
    "plt.xlabel(\"evaluations\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0_domain = DiscreteDomain([0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1])\n",
    "x1_domain = DiscreteDomain([0,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1])\n",
    "x1 = CartesianProductDomain([x0_domain, x1_domain])\n",
    "x2_domain = DiscreteDomain([4, 10, 23, 45, 78, 87.1, 91.8, 99, 75.7, 28.1, 3.141593])\n",
    "x3_domain = [0, 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def park1_3(x):\n",
    "  \"\"\" Computes the park1 function. \"\"\"\n",
    "  return park1_3_z_x([1.0, 1.0, 1.0], x)\n",
    "\n",
    "def park1_3_z_x(z, x):\n",
    "  \"\"\" Computes the park1 function. \"\"\"\n",
    "  x1 = max(x[0], 0.01) * np.sqrt(z[0])\n",
    "  x2 = x[1] * np.sqrt(z[1])\n",
    "  x3 = x[2]/100 * np.sqrt(z[2])\n",
    "  x4 = (x[3] - 10)/6.0 * np.sqrt((z[0] + z[1] + z[2]) / 3.0)\n",
    "  ret1 = (x1/2) * (np.sqrt(1 + (x2 + x3**2)*x4/(x1**2 + 0.00001)) - 1)\n",
    "  ret2 = (x1 + 3*x4) * np.exp(1 + np.sin(x3))\n",
    "  return ret1 + ret2\n",
    "\n",
    "# # Write a function like this called obj.\n",
    "# def objective(x):\n",
    "#   \"\"\" Objective. \"\"\"\n",
    "#   return park1_3(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "vars() argument must have __dict__ attribute",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn [9], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m opt \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreport_results_every\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;241m1\u001b[39m}\n\u001b[0;32m----> 2\u001b[0m min_val, min_pt, history \u001b[38;5;241m=\u001b[39m \u001b[43mmaximise_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpark1_3\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdomain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mx3_domain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx3_domain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx3_domain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx3_domain\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_capital\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopt_method\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopt\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/venkat/dragonfly/dragonfly/apis/opt.py:191\u001b[0m, in \u001b[0;36mmaximise_function\u001b[0;34m(func, domain, max_capital, opt_method, worker_manager, num_workers, capital_type, config, options, reporter)\u001b[0m\n\u001b[1;32m    188\u001b[0m   func_caller \u001b[38;5;241m=\u001b[39m CPFunctionCaller(func, domain, raw_func\u001b[38;5;241m=\u001b[39mraw_func,\n\u001b[1;32m    189\u001b[0m                   domain_orderings\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mdomain_orderings, config\u001b[38;5;241m=\u001b[39mconfig)\n\u001b[1;32m    190\u001b[0m \u001b[38;5;66;03m# load options\u001b[39;00m\n\u001b[0;32m--> 191\u001b[0m options \u001b[38;5;241m=\u001b[39m \u001b[43mload_options_for_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mopt_method\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mopt\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdomain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapital_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;66;03m# Create worker manager and function caller\u001b[39;00m\n\u001b[1;32m    193\u001b[0m worker_manager \u001b[38;5;241m=\u001b[39m get_worker_manager_from_type(num_workers\u001b[38;5;241m=\u001b[39mnum_workers,\n\u001b[1;32m    194\u001b[0m                    worker_manager_type\u001b[38;5;241m=\u001b[39mworker_manager, capital_type\u001b[38;5;241m=\u001b[39mcapital_type)\n",
      "File \u001b[0;32m~/Documents/venkat/dragonfly/dragonfly/apis/api_utils.py:138\u001b[0m, in \u001b[0;36mload_options_for_method\u001b[0;34m(method, prob, domain, capital_type, options)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;66;03m# Now look at any options that have been passed in already and add them to opt_options\u001b[39;00m\n\u001b[1;32m    137\u001b[0m options \u001b[38;5;241m=\u001b[39m Namespace() \u001b[38;5;28;01mif\u001b[39;00m options \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m options\n\u001b[0;32m--> 138\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m attr \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mvars\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    139\u001b[0m   \u001b[38;5;28msetattr\u001b[39m(opt_options, attr, \u001b[38;5;28mgetattr\u001b[39m(options, attr))\n\u001b[1;32m    140\u001b[0m \u001b[38;5;66;03m# Also add capital type\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: vars() argument must have __dict__ attribute"
     ]
    }
   ],
   "source": [
    "opt = {\"report_results_every\":1}\n",
    "min_val, min_pt, history = maximise_function(func=park1_3, domain=[x3_domain, x3_domain, x3_domain, x3_domain], max_capital=100, opt_method='bo', options=opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "\n",
    "\"\"\"\n",
    "  Command line tool for Dragonfly.\n",
    "  -- kandasamy@cs.cmu.edu\n",
    "  -- kvysyara@andrew.cmu.edu\n",
    "\n",
    "  Usage:\n",
    "  python dragonfly-script.py --config <config file in .json or .pb format> --options <options file>\n",
    "  See README in main repository for examples.\n",
    "\"\"\"\n",
    "\n",
    "# pylint: disable=relative-import\n",
    "# pylint: disable=invalid-name\n",
    "# pylint: disable=import-error\n",
    "\n",
    "from __future__ import print_function\n",
    "import os\n",
    "# import imp\n",
    "from importlib import import_module\n",
    "import sys\n",
    "# Local\n",
    "\n",
    "from dragonfly import maximise_function, minimise_function, \\\n",
    "                      maximise_multifidelity_function, \\\n",
    "                      minimise_multifidelity_function, \\\n",
    "                      multiobjective_maximise_functions, \\\n",
    "                      multiobjective_minimise_functions\n",
    "from dragonfly.exd.cp_domain_utils import load_config_file\n",
    "from dragonfly.exd.exd_utils import get_unique_list_of_option_args\n",
    "from dragonfly.utils.option_handler import get_option_specs, load_options\n",
    "# Get options\n",
    "from dragonfly.opt.ga_optimiser import ga_opt_args\n",
    "from dragonfly.opt.gp_bandit import get_all_euc_gp_bandit_args, \\\n",
    "                            get_all_cp_gp_bandit_args, get_all_mf_euc_gp_bandit_args, \\\n",
    "                            get_all_mf_cp_gp_bandit_args\n",
    "from dragonfly.opt.random_optimiser import euclidean_random_optimiser_args, \\\n",
    "                                   mf_euclidean_random_optimiser_args, \\\n",
    "                                   cp_random_optimiser_args, mf_cp_random_optimiser_args\n",
    "from dragonfly.opt.multiobjective_gp_bandit import get_all_euc_moo_gp_bandit_args, \\\n",
    "                                                   get_all_cp_moo_gp_bandit_args\n",
    "from dragonfly.opt.random_multiobjective_optimiser import \\\n",
    "              euclidean_random_multiobjective_optimiser_args, \\\n",
    "              cp_random_multiobjective_optimiser_args\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dragonfly_args = [ \\\n",
    "  get_option_specs('config', False, None, 'Path to the json or pb config file. '),\n",
    "  get_option_specs('options', False, None, 'Path to the options file. '),\n",
    "  get_option_specs('max_or_min', False, 'max', 'Whether to maximise or minimise. '),\n",
    "  get_option_specs('max_capital', False, -1.0,\n",
    "    'Maximum capital (available budget) to be used in the experiment. '),\n",
    "  get_option_specs('capital_type', False, 'return_value',\n",
    "    'Maximum capital (available budget) to be used in the experiment. '),\n",
    "  get_option_specs('is_multi_objective', False, 0,\n",
    "    'If True, will treat it as a multiobjective optimisation problem. '),\n",
    "  get_option_specs('opt_method', False, 'bo',\n",
    "    ('Optimisation method. Default is bo. This should be one of bo, ga, ea, direct, ' +\n",
    "     ' pdoo, or rand, but not all methods apply to all problems.')),\n",
    "  get_option_specs('report_progress', False, 'default',\n",
    "    ('How to report progress. Should be one of \"default\" (prints to stdout), ' +\n",
    "     '\"silent\" (no reporting), or a filename (writes to file).')),\n",
    "                 ]\n",
    "\n",
    "\n",
    "def get_command_line_args():\n",
    "  \"\"\" Returns all arguments for the command line. \"\"\"\n",
    "  ret = dragonfly_args + \\\n",
    "        ga_opt_args + \\\n",
    "        euclidean_random_optimiser_args + cp_random_optimiser_args + \\\n",
    "        mf_euclidean_random_optimiser_args + mf_cp_random_optimiser_args + \\\n",
    "        get_all_euc_gp_bandit_args() + get_all_cp_gp_bandit_args() + \\\n",
    "        get_all_mf_euc_gp_bandit_args() + get_all_mf_cp_gp_bandit_args() + \\\n",
    "        euclidean_random_multiobjective_optimiser_args + \\\n",
    "        cp_random_multiobjective_optimiser_args + \\\n",
    "        get_all_euc_moo_gp_bandit_args() + get_all_cp_moo_gp_bandit_args()\n",
    "  return get_unique_list_of_option_args(ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Main function. \"\"\"\n",
    "sys.path.insert(0, os.getcwd())\n",
    "options = load_options(get_command_line_args(), cmd_line=True)\n",
    "# Load domain and objective\n",
    "config = load_config_file(options.config)\n",
    "if hasattr(config, 'fidel_space'):\n",
    "    is_mf = True\n",
    "else:\n",
    "    is_mf = False\n",
    "\n",
    "# Load module\n",
    "expt_dir = os.path.dirname(os.path.abspath(os.path.realpath(options.config)))\n",
    "if not os.path.exists(expt_dir):\n",
    "    raise ValueError(\"Experiment directory does not exist.\")\n",
    "sys.path.append(expt_dir)\n",
    "obj_module = import_module(config.name, expt_dir)\n",
    "sys.path.remove(expt_dir)\n",
    "\n",
    "# Set capital\n",
    "if options.max_capital < 0:\n",
    "    raise ValueError('max_capital (time or number of evaluations) must be positive.')\n",
    "\n",
    "# Call optimiser\n",
    "_print_prefix = 'Maximising' if options.max_or_min == 'max' else 'Minimising'\n",
    "call_to_optimise = {\n",
    "    'single': {'max': maximise_function, 'min': minimise_function},\n",
    "    'single_mf': {'max': maximise_multifidelity_function,\n",
    "                    'min': minimise_multifidelity_function},\n",
    "    'multi': {'max': multiobjective_maximise_functions,\n",
    "                'min': multiobjective_minimise_functions},\n",
    "}\n",
    "if not options.is_multi_objective:\n",
    "    if is_mf:\n",
    "        print('%s multi-fidelity function on\\n Fidelity-Space: %s.\\n Domain: %s.\\n' % (\n",
    "            _print_prefix, config.fidel_space, config.domain))\n",
    "        opt_val, opt_pt, history = call_to_optimise['single_mf'][options.max_or_min](\n",
    "            obj_module.objective, fidel_space=None, domain=None,\n",
    "            fidel_to_opt=config.fidel_to_opt, fidel_cost_func=obj_module.cost,\n",
    "            max_capital=options.max_capital, capital_type=options.capital_type,\n",
    "            opt_method=options.opt_method, config=config, options=options,\n",
    "            reporter=options.report_progress)\n",
    "    else:\n",
    "        print('%s function on Domain: %s.\\n' % (_print_prefix, config.domain))\n",
    "        opt_val, opt_pt, history = call_to_optimise['single'][options.max_or_min](\n",
    "            obj_module.objective, domain=None, max_capital=options.max_capital,\n",
    "            capital_type=options.capital_type, opt_method=options.opt_method,\n",
    "            config=config, options=options, reporter=options.report_progress)\n",
    "    print('Optimum Value in %d evals: %0.4f' % (len(history.curr_opt_points), opt_val))\n",
    "    print('Optimum Point: %s.' % (opt_pt))\n",
    "else:\n",
    "    if is_mf:\n",
    "        raise ValueError('Multi-objective multi-fidelity optimisation has not been ' +\n",
    "                            'implemented yet.')\n",
    "    else:\n",
    "        # Check format of function caller\n",
    "        if hasattr(obj_module, 'objectives'):\n",
    "            objectives_to_pass = obj_module.objectives\n",
    "            num_objectives = len(objectives_to_pass)\n",
    "        else:\n",
    "            num_objectives = obj_module.num_objectives\n",
    "            objectives_to_pass = (obj_module.compute_objectives, obj_module.num_objectives)\n",
    "        print('%s %d multiobjective functions on Domain: %s.\\n' % (_print_prefix,\n",
    "                                                                    num_objectives, config.domain))\n",
    "        print(objectives_to_pass)\n",
    "        pareto_values, pareto_points, history = \\\n",
    "            call_to_optimise['multi'][options.max_or_min](objectives_to_pass,\n",
    "                                                            domain=None, max_capital=options.max_capital,\n",
    "                                                            capital_type=options.capital_type,\n",
    "                                                            opt_method=options.opt_method, config=config,\n",
    "                                                            options=options,\n",
    "                                                            reporter=options.report_progress)\n",
    "    num_pareto_points = len(pareto_points)\n",
    "    print('Found %d Pareto Points: %s.' % (num_pareto_points, pareto_points))\n",
    "    print('Corresponding Pareto Values: %s.' % (pareto_values))"
   ]
  }
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