analyze_GP2d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65163f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "import util\n",
    "import copy\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from keep_default_optimizer import KeepDefaultOpt\n",
    "from grid_search_optimizer import GridSearchOpt\n",
    "import os\n",
    "from tune_util import get_vacbo_optimizer\n",
    "import pickle \n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb7199bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./result/twod_gp_min_16_30_12-Mar_05_2023.pkl', 'rb') as pickle_file:\n",
    "#with open('./result/discomfort_constrained_energy_min_05_02_09-Nov_26_2022.pkl', 'rb') as pickle_file:\n",
    "    content = pickle.load(pickle_file)\n",
    "\n",
    "opt_obj_list_pdcbo_list, constraints1_list_pdcbo_list, \\\n",
    "evaluated_points_list_pdcbo_list, \\\n",
    "contexts_list_pdcbo_list, \\\n",
    "cond_min_list_pdcbo_list, \\\n",
    "opt_obj_list_cbo_list, \\\n",
    "constraints1_list_cbo_list, evaluated_points_list_cbo_list, contexts_list_cbo_list, \\\n",
    "cond_min_list_cbo_list, \\\n",
    "opt_obj_list_safebo_list, \\\n",
    "constraints1_list_safebo_list, \\\n",
    "evaluated_points_list_safebo_list, \\\n",
    "contexts_list_safebo_list, \\\n",
    "cond_min_list_safebo_list = content\n",
    "\n",
    "dynamic_regret_list_pdcbo_list = [[opt_obj_list_pdcbo_list[traj_id][k] - cond_min_list_pdcbo_list[traj_id][k] for k in range(len(opt_obj_list_pdcbo_list[traj_id]))] \n",
    "                                  for traj_id in range(len(opt_obj_list_pdcbo_list))\n",
    "                                 ]\n",
    "dynamic_regret_list_cbo_list = [[opt_obj_list_cbo_list[traj_id][k] - cond_min_list_cbo_list[traj_id][k] for k in range(len(opt_obj_list_cbo_list[traj_id]))] \n",
    "                                for traj_id in range(len(opt_obj_list_cbo_list))\n",
    "                               ]\n",
    "dynamic_regret_list_safebo_list = [[-opt_obj_list_safebo_list[traj_id][k] - cond_min_list_safebo_list[traj_id][k] for k in range(len(opt_obj_list_safebo_list[traj_id]))]\n",
    "                                  for traj_id in range(len(opt_obj_list_safebo_list))\n",
    "                                  ]\n",
    "\n",
    "cumu_dynamic_regret_list_pdcbo_list = np.cumsum(dynamic_regret_list_pdcbo_list, axis=1)\n",
    "cumu_dynamic_regret_list_cbo_list = np.cumsum(dynamic_regret_list_cbo_list, axis=1)\n",
    "cumu_dynamic_regret_list_safebo_list = np.cumsum(dynamic_regret_list_safebo_list, axis=1)\n",
    "\n",
    "cumu_dynamic_regret_list_pdcbo_list_mean = np.mean(cumu_dynamic_regret_list_pdcbo_list, axis=0)\n",
    "cumu_dynamic_regret_list_cbo_list_mean = np.mean(cumu_dynamic_regret_list_cbo_list, axis=0)\n",
    "cumu_dynamic_regret_list_safebo_list_mean = np.mean(cumu_dynamic_regret_list_safebo_list, axis=0)\n",
    "\n",
    "cumu_dynamic_regret_list_pdcbo_list_std = np.std(cumu_dynamic_regret_list_pdcbo_list, axis=0)\n",
    "cumu_dynamic_regret_list_cbo_list_std = np.std(cumu_dynamic_regret_list_cbo_list, axis=0)\n",
    "cumu_dynamic_regret_list_safebo_list_std = np.std(cumu_dynamic_regret_list_safebo_list, axis=0)\n",
    "\n",
    "cumu_constraints1_list_pdcbo_list = np.cumsum(constraints1_list_pdcbo_list, axis=1)\n",
    "cumu_constraints1_list_cbo_list = np.cumsum(constraints1_list_cbo_list, axis=1)\n",
    "cumu_constraints1_list_safebo_list = np.cumsum(constraints1_list_safebo_list, axis=1)\n",
    "\n",
    "cumu_constraints1_list_pdcbo_list_mean = np.mean(cumu_constraints1_list_pdcbo_list, axis=0)\n",
    "cumu_constraints1_list_cbo_list_mean = np.mean(cumu_constraints1_list_cbo_list, axis=0)\n",
    "cumu_constraints1_list_safebo_list_mean = np.mean(cumu_constraints1_list_safebo_list, axis=0)\n",
    "\n",
    "cumu_constraints1_list_pdcbo_list_std = np.std(cumu_constraints1_list_pdcbo_list, axis=0)\n",
    "cumu_constraints1_list_cbo_list_std = np.std(cumu_constraints1_list_cbo_list, axis=0)\n",
    "cumu_constraints1_list_safebo_list_std = np.std(cumu_constraints1_list_safebo_list, axis=0)\n",
    "\n",
    "\n",
    "\n",
    "dynamic_regret_mean_pdcbo = np.mean(dynamic_regret_list_pdcbo_list, axis=0)\n",
    "dynamic_regret_mean_cbo = np.mean(dynamic_regret_list_cbo_list, axis=0)\n",
    "dynamic_regret_mean_safebo = np.mean(dynamic_regret_list_safebo_list, axis=0)\n",
    "\n",
    "constraints1_list_pdcbo = np.mean(constraints1_list_pdcbo_list, axis=0)\n",
    "constraints1_list_cbo = np.mean(constraints1_list_cbo_list, axis=0)\n",
    "constraints1_list_safebo = np.mean(constraints1_list_safebo_list, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36be30c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 215.065x305.716 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MARKERSIZE = 1.5 * 0.4 * 0.1\n",
    "colors = ['red','purple', 'orange', 'green', 'blue', 'black', 'blue']\n",
    "marksCur = ['o','s', '*', 'v', '^','+']\n",
    "my_color = 'black' #'c'\n",
    "MS_ratio = 0.1\n",
    "from fig_hp import *\n",
    "def plot_with_shade_area(ax, x, y, yerr, **kwargs):\n",
    "    y = np.squeeze(y)\n",
    "    yerr = np.squeeze(yerr)\n",
    "    aver_line, = ax.plot(x, y,markersize=MARKERSIZE*MS_ratio, linewidth=LINEWIDTH, color=kwargs['color'], marker=kwargs['marker'], markerfacecolor='None')\n",
    "    aver_region = ax.fill_between(x, y-yerr, y+yerr, alpha=0.3,color=kwargs['color'])\n",
    "    return aver_line, aver_region\n",
    "\n",
    "if True:\n",
    "    total_steps = 500\n",
    "    step_arr = np.arange(total_steps)\n",
    "    \n",
    "    %matplotlib inline\n",
    "    #from matplotlib import rc\n",
    "    #plt.rc('text',usetex=False)\n",
    "   \n",
    "\n",
    "    \n",
    "    fig, axes = plt.subplots(2, 1)\n",
    "    fig.set_size_inches(WIDTH * 1.0, HEIGHT * 2.3)\n",
    "\n",
    "    fig.subplots_adjust(left=.22, bottom=.20, right=.99, top=.97, hspace=0.33, wspace=0.4) \n",
    "   \n",
    "    ax = axes[0]\n",
    "    BETA1 = 0.5\n",
    "    safe_line, _ = plot_with_shade_area(ax, step_arr, cumu_dynamic_regret_list_safebo_list_mean, BETA1*cumu_dynamic_regret_list_safebo_list_std,\n",
    "                         linewidth=LINEWIDTH, color=colors[0], marker=marksCur[0], markerfacecolor='None', markersize=MARKERSIZE)\n",
    "    cei_line, _ = plot_with_shade_area(ax, step_arr, cumu_dynamic_regret_list_cbo_list_mean, BETA1*cumu_dynamic_regret_list_cbo_list_std,\n",
    "                         linewidth=LINEWIDTH, color=colors[1], marker=marksCur[1], markerfacecolor='None', markersize=MARKERSIZE)\n",
    "    pdcbo_line, _ = plot_with_shade_area(ax, step_arr, cumu_dynamic_regret_list_pdcbo_list_mean, BETA1*cumu_dynamic_regret_list_pdcbo_list_std, \n",
    "                         linewidth=LINEWIDTH, color=colors[2], marker=marksCur[2], markerfacecolor='None', markersize=MARKERSIZE)\n",
    "   \n",
    "    plt.xticks(fontsize=LABELSIZE)\n",
    "    plt.yticks(fontsize=LABELSIZE)\n",
    "    ax.tick_params(axis='x', colors=my_color)\n",
    "    ax.tick_params(axis='y', colors=my_color)\n",
    "    ax.set_xlabel('Step', fontsize=LABELSIZE, color=my_color)\n",
    "    ax.set_ylabel('Cumulative Dynamic Regret', fontsize=LABELSIZE, color=my_color)  \n",
    "    plt.xlim((0, total_steps))\n",
    "\n",
    "    #ax.legend(['CEI', 'Safe BO', 'Primal-Dual', 'LCB2'])\n",
    "    \n",
    "    ax = axes[1]\n",
    "\n",
    "    #phi_line = ax.plot(con_bo_vio, linewidth=LINEWIDTH, color=colors[-1], marker=marksCur[1], markerfacecolor='None', markersize=MARKERSIZE)\n",
    "    BETA2 = 0.5\n",
    "    plot_with_shade_area(ax, step_arr, -cumu_constraints1_list_safebo_list_mean, BETA2*cumu_constraints1_list_safebo_list_std, \n",
    "                         linewidth=LINEWIDTH, color=colors[0], marker=marksCur[0], markerfacecolor='None')\n",
    "    plot_with_shade_area(ax, step_arr, cumu_constraints1_list_cbo_list_mean, BETA2*cumu_constraints1_list_cbo_list_std,\n",
    "                        linewidth=LINEWIDTH, color=colors[1], marker=marksCur[1], markerfacecolor='None')\n",
    "    plot_with_shade_area(ax, step_arr, cumu_constraints1_list_pdcbo_list_mean, BETA2*cumu_constraints1_list_pdcbo_list_std,\n",
    "                        linewidth=LINEWIDTH, color=colors[2], marker=marksCur[2], markerfacecolor='None')\n",
    "    #plot_with_shade_area(ax, step_arr, epbo_vio_1, BETA2*epbo_vio_sd_1,\n",
    "    #                    linewidth=LINEWIDTH, color=colors[4], marker=marksCur[4], markerfacecolor='None')\n",
    "    \n",
    "    plt.xlim((0, 500))\n",
    "    ax.legend([safe_line, cei_line, pdcbo_line], ['Safe BO', 'CEI', 'PDCBO'], loc='lower center', bbox_to_anchor=(0.2, -0.65, 0.5, 0.5),\n",
    "             ncol=3)\n",
    "\n",
    "    plt.xticks(fontsize=LABELSIZE, color=my_color)\n",
    "    plt.yticks(fontsize=LABELSIZE, color=my_color)\n",
    "    ax.set_xlabel('Step', fontsize=LABELSIZE, color=my_color)\n",
    "    ax.set_ylabel('Cumulative Constraint', fontsize=LABELSIZE, color=my_color)\n",
    "    #ax.set_xscale('log')\n",
    "    #ax.set_title('The Second Contextual Variable', fontsize=LABELSIZE)\n",
    "    #ax.set_ylim(0, 0.2)\n",
    "    #lgd = plt.legend(['', ''], loc='upper right', bbox_to_anchor=(1.0, 1.0), ncol=1, prop={'size': LABELSIZE})\n",
    "    #fig.set_size_inches(WIDTH, HEIGHT)\n",
    "\n",
    "    #lgd = fig.legend((aver_line[0], adv_line[0]), ('Average', 'Adversarial'), \n",
    "    #                 loc='upper right', bbox_to_anchor=(0.78, 0.1), ncol=3, prop={'size': LABELSIZE})\n",
    "\n",
    "    import tikzplotlib\n",
    "    tikzplotlib.clean_figure()\n",
    "    tikzplotlib.save('./fig/cumu_dyr_and_constraints.tex')\n",
    "    plt.savefig('./fig/cumu_dyr_and_constraints.pdf', format='pdf', bbox_inches='tight')\n",
    "    # plt.savefig(f'./fig/sample_funcs_r_and_v.pdf', format='pdf', bbox_inches='tight')\n",
    "    \n",
    "    plt.show()  \n",
    "   \n",
    "#plot_cumu_result(con_bo_regret_lists, safe_regret_lists, pdbo_regret_lists, lcb2_regret_lists,\n",
    "#                    con_bo_constrs_lists, safe_constrs_lists, pdbo_constrs_lists, lcb2_constrs_lists,\n",
    "#                epbo_regret_lists_2, epbo_constrs_lists_2, epbo_regret_lists_3, epbo_constrs_lists_3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba9d041f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6172086528693974"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "safe_bo_cumu_reg = cumu_dynamic_regret_list_safebo_list_mean[-1]\n",
    "pdcbo_cumu_reg = cumu_dynamic_regret_list_pdcbo_list_mean[-1]\n",
    "(safe_bo_cumu_reg-pdcbo_cumu_reg)/pdcbo_cumu_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5e783bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8aafd35760>]"
      ]
     },
     "execution_count": 13,
     "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"
    },
    {
     "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": [
    "# plt.plot(np.cumsum(opt_obj_list_pdcbo))\n",
    "plt.plot(np.cumsum(dynamic_regret_mean_pdcbo))\n",
    "#plt.figure()\n",
    "#plt.plot(np.cumsum(constraints1_list_pdcbo)/(np.arange(len(constraints1_list_pdcbo))+1))\n",
    "\n",
    "#plt.figure()\n",
    "#plt.plot(np.cumsum(opt_obj_list_cbo))\n",
    "plt.plot(np.cumsum(dynamic_regret_mean_cbo))\n",
    "#plt.figure()\n",
    "#plt.plot(np.cumsum(constraints1_list_cbo)/(np.arange(len(constraints1_list_cbo))+1))\n",
    "\n",
    "\n",
    "#plt.figure()\n",
    "#plt.plot(-np.cumsum(opt_obj_list_safebo))\n",
    "plt.plot(np.cumsum(dynamic_regret_mean_safebo))\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(np.cumsum(constraints1_list_pdcbo)/(np.arange(len(constraints1_list_pdcbo))+1))\n",
    "plt.plot(np.cumsum(constraints1_list_cbo)/(np.arange(len(constraints1_list_cbo))+1))\n",
    "plt.plot(-np.cumsum(constraints1_list_safebo)/(np.arange(len(constraints1_list_safebo))+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91d80ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4.399999999999999,\n",
       " -8.0,\n",
       " 8.799999999999997,\n",
       " 3.200000000000001,\n",
       " 1.5999999999999996,\n",
       " -2.4000000000000004,\n",
       " 5.199999999999999,\n",
       " -3.5999999999999996,\n",
       " 9.2,\n",
       " -2.8000000000000007,\n",
       " -4.0,\n",
       " 1.200000000000001,\n",
       " 5.199999999999999,\n",
       " 4.0,\n",
       " -1.1999999999999993,\n",
       " -7.6,\n",
       " 5.600000000000001,\n",
       " -7.199999999999999,\n",
       " 8.799999999999997,\n",
       " -0.7999999999999989,\n",
       " 7.600000000000001,\n",
       " 0.8000000000000007,\n",
       " -3.1999999999999993,\n",
       " -9.6,\n",
       " -3.5999999999999996,\n",
       " -8.4,\n",
       " 2.8000000000000007,\n",
       " -1.1999999999999993,\n",
       " -5.6,\n",
       " 3.6000000000000014,\n",
       " -1.5999999999999996,\n",
       " -4.8,\n",
       " -10.0,\n",
       " -4.8,\n",
       " 2.8000000000000007,\n",
       " -5.6,\n",
       " -6.8,\n",
       " -2.8000000000000007,\n",
       " 4.800000000000001,\n",
       " -3.5999999999999996,\n",
       " -4.3999999999999995,\n",
       " -3.5999999999999996,\n",
       " 3.200000000000001,\n",
       " -0.40000000000000036,\n",
       " -4.3999999999999995,\n",
       " -2.8000000000000007,\n",
       " -9.2,\n",
       " -7.199999999999999,\n",
       " -7.199999999999999,\n",
       " 7.600000000000001]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contexts_list_pdcbo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a021480",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start sampling functions.\n",
      "  \u001b[1mrbf.       \u001b[0;0m  |  value  |  constraints  |  priors\n",
      "  \u001b[1mvariance   \u001b[0;0m  |    2.0  |      +ve      |        \n",
      "  \u001b[1mlengthscale\u001b[0;0m  |   (2,)  |      +ve      |         0.01\n",
      "  \u001b[1mrbf.       \u001b[0;0m  |  value  |  constraints  |  priors\n",
      "  \u001b[1mvariance   \u001b[0;0m  |    2.0  |      +ve      |        \n",
      "  \u001b[1mlengthscale\u001b[0;0m  |   (2,)  |      +ve      |         0.01\n",
      "  \u001b[1mrbf.       \u001b[0;0m  |  value  |  constraints  |  priors\n",
      "  \u001b[1mvariance   \u001b[0;0m  |    2.0  |      +ve      |        \n",
      "  \u001b[1mlengthscale\u001b[0;0m  |   (2,)  |      +ve      |         0.01\n",
      "  \u001b[1mrbf.       \u001b[0;0m  |  value  |  constraints  |  priors\n",
      "  \u001b[1mvariance   \u001b[0;0m  |    2.0  |      +ve      |        \n",
      "  \u001b[1mlengthscale\u001b[0;0m  |   (2,)  |      +ve      |         0.01\n",
      "  \u001b[1mrbf.       \u001b[0;0m  |  value  |  constraints  |  priors\n",
      "  \u001b[1mvariance   \u001b[0;0m  |    2.0  |      +ve      |        \n",
      "  \u001b[1mlengthscale\u001b[0;0m  |   (2,)  |      +ve      |         0.01\n",
      "  \u001b[1mrbf.       \u001b[0;0m  |  value  |  constraints  |  priors\n",
      "  \u001b[1mvariance   \u001b[0;0m  |    2.0  |      +ve      |        \n",
      "  \u001b[1mlengthscale\u001b[0;0m  |   (2,)  |      +ve      |         0.01\n",
      "Functions sampled.\n",
      "[0.0, 0.0]\n",
      "2\n",
      "[50, 1]\n"
     ]
    }
   ],
   "source": [
    "config = util.get_sample_GP_two_dim_config('gp2d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dc6e8964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6.0, -2.1553277245148785)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "config['get_context'](step=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "396833f9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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