demo_BOIL_A2C_Reacher.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1118 09:51:59.859382  9296 deprecation.py:323] From C:\\Users\\Vu\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import bayes_opt.visualization.visualization_utility_cost as viz\n",
    "from bayes_opt.test_functions import drl_experiments\n",
    "from bayes_opt.utility.basic_utility_functions import evaluating_final_util_MaxEpoch,set_seed\n",
    "from bayes_opt.curve_compression import return_logistic_curve\n",
    "\n",
    "from bayes_opt.sequentialBO.boil import BOIL\n",
    "\n",
    "from tqdm import tqdm\n",
    "import warnings\n",
    "\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "set_seed(1337)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specifying the search space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SearchSpace = OrderedDict([('gamma',(0.8,1)),(\"lr_actor\", (1e-6,1e-2)),(\"lr_critic\", (1e-6,1e-2)),\n",
    "                      ('maxEpisodes',(200, 500))])\n",
    "\n",
    "varParams = list(SearchSpace.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# These are the fixed hyperparamters for DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixedParams = OrderedDict([\n",
    "        (\"lr_actor\", 5e-4),\n",
    "        (\"lr_critic\", 5e-4),\n",
    "        (\"ent_coef\", 0.01),\n",
    "        (\"maxSteps\", 200),\n",
    "        (\"val_architecture\", [32,32]),\n",
    "        (\"pol_architecture\", [32,32])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializing the Advantage Actor Critic (A2C) on Reacher-v2 environment (require Mujoco license)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "myfunction=drl_experiments.DRL_experiment(\"A2C\", \"Reacher-v2\", varParams, fixedParams, SearchSpace)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializing BOIL class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "acq_name='ei_mu_max'\n",
    "\n",
    "func_params={}\n",
    "func_params['function']=myfunction\n",
    "\n",
    "boil=BOIL(myfunction.func,myfunction.bounds,acq_name=acq_name,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializing BOIL with 10 points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initialization 10 points - please wait...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W1118 09:52:02.270612  9296 deprecation.py:506] From C:\\Users\\Vu\\Anaconda3\\envs\\tfgpu\\lib\\site-packages\\tensorflow\\python\\ops\\linalg\\linear_operator_diag.py:166: calling LinearOperator.__init__ (from tensorflow.python.ops.linalg.linear_operator) with graph_parents is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Do not pass `graph_parents`.  They will  no longer be used.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max #augmented points 10 \t #augmented points  8 \t Augmented points:  [0.    0.106 0.245 0.365 0.5   0.654 0.783 0.905]\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n"
     ]
    }
   ],
   "source": [
    "n_init_points=10\n",
    "print(\"initialization {} points - please wait...\".format(n_init_points))\n",
    "boil.init(n_init_points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running BOIL over multiple iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running BOIL for 40 iterations\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                           | 0/40 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "selected point from acq func: [0.8662 0.917  0.0705 0.1463] acq val=log(Utility/Cost)= -0.1973\n",
      "Max #augmented points 1 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.9732 0.0092 0.0007] t=244 current y=60.18, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  2%|██                                                                              | 1/40 [03:16<2:07:34, 196.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "selected point from acq func: [0.571 1.    0.    0.   ] acq val=log(Utility/Cost)= -0.4733\n",
      "[original scale] x=[0.9142 0.01   0.    ] t=200 current y=34.48, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  5%|████                                                                            | 2/40 [05:56<1:57:26, 185.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "selected point from acq func: [1. 0. 0. 0.] acq val=log(Utility/Cost)= -0.6639\n",
      "[original scale] x=[1. 0. 0.] t=200 current y=30.76, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  8%|██████                                                                          | 3/40 [08:36<1:49:42, 177.91s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "selected point from acq func: [0. 1. 0. 0.] acq val=log(Utility/Cost)= -0.7294\n",
      "[original scale] x=[0.8  0.01 0.  ] t=200 current y=37.94, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 10%|████████                                                                        | 4/40 [11:17<1:43:35, 172.65s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "selected point from acq func: [1.     0.6312 0.     0.    ] acq val=log(Utility/Cost)= -1.4418\n",
      "[original scale] x=[1.     0.0063 0.    ] t=200 current y=31.05, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 12%|██████████                                                                      | 5/40 [13:57<1:38:35, 169.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "selected point from acq func: [0.2794 1.     0.1625 0.    ] acq val=log(Utility/Cost)= -1.6563\n",
      "[original scale] x=[0.8559 0.01   0.0016] t=200 current y=36.55, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 15%|████████████                                                                    | 6/40 [16:39<1:34:29, 166.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 9.2\n",
      "==estimated lengthscale_x=0.3000 lengthscale_t=0.600 Logistic midpoint=1.3 Logistic growth=0.8\n",
      "selected point from acq func: [0.     1.     0.     0.6213] acq val=log(Utility/Cost)= -1.2254\n",
      "Max #augmented points 6 \t #augmented points  4 \t Augmented points:  [0.    0.153 0.296 0.47 ]\n",
      "[original scale] x=[0.8  0.01 0.  ] t=341 current y=145.01, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 18%|██████████████                                                                  | 7/40 [21:57<1:56:38, 212.09s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.0\n",
      "selected point from acq func: [0.0516 0.438  0.2536 1.    ] acq val=log(Utility/Cost)= -1.6142\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8103 0.0044 0.0025] t=500 current y=325.13, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 20%|████████████████                                                                | 8/40 [28:33<2:22:39, 267.48s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.0\n",
      "selected point from acq func: [0.3484 0.113  0.4265 0.8413] acq val=log(Utility/Cost)= -1.8736\n",
      "Max #augmented points 8 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8697 0.0011 0.0043] t=452 current y=252.38, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 22%|██████████████████                                                              | 9/40 [34:34<2:32:36, 295.36s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.0\n",
      "selected point from acq func: [0.     0.     0.     0.6744] acq val=log(Utility/Cost)= -1.9319\n",
      "Max #augmented points 6 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8 0.  0. ] t=402 current y=219.85, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 25%|███████████████████▊                                                           | 10/40 [39:54<2:31:24, 302.82s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.0\n",
      "==estimated lengthscale_x=0.3000 lengthscale_t=0.600 Logistic midpoint=1.1 Logistic growth=0.8\n",
      "selected point from acq func: [0. 0. 0. 1.] acq val=log(Utility/Cost)= -1.779\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8 0.  0. ] t=500 current y=329.46, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 28%|█████████████████████▋                                                         | 11/40 [46:39<2:41:12, 333.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.0\n",
      "selected point from acq func: [0. 1. 1. 0.] acq val=log(Utility/Cost)= -2.2825\n",
      "[original scale] x=[0.8  0.01 0.01] t=200 current y=30.87, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 30%|███████████████████████▋                                                       | 12/40 [49:19<2:11:24, 281.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.0\n",
      "selected point from acq func: [0.3378 1.     0.1959 1.    ] acq val=log(Utility/Cost)= -2.3475\n",
      "Max #augmented points 10 \t #augmented points  5 \t Augmented points:  [0.    0.211 0.5   0.774 1.   ]\n",
      "[original scale] x=[0.8676 0.01   0.002 ] t=500 current y=313.28, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 32%|█████████████████████████▋                                                     | 13/40 [55:58<2:22:31, 316.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "==estimated lengthscale_x=0.3000 lengthscale_t=0.600 Logistic midpoint=0.6 Logistic growth=0.8\n",
      "selected point from acq func: [0.5389 1.     0.5602 0.    ] acq val=log(Utility/Cost)= -0.3017\n",
      "[original scale] x=[0.9078 0.01   0.0056] t=200 current y=35.35, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 35%|███████████████████████████▋                                                   | 14/40 [58:46<1:57:56, 272.16s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [0.3027 0.     0.     1.    ] acq val=log(Utility/Cost)= -2.424\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8605 0.     0.    ] t=500 current y=343.05, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 38%|████████████████████████████▉                                                | 15/40 [1:05:20<2:08:36, 308.67s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [0.7158 0.6143 0.3951 1.    ] acq val=log(Utility/Cost)= -2.6739\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.9432 0.0061 0.004 ] t=500 current y=304.03, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 40%|██████████████████████████████▊                                              | 16/40 [1:11:56<2:13:53, 334.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [1.     0.3313 1.     0.    ] acq val=log(Utility/Cost)= -2.6504\n",
      "[original scale] x=[1.     0.0033 0.01  ] t=200 current y=30.52, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 42%|████████████████████████████████▋                                            | 17/40 [1:14:37<1:48:18, 282.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [0.5551 0.6868 0.8878 1.    ] acq val=log(Utility/Cost)= -2.718\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.911  0.0069 0.0089] t=500 current y=295.51, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 45%|██████████████████████████████████▋                                          | 18/40 [1:21:13<1:56:07, 316.72s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [0.2481 0.6917 0.2723 1.    ] acq val=log(Utility/Cost)= -2.723\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8496 0.0069 0.0027] t=500 current y=305.34, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 48%|████████████████████████████████████▌                                        | 19/40 [1:27:48<1:59:04, 340.19s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [0.4968 0.3985 0.6761 1.    ] acq val=log(Utility/Cost)= -2.1877\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8994 0.004  0.0068] t=500 current y=325.18, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 50%|██████████████████████████████████████▌                                      | 20/40 [1:34:24<1:58:58, 356.94s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.2\n",
      "selected point from acq func: [0.2216 0.2486 0.     1.    ] acq val=log(Utility/Cost)= -2.4337\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8443 0.0025 0.    ] t=500 current y=305.53, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 52%|████████████████████████████████████████▍                                    | 21/40 [1:40:59<1:56:39, 368.39s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "==estimated lengthscale_x=0.3000 lengthscale_t=0.600 Logistic midpoint=0.9 Logistic growth=1.0\n",
      "selected point from acq func: [0.     1.     0.0872 1.    ] acq val=log(Utility/Cost)= -2.3766\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8    0.01   0.0009] t=500 current y=340.66, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 55%|██████████████████████████████████████████▎                                  | 22/40 [1:47:45<1:53:54, 379.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [1.     1.     0.8894 1.    ] acq val=log(Utility/Cost)= -2.5505\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[1.     0.01   0.0089] t=500 current y=278.88, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 57%|████████████████████████████████████████████▎                                | 23/40 [1:54:20<1:48:50, 384.15s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.4224 0.3318 0.3251 1.    ] acq val=log(Utility/Cost)= -2.7024\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8845 0.0033 0.0033] t=500 current y=326.57, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 60%|██████████████████████████████████████████████▏                              | 24/40 [2:00:54<1:43:16, 387.28s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.     0.1843 0.511  1.    ] acq val=log(Utility/Cost)= -1.7932\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8    0.0018 0.0051] t=500 current y=328.20, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 62%|████████████████████████████████████████████████▏                            | 25/40 [2:07:29<1:37:21, 389.42s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0. 0. 1. 0.] acq val=log(Utility/Cost)= -2.3391\n",
      "[original scale] x=[0.8  0.   0.01] t=200 current y=20.78, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 65%|██████████████████████████████████████████████████                           | 26/40 [2:10:09<1:14:47, 320.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.2726 0.4427 0.6335 1.    ] acq val=log(Utility/Cost)= -2.9916\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8545 0.0044 0.0063] t=500 current y=341.56, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 68%|███████████████████████████████████████████████████▉                         | 27/40 [2:16:43<1:14:16, 342.79s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [1.     0.6037 1.     1.    ] acq val=log(Utility/Cost)= -3.1332\n",
      "Max #augmented points 10 \t #augmented points  6 \t Augmented points:  [0.    0.158 0.356 0.513 0.718 0.842]\n",
      "[original scale] x=[1.    0.006 0.01 ] t=453 current y=243.98, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 70%|█████████████████████████████████████████████████████▉                       | 28/40 [2:23:14<1:11:25, 357.10s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.     0.     0.2534 1.    ] acq val=log(Utility/Cost)= -2.6818\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8    0.     0.0025] t=500 current y=340.50, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 72%|███████████████████████████████████████████████████████▊                     | 29/40 [2:29:48<1:07:31, 368.35s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [1. 0. 1. 1.] acq val=log(Utility/Cost)= -3.5166\n",
      "Max #augmented points 10 \t #augmented points  7 \t Augmented points:  [0.    0.153 0.339 0.505 0.713 0.842 1.   ]\n",
      "[original scale] x=[1.   0.   0.01] t=500 current y=275.22, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 75%|█████████████████████████████████████████████████████████▊                   | 30/40 [2:36:23<1:02:42, 376.25s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.4224 0.4942 0.5893 0.7394] acq val=log(Utility/Cost)= -3.77\n",
      "Max #augmented points 7 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8845 0.0049 0.0059] t=422 current y=257.42, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 78%|█████████████████████████████████████████████████████████████▏                 | 31/40 [2:41:56<54:29, 363.31s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.     0.79   0.0005 1.    ] acq val=log(Utility/Cost)= -3.018\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8    0.0079 0.    ] t=500 current y=334.95, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 80%|███████████████████████████████████████████████████████████████▏               | 32/40 [2:48:31<49:42, 372.76s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "==estimated lengthscale_x=0.3000 lengthscale_t=0.600 Logistic midpoint=0.9 Logistic growth=1.0\n",
      "selected point from acq func: [0.5382 1.     0.311  0.    ] acq val=log(Utility/Cost)= -0.1731\n",
      "[original scale] x=[0.9076 0.01   0.0031] t=200 current y=17.88, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 82%|█████████████████████████████████████████████████████████████████▏             | 33/40 [2:51:29<36:39, 314.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.3\n",
      "selected point from acq func: [0.2111 0.317  0.4184 1.    ] acq val=log(Utility/Cost)= -3.5169\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8422 0.0032 0.0042] t=500 current y=318.99, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 85%|███████████████████████████████████████████████████████████████████▏           | 34/40 [2:58:03<33:50, 338.40s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.4\n",
      "selected point from acq func: [0.     0.4192 1.     1.    ] acq val=log(Utility/Cost)= -2.5139\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8    0.0042 0.01  ] t=500 current y=339.46, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 88%|█████████████████████████████████████████████████████████████████████▏         | 35/40 [3:04:38<29:36, 355.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.4\n",
      "selected point from acq func: [0.     0.0682 1.     1.    ] acq val=log(Utility/Cost)= -2.9841\n",
      "Max #augmented points 10 \t #augmented points  5 \t Augmented points:  [0.    0.202 0.49  0.768 1.   ]\n",
      "[original scale] x=[0.8    0.0007 0.01  ] t=500 current y=335.23, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 90%|███████████████████████████████████████████████████████████████████████        | 36/40 [3:11:13<24:29, 367.26s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.4\n",
      "selected point from acq func: [0.4774 0.5712 0.6082 1.    ] acq val=log(Utility/Cost)= -3.2675\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8955 0.0057 0.0061] t=500 current y=339.75, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 92%|█████████████████████████████████████████████████████████████████████████      | 37/40 [3:17:48<18:46, 375.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.4\n",
      "selected point from acq func: [0.238  0.3731 1.     1.    ] acq val=log(Utility/Cost)= -2.6082\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8476 0.0037 0.01  ] t=500 current y=340.55, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 95%|███████████████████████████████████████████████████████████████████████████    | 38/40 [3:24:20<12:40, 380.45s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.4\n",
      "selected point from acq func: [0.     0.281  0.8642 1.    ] acq val=log(Utility/Cost)= -2.909\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8    0.0028 0.0086] t=500 current y=320.27, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      " 98%|█████████████████████████████████████████████████████████████████████████████  | 39/40 [3:30:55<06:24, 384.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ln of conditioning number of GP covariance matrix 10.4\n",
      "selected point from acq func: [0.1917 0.6357 1.     1.    ] acq val=log(Utility/Cost)= -2.3863\n",
      "Max #augmented points 10 \t #augmented points  0 \t Augmented points:  []\n",
      "[original scale] x=[0.8383 0.0064 0.01  ] t=500 current y=330.97, ybest=356.07\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 40/40 [3:37:29<00:00, 387.51s/it]\n"
     ]
    }
   ],
   "source": [
    "NN=10*myfunction.input_dim\n",
    "print(\"Running BOIL for {} iterations\".format(NN))\n",
    "strFolderOut=\"run_experiments\\plot\"\n",
    "\n",
    "for index in tqdm(range(0,NN)):\n",
    "    boil.suggest_nextpoint()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We evaluate the final performance score util MaxEpoch. The final utility score is marginalizing with different choices for the Logisic curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████████████| 49/49 [00:00<?, ?it/s]\n"
     ]
    },
    {
     "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": [
    "boil.Y_original=evaluating_final_util_MaxEpoch(boil)\n",
    "viz.show_optimization_progress(boil)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the training curve of the best found hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best found hyperparameter = [1.       0.000001 0.000001]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training curve of best found hyperparameter')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select the optimal hyperparameter\n",
    "idxMax=np.argmax(boil.Y_original)\n",
    "print(\"Best found hyperparameter =\",boil.X_original[idxMax])\n",
    "\n",
    "# play the best parameter to Max Iteration\n",
    "T_max=boil.SearchSpace[-1,1]\n",
    "\n",
    "curve,time=myfunction.func([boil.X_original[idxMax,:-1],T_max])\n",
    "curve=curve[0]\n",
    "average100_curve=[np.mean( curve[max(0,n-100):n+1] )     for n in range(len(curve)) ]\n",
    "\n",
    "# plot the found curve\n",
    "fig=plt.figure()\n",
    "plt.plot(average100_curve)\n",
    "plt.xlabel('Episode',fontsize=16)\n",
    "plt.ylabel('Average Reward',fontsize=16)\n",
    "plt.title(\"Training curve of best found hyperparameter\",fontsize=18)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Play the Reacher environment using the best found hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.      , 0.000001, 0.000001])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boil.X_original[idxMax]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Conditioning number of the GP covariance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Log of Cond Number of Cov Matrix')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mjPYEAxvSnGCtYp71BS6zLjIlWEnlF+E062TJ2q71Vw/RsqEuaw/2RUlXpgtem5XlIVpmXWVNsP9CsnX3HyU9I+nTkg7MMS4roG4JdqpHENjQlinBRsT8iDiJZB2Cq4G3AYsl/VHShTnGZwWxs7mNVZt2vvy4TjBnkhOsDW39usgVESsj4oqIOBKYB4wg2V3Whril67d12bRw1sRRjBjm0r0Nbf3e9FDSa4H3kiyO3YjXajUoM8HA9VezTAlW0l+SJNX3ADOA24GLgZ9ExM7efteGBq+iZdZd1h7sY8ADwNeABRGxLr+QrIg8Btasu6wJ9i8iYnGukVihlfZgPQbWLGOC7Uiukl4HHEayhOETEfG7HGOzgmhrD55b7xqsWamsNdgZwE+Bo4HV6eH9JC0E3hoRq3v8Zdvrrd60k12tu7eJmTi6kQmjGwcxIrPakHWY1pVAK3BQRMyKiFnAwUBb+pwNYd3WgPUFLjMgew32FOCkiHiu40BELJV0MXBHLpFZYXibGLPyBrqaVnvfp9jezmsQmJWXNcHeAVwpaVbHAUmzgf/EPdghr3uJwAnWDLIn2IuBUcBSSc9LWgYsSY9dnFNsVhCls7hcIjBLZB2mtQI4WtIpwCGASIZp3Z5ncFb7NmxvZsP25pcfD2+oY7/x3ibGDPq5FkFE3AbcllMsVkBLS+qvcyePpt7bxJgBfZQIJJ0maZmkcWWeG5c+94b8wrNa5226zXrWVw32o8CXI2Jz6RPpsSuAS/IIzIqh2xAtX+Aye1lfCfYIkpWzevJb4MjKhWNF022ZQvdgzV7WV4KdQu9jXQOYVLlwrGi6j4H1LC6zDn0l2JUkvdieHAGsqlw4ViRNLW2s2LDj5ccSHDDZPVizDn0l2F8An5fUbdyNpFHA59Jz+iTpu5LWSlrU6dhESbdJWpz+nNDD756TnrNY0jlZ2rP8LXtpO+2dtomZMX4kIxu9TYxZh74S7BeAcSQbHF4q6cz0dhnwTPrcFzO2dQ1wasmxy4A7IuJgkhlhl5X+kqSJwL8BrwaOBf6tp0Rs1bVkrZcoNOtNr+NgI2KtpL8Gvk2SSDsGOAbwa+DCiFiTpaGIuEvSnJLDZwInpfevBe4ELi05543AbRGxAUDSbSSJekGWdm3PRAS7WtvZtquV7bta2b6rjfbOuxoCf3rupS6PnWDNuupzokFEPA+cnvYaDyJJsosjYmMF2p8WES+k7bwgaWqZc2YAKzo9Xpkeswy27WplzZYm1mxuYs3WJl7cvIu1W5uSpNncxs7mNrbvamVnS/JzR3Mb29Kfbe3RdwOdHDjVF7jMOss8kytNqA/kGEtPyk0LKvvJl3Q+cD7A7Nmz84ypZu1sbuPWR1bx44UreerFrWzb1Vq1tj0G1qyrfm/bXWFrJE1Pe6/TgbVlzlnJ7jICwEySUkI3ETEfmA8wb968/nW/Cu7Ztdv44Z+e56YHV7KlqXpJtcM+wxs4fEa3CX9mQ9pgJ9hbgXOAy9Oft5Q559fAFztd2HoD8MnqhFfbWtva+c0Ta/jBfc9z75KX+v6Ffmqsr2P08HpGD29gVGM9DXW7r4mq0/eKiaMbufCkgxg9fLD/OZnVlqp9IiQtIOmJTpa0kmRkwOXADZI+DCwH3pGeOw+4ICLOi4gNkj7P7vLE5zoueA1lLW3tvP/qP3Hf0t7fisb6OqaOHc6+Y0cwLb1NHTuccSOHMaqxntGNSfIclSbRUY317DO8gVGNDTQ2DHQ9drOhrccEK+m3wFkRsUnSB4AfRcSuPW0oIt7Tw1Mnlzl3IXBep8ffBb67p23vjf60dEOPyXXi6EbeMW8m7z5mNnMmjULy6lZmg6G3HuzxJAtqbwK+B/yK8jVSGwSrN+/sduyYORN432v259TD92V4gwf8mw223hLsUyS1z9+RXMl/p6Qt5U6MiO/nEZz1rKmlrcvjd82bxRVv721Ws5lVW28J9h9I9tw6k2RY1OWUHx4VgBNsle1s7ppgx470BSazWtPjpzIi7gWOAZDUDhwQES4R1IgdJQl2ZKMTrFmtyXqZeC6wLs9ArH9KSwQjh7nmalZrsm56+LykaZIuAg4jKQs8AXwr61oEVlk7uyVYD6kyqzWZPpWSjgeeBc4GdgJNwHtJVtk6Lr/wrCelNVgvE2hWe7IW7r5CsnrVBRHRDiCpDrgK+Crw1/mEZz3ZUdqDdQ3WrOZk/VQeBZzbkVwBIqJd0n8Af84lMutVU2kP1jVYs5qTtXC3meRCV6m5JBMRrMq612CdYM1qTdYe7PXA1ZI+AdxLcpHrtSRjY73w9SDolmAbfZHLrNZkTbCfIJnN9d1Ov9NCstNBt21eLH+lF7lGuAdrVnOyDtNqBi6R9EngQJJk+2xE7Oj9Ny0vpT3YUb7IZVZz+vWpTBPqYznFYv3QbZiWe7BmNceFu4LyRS6z2ucEW1ClU2VH+CKXWc3xp7KAWtraaWnbvbBZfZ1orPd/SrNa0+enUtIwSV+StH81ArK+lSsPeNcCs9rTZ4KNiBbgQspvn22DoHQWl4domdWmrN8rfw28Ls9ALDtPMjArhqzDtO4g2T7mCOBBYHvnJyPi5koHZj0rXWx71DCPgTWrRVk/md9If15c5rkA/B21ikp7sCO8VKFZTco6k8vfQWtI95W0/J/HrBb5k1lAnmRgVgxZdzSQpAslPS5ph6QD0uOXSXpnviFaqe4XuZxgzWpR1h7sJcC/AvPpOlxrFfDRSgdlveu2o6wvcpnVpKwJ9gLgIxHxn0Brp+MPAX9Z8aisV912lPUwLbOalPWTuT+wqMzxFmBk5cKxLLySllkxZE2wS4Gjyxw/nWT7bqsiX+QyK4b+7Cr7DUmjSGqwx0l6P8lOBx/KKzgrr/uW3a7BmtWirONgvyepAfgiMAr4H5ILXBdHxI9yjM/K6N6DdQ3WrBZl7vpExHeA70iaDNRFxNr8wrLedO/BukRgVov69d1S0oHAoen9JyJiaS5RWa+6TZV1DdasJmWdaDBJ0k+BxcBP09tiSbdImjTQICRdImlROpHhY2WeHyfpZ5IeSc/54EDbLDKPIjArhqzFu/8GDgJOAEaktxOBucB3BhKApMOBjwDHAkcCZ0g6uOS0i4AnIuJI4CTgq5IaB9JukXlHWbNiyJpg30gy0eCeiGhNb/cAf58+NxCHAvdFxI6IaAV+D7y15JwAxihZtn8fYANdJzwMKV4P1qwYsn4y11GyBmxqB/DSAGNYBJyYliFGkYytnVVyzjdIEvFqkm3DL4mI9tIXknS+pIWSFq5bt26AYdWu0hKBa7BmtSlrgv0c8HVJMzoOpPe/mj63xyLiSeAK4DbgV8AjdO+dvhF4GNgPOIpkTO7YMq81PyLmRcS8KVOmDCSsmuaJBmbF0GPxTtJjJF/NO8wFlklalT6eATQBU0lqtHssIq4Grk7b/SKwsuSUDwKXR0QAz0p6DjgEuH8g7RZVaQ/WNViz2tTbJ/PGagUhaWpErJU0GzgLOK7klOXAycDdkqYBf0EyfXdIcg/WrBh6TLAR8dkqxnFTOtyrBbgoIjZKuiCN4yrg88A1aa9awKURsb6K8dWU0tW0Rvgil1lN6vd3S0kjKKndRsSOgQQRESeUOXZVp/urgTcMpI29RUtbOy1tuys3dYLGeidYs1qUdaLB/umkgi0kowm2ltysSsqNgU1Gr5lZrcnag/0ByeSCfwTW0PXil1VR6YaHHqJlVruyJti/Ao5Jh1TZIPIkA7PiyPrpfATYeweWFohHEJgVR9Ye7PnAlZKuJJl51dL5yYhYXunArLzuGx46wZrVqqwJto5kQsFP6Fp/VfrYn/IqKa3Bei1Ys9qVNcFeS7Iewd/hi1yDyiUCs+LImmAPAY6KiGfyDMb61v0ilxOsWa3KepHrfpK1CGyQldZgPUzLrHZl7cF+m2Q1ra+SLBdYepHroUoHZuWVTpMd5R6sWc3KmmAXpD/nl3nOF7mqyNvFmBVH1gTr8kCN8EUus+LIlGAj4vm8A7Fsuu0o6xKBWc3KlGAlndXb8xFxc2XCsb50W2zbPVizmpW1RNDT4tsd42H9Ka+SbjVY92DNalamYVoRUdf5BjQCrwbuJtm+26qkW4nAPVizmrVHSzGl23Y/APwz8K3KhmS9KR2m5YtcZrVroGvdbQIOrEQglk23xV5cIjCrWVkvch1degiYDlwK/LnSQVnPuu9o4ARrVquyXuRaSHJBq3RvkvtIttS2Kim9yOUarFnt2tOJBu3AuohoqnA81gfXYM2KwxMNCsY1WLPi6DXBlqm9luXFXqqnWw12WL93XjezKunr09lT7RV2TzKIDK9jFVJaIhjhTQ/NalZfibGnRV4agPOAS4C1FY3IetTS1k5L2+7NJOoEjfVOsGa1qtcEW672KuktwP8h2WX2U8B/5ROalSq3kpZU7suFmdWCzN0fScdLuhe4DrgVOCgivhoRzblFZ1103/DQlRmzWtZngpV0qKRbgN8DTwGviIhLI2JT7tFZF93343J5wKyW9foJlTQfeCQ978iI+FBErKxKZNaNF9s2K5a+vmOeBzQB+wMLeqr3RcQRFY7LyvB2MWbF0leC/WxVorBMPE3WrFj6GkXgBFtDvNCLWbH4KkmBdL/I5QRrVstqIsFKukTSIkmPS/pYD+ecJOnh9JzfVzvGWuASgVmxDPpASkmHAx8BjgWagV9J+kVELO50zniSnRNOjYjlkqYOTrSDy6MIzIqlFnqwhwL3RcSOiGglGW/71pJzzgZujojlABExJKfndttR1iUCs5pWCwl2EXCipEmSRgGnA7NKznkFMEHSnZIelPSBci8k6XxJCyUtXLduXc5hV597sGbF0mOJoKckVk5EfH9PA4iIJyVdAdwGbCOZ2NBacloD8CrgZGAk8EdJ90XEMyWvNR+YDzBv3rxgL9NtR1n3YM1qWm812G+WPG4EhpHsZgBJ77cF2AXscYIFiIirgasBJH0RKJ0tthJYHxHbge2S7gKOBJ5hCPFEA7Ni6bFEEBFjOm7Au4FHgROAEentBOBhkvrogHRctJI0GzgLWFByyi3ACZIa0jLCq4EnB9pu0bgGa1YsWUcRfAX4UET8sdOxe9IhVdcAPx9gHDdJmkTSI74oIjZKugAgIq5Kywi/Ikny7cB/R8SiAbZZON1KBO7BmtW0rAl2DrC9zPEdwOyBBhERJ5Q5dlXJ4y8DXx5oW0XmDQ/NiiXrKII/AVdKmtFxIL3/NZKtu60KPJPLrFiyJtgPA5OAZZKWSVoGLAOmkkwSsCrotqOse7BmNS3rtt1LJB0BnAIcQrIJ4hPA7RGx1w2HqlXdRhG4B2tW0zJPlU0T6W/Smw0C12DNiiVzgpX0apKB/lMpKS1ExMUVjsvKcA3WrFgyJVhJHwe+BDwLrAY6lwVcIqgS12DNiiVrD/YS4OKI+EaewVjvupUI3IM1q2lZRxGMBX6ZZyDWu5a2dlradn9ZqBM01tfCWj1m1pOsn9AFwKl5BmK9K3eBq6dNKM2sNmQtEawAPivpeJLpqi2dn4yI/6h0YNaVh2iZFU/WBHseyVKCf53eOgvACTZnHkFgVjxZJxrMzTsQ650X2zYrnn5fJZG0j6TReQRjPfNasGbFkznBSrpI0nJgM7BF0vOSLswvNOvMSxWaFU/WiQb/DHySZF3YP6SHTwAulzQ2Ii7PKT5L+SKXWfFkvch1AXB+RHTeaeAOSYuBLwJOsDkr7cF6NwOz2pe1RDAVeKDM8fuBaZULx3pS2oN1icCs9mVNsM9Qfu+ts4GnKxeO9cQraZkVT9YSwWeAGySdCNxDMvb1tcDfAO/IJzTrzAu9mBVPph5sRNxMspPri8AZwJvT+8dGxE/zC886uAZrVjz9WXD7QeB9OcZiveg2TMsJ1qzm9dqDlbS/pPmSxpZ5bpyk/ytpVn7hWYcmlwjMCqevEsH/AnZFxJbSJyJiM7AL+HgegVlXrsGaFU9fCfb1wHW9PH8d8IbKhWM98WIvZsXTV4KdA6zq5fnVwP4Vi8Z65GFaZsXTV4LdDvS2ktbc9BzLmXuwZsXTV4K9Dzinl+c/CPypcuFYT1yDNSuevoZpfRW4XdJm4IqIeBFA0r7AZSTDtk7JN0QDT5U1K6JeE2xE3CnpIuDkUWJ7AAAOoklEQVQ/gYslbSGZxTWOZNuYf4yI3+UfppXWYD3RwKz29TnRICL+r6SfA+8EDgJEsjbBjRGxMuf4LOUarFnxZN0yZhXwtZxjsV54RwOz4un3ljE2OLyjgVnxOMEWQEtbOy1t8fLjOsHwBv+nM6t1NfEplXSJpEWSHpf0sV7OO0ZSm6S3VzO+wVZukoGkQYrGzLLqMcFK+oCk4XkHIOlw4CPAscCRwBmSDi5zXj1wBfDrvGOqNb7AZVZMvfVgvweMBUh7jVNziuFQ4L6I2BERrcDvgbeWOe8fgZuAtTnFUbM8BtasmHpLsOuA49L7Ihn/modFwImSJkkaBZwOdFkCUdIMkqR7VW8vJOl8SQslLVy3bl1O4VZftx6sE6xZIfSWYK8CfiqpjSS5vpj2ZLvdBhJARDxJ8tX/NuBXwCNAa8lpXwcujYhe24qI+RExLyLmTZkyZSBh1ZTSHqwnGZgVQ4/jYCPiM5J+DBwM3ExSJ92URxARcTVwNYCkLwKlExjmAdenF3YmA6dLah0q29V4iJZZMfU1VfZx4HFJnwUWRMSOPIKQNDUi1kqaDZzF7tJERxxzO517DfDzoZJcocwkA/dgzQoh60yuzwJIOgA4jKRk8GRELK1QHDdJmkSyvsFFEbFR0gVp273WXYcC12DNiilTgpU0Bvgu8Dagffdh3QR8OCK2DiSIiDihzLGyiTUizh1IW0XkHqxZMWWdaHAlcATwt8DI9HZyeuzr+YRmHbybgVkxZU2wbwbOi4jfR0RLersTOB94S27RGeASgVlRZU2wI4GXyhzfAIyoXDhWTrfdDFwiMCuErAn2HuDz6UQAACSNBj4L3JtHYLabp8qaFVOmi1zA/0cyCWCVpEdJRhEcSbLh4Rtzis1STV4L1qyQsg7TWpQuwPI+4BCSqbM/AH4YETtzjM9wDdasqLL2YEkT6XdyjMV64BqsWTHVxHqw1rvSYVqeKmtWDE6wBVBaIvBiL2bF4ARbAN7w0KyYnGALoLQG6xKBWTE4wRZAt6myLhGYFULWxV6eo/yOBgE0Ac8CV0fErRWMzVKuwZoVU9Ye7PeAicBikvGvP0jvTwRuBdqAmyW9O48ghzrXYM2KKes42AOAyyPi8s4HJX0COCwizpL0z8ClwPUVjnHI844GZsWUtQd7FnBjmeM3p89BsuNrt+22bWBa2tppadtdnZFgeINL52ZFkPWTugPotih2eqxjG5l6wNNmK6z0AteoYfWke5OZWY3LWiL4T+BbkuYBD5Bc3DoWOBf4fHrOqcDDlQ5wqPNKWmbFlXWxl/+TjiS4GHhPevgp4EMR8aP08beBb1U+xKGtqbm9y2PXX82Koz+LvVxPLxewvKpWPna0tHZ57BEEZsWROcECSHodu3eVfTzdNsZy5A0PzYor60SDGcBPgFcBq9PD+0laCLw1Ilb3+Ms2IF4L1qy4+rOrbBtwUETMiohZJEOy2tLnLCeeJmtWXFlLBKcAJ0XEcx0HImKppIuBO3KJzIAyi227B2tWGAMdsd7e9yk2EJ4ma1ZcWRPsHcCVkmZ1HJA0m2R8rHuwOXKJwKy4sibYi4FRwFJJz0taBixJj12SU2yGL3KZFVnWiQYrgKMlncLuXWWfiIjb8wzOvOGhWZH1axxsRNwG3NbxWNL+wJcj4p2VDswSXknLrLgGepFrPPC2SgRi5TU1e7Fts6Lyunc1zjVYs+Jygq1xO1u6joRzDdasOGoiwUq6RNIiSY9L+liZ598r6dH0dq+kIwcjzsGws7nrYi+uwZoVR68XuST1tYnh2IEGIOlw4CMk68s2A7+S9IuIWNzptOeAv4mIjZJOA+YDrx5o20XgEoFZcfU1iuClDM8/18c5fTkUuC8idgBI+j3wVuBLHSdExL2dzr8PmDnANrv4rzsWs2lnSyVfsmKeXbuty2Nf5DIrjl4TbER8sAoxLAK+IGkSyZYzpwMLezn/w8D/q2QANzy4ghUbirGcrUsEZsXRr3GweYiIJyVdQTK+dhvwCNBa7lxJf0uSYF/bw/PnA+cDzJ49O5d4B9u4kcMGOwQzy6gmLnJFxNURcXREnAhsABaXniPpCOC/gTMjomzpIiLmR8S8iJg3ZcqUfIMeBMfMmcDMCSMHOwwzy2jQe7AAkqZGxNp0AZmzgONKnp9NskX4+yPimUq3/9G/PYitTWU7zTVjypjhvP7Qad5R1qxAaiLBAjelNdgW4KJ0tMAFABFxFfBpYBLJzrYArRExr1KNv+uYvbOcYGaDqyYSbEScUObYVZ3unwecV9WgzMwGqCZqsGZmeyMnWDOznDjBmpnlxAnWzCwnTrBmZjlxgjUzy4kTrJlZTpxgzcxyoogY7BhyIWkd8Hw/f20ysD6HcIoUw2C3XwsxDHb7tRDDYLdfCzH01P7+EZFpsZO9NsHuCUkLKzkFt4gxDHb7tRDDYLdfCzEMdvu1EEMl2neJwMwsJ06wZmY5cYLtav5gB8DgxzDY7cPgxzDY7cPgxzDY7cPgxzDg9l2DNTPLiXuwZmY5cYI1M8uJE2xK0qmSnpb0rKTLBqH9ZZIek/SwpN521a1km9+VtFbSok7HJkq6TdLi9OeEKrf/GUmr0vfhYUmn59V+2t4sSb+T9KSkxyVdkh6vyvvQS/tVex8kjZB0v6RH0hg+mx6fK+lP6XvwI0mNVW7/GknPdXoPjsqj/U5x1Ev6s6Sfp48H/vdHxJC/AfXAEuAAoJFkZ9vDqhzDMmBylds8ETgaWNTp2JeAy9L7lwFXVLn9zwAfr+J7MB04Or0/BngGOKxa70Mv7VftfQAE7JPeHwb8CXgNcAPw7vT4VcA/VLn9a4C3V/Hfwj8B1wE/Tx8P+O93DzZxLPBsRCyNiGbgeuDMQY4pdxFxF8kuvp2dCVyb3r8WeEuV26+qiHghIh5K728FngRmUKX3oZf2qyYS29KHw9JbAK8DbkyP5/ke9NR+1UiaCbyJZOdqlGz+N+C/3wk2MQNY0enxSqr8j5zkH9RvJD0o6fwqt93ZtIh4AZIPPzB1EGL4qKRH0xJCbiWKUpLmAH9F0oOq+vtQ0j5U8X1Ivx4/DKwFbiP5RrcpIjq2W871M1HafkR0vAdfSN+Dr0kanlf7wNeBTwDt6eNJVODvd4JNlNsLu9rj146PiKOB04CLJJ1Y5fZrxbeBA4GjgBeAr1ajUUn7ADcBH4uILdVos4/2q/o+RERbRBwFzCT5RndoudOq1b6kw4FPAocAxwATgUvzaFvSGcDaiHiw8+FyYfb3tZ1gEyuBWZ0ezwRWVzOAiFid/lwL/ITkH/lgWCNpOkD6c201G4+INemHrR34DlV4HyQNI0luP4yIm9PDVXsfyrU/GO9D2u4m4E6SGuh4SR07T1flM9Gp/VPT8klExC7ge+T3HhwPvFnSMpLy4OtIerQD/vudYBMPAAenVw0bgXcDt1arcUmjJY3puA+8AVjU+2/l5lbgnPT+OcAt1Wy8I6ml3krO70Naa7saeDIi/qPTU1V5H3pqv5rvg6Qpksan90cCryepBf8OeHt6Wp7vQbn2n+r0PziR1D9zeQ8i4pMRMTMi5pB89n8bEe+lEn9/ta7Q1foNOJ3kCu4S4F+q3PYBJCMXHgEer1b7wAKSr58tJL34D5PUnu4AFqc/J1a5/f8BHgMeJUly03N+D15L8tXvUeDh9HZ6td6HXtqv2vsAHAH8OW1rEfDpTv8u7weeBX4MDK9y+79N34NFwA9IRxrk/O/hJHaPIhjw3++psmZmOXGJwMwsJ06wZmY5cYI1M8uJE6yZWU6cYM16kC64MljD5Wwv4FEEVhMkXUOy2M0Zne9Xqe05wHPAMRGxsNPxfUiG5rxUjThs79PQ9ylmxZTOwmmLPexFRLIAybY+TzTrgUsEVlMkfYZk1sybJEV6Oyl9boak6yVtTG+/kHRw59+VtEjSuZKWALuA0UrW+r07/Z0Nkn4tqfNc++fSnw+k7d3Z+fU6vX6dpE9JWiFpl5L1e8/s9Pyc9PffpmQN2R2SnpB0Sqdzhkm6UtLq9DVWSLq84m+k1QQnWKs1XyFZh/N2krVSpwP3ShpFMnWxCfgb4DiSWWC3p891mAucDbwDODI9fzTJ3PJjSWbqbAZ+1mkB5Y457qem7Z3VQ2yXAP+bZNGRV5KsGXFzmYWgvwBcmbb/AHB9Wm4AuJhk6uu7gYOBdwFP9/22WBG5RGA1JSK2SdoJ7IqIFzuOS3ofyQpHH+z4yi/p70kWYTmDJClDsmD6+yNiTaeXvalzG5I+CGwhSax/ANalT73Uuc0yPg58JSKuSx9/Ol317OPA+zqd97WI+Fna1j8DHyBZFesPwP4kU7LvTv+O5cC9vb8rVlTuwVpRvIqkd7pV0jZJ20h6ohNIlvXrsLIkuSLpQEnXSVoiaQuwhuTf/uysjUsaC+wH3FPy1B9IdiDo7NFO9ztWYOpYT/YakmT7jKRvSnqTJH8O91LuwVpR1JEshPLuMs913hVhe5nnfwasAv4+/dkKPEHS2+2vchfMSo+1vPxERCSLQSWdmYh4KB21cCrJsnjXAo9IOiWSpQltL+IEa7WomWSftM4eAt4DrI9kzdBMJE0iWTz6ooj4XXrsaLr+229Of5a2+bKI2CJpNcnqV7/t9NRrSZJ1ZpFsDfNj4MfpkLT7gINISge2F3GCtVq0DDhN0l8AL5GUAn5IUuu8RdKnSWqXs0j2zroqIhb38FobgfXARyStINn248skvdgOa4GdwBvTRZebImJzmdf6MvA5SYuBB0nqrieQlC8ykfRPJBfnHibp6Z5NUg9emfU1rDhc+7Fa9B2SBZ8XklyAOj4idpDsQruUpPf3FMnX6wkkSbSs9Gv3u0jWHF0EfBP4FMkQro5zWkmu7p9HUjPtaWHlK0mS7JfS13or8LaIeLgff9tWkpEI95P0yo8CTkv/PtvLeCaXmVlO3IM1M8uJE6yZWU6cYM3McuIEa2aWEydYM7OcOMGameXECdbMLCdOsGZmOXGCNTPLyf8PdAaPKq3scdEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(5,5))\n",
    "plt.plot(np.log(boil.condition_number),linewidth=4)\n",
    "plt.xlabel('Iterations',fontsize=14)\n",
    "plt.ylabel('Log of Cond Number of Cov Matrix',fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the estimated preference function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Estimated Logistic Curve $m_0$=0.9 $g_0$=1.0')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "estimated_curve=return_logistic_curve(midpoint=boil.gp.logistic_hyper['midpoint'], \n",
    "                                      growth=boil.gp.logistic_hyper['growth'],\n",
    "                                      MaxEpoch=SearchSpace['maxEpisodes'][1])\n",
    "\n",
    "fig=plt.figure(figsize=(7,5))\n",
    "plt.plot(estimated_curve,linewidth=4)\n",
    "plt.xlabel('Epochs',fontsize=14)\n",
    "plt.xlabel('Epochs',fontsize=14)\n",
    "\n",
    "strTitle=\"Estimated Logistic Curve $m_0$={:.1f} $g_0$={:.1f}\".format(boil.gp.logistic_hyper['midpoint'],boil.gp.logistic_hyper['growth'])\n",
    "plt.title(strTitle,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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}