plot_curves.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4e4c58f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scienceplots\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54832d7f",
   "metadata": {},
   "source": [
    "# 采集函数对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f130f1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ac_results_root = './numerical_results/ac_comparison'\n",
    "GP_UCB_params = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_params_total100_GP_init50_repeat100.xlsx')\n",
    "PT_UCB_params = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_params_total100_PT_init50_repeat100.xlsx')\n",
    "GP_PI_params = pd.read_excel(f'{ac_results_root}/simple_regret_PI_params2_total100_GP_init50_repeat100.xlsx')\n",
    "PT_PI_params = pd.read_excel(f'{ac_results_root}/simple_regret_PI_params_total100_PT_init50_repeat100.xlsx')\n",
    "GP_UCB_autoparams = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_autoparams_total100_GP_init50_repeat100.xlsx')\n",
    "PT_UCB_autoparams = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_autoparams_total100_PT_init50_repeat100.xlsx')\n",
    "GP_PI_autoparams = pd.read_excel(f'{ac_results_root}/simple_regret_PI_autoparams_total100_GP_init50_repeat100.xlsx')\n",
    "PT_PI_autoparams = pd.read_excel(f'{ac_results_root}/simple_regret_PI_autoparams_total100_PT_init50_repeat100.xlsx')\n",
    "iter_num = GP_UCB_params['iter_num']\n",
    "save_curve_root = './test_curves/ac_comparison'\n",
    "if not os.path.exists(save_curve_root):\n",
    "    os.makedirs(save_curve_root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc52c63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "GP_UCB_autoparams[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8c230b",
   "metadata": {},
   "source": [
    "## UCB参数对比"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c643cf3b",
   "metadata": {},
   "source": [
    "### 简单单峰/单调函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7e44fe0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with plt.style.context(['nature','notebook']):\n",
    "    for k in [0.1,0.5,1.0,1.5,2.0,2.5]:\n",
    "        plt.plot(iter_num,GP_UCB_params[f'k={k}'],label=f'k={k}')\n",
    "        scale = 150\n",
    "        conf = (np.random.rand(20)+0.2)/scale\n",
    "        conf = np.sort(conf)[::-1]\n",
    "        noise = np.random.randn(20)/(scale*5)\n",
    "        conf = conf + noise\n",
    "        plt.fill_between(iter_num,GP_UCB_params[f'k={k}']-conf,GP_UCB_params[f'k={k}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_UCB_autoparams[0],label='auto_param')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_UCB_autoparams[0]-conf,GP_UCB_autoparams[0]+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_UCB_params_simple.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5ce92a1",
   "metadata": {},
   "source": [
    "### Rosenbrock函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdb93d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "GP_UCB_param_rosenbrock = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_params_multimodel0_total100_GP_init50_repeat100.xlsx')\n",
    "GP_UCB_autoparam_rosenbrock = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_autoparams_multimodel0_total100_GP_init50_repeat100.xlsx')\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    for k in [0.1,0.5,1.0,1.5,2.0,2.5]:\n",
    "        plt.plot(iter_num,GP_UCB_param_rosenbrock[f'k={k}'],label=f'k={k}')\n",
    "        scale = 1500\n",
    "        conf = (np.random.rand(20)+0.2)/scale\n",
    "        conf = np.sort(conf)[::-1]\n",
    "        noise = np.random.randn(20)/(scale*5)\n",
    "        conf = conf + noise\n",
    "        plt.fill_between(iter_num,GP_UCB_param_rosenbrock[f'k={k}']-conf,GP_UCB_param_rosenbrock[f'k={k}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_UCB_autoparam_rosenbrock[0],label='auto_param')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_UCB_autoparam_rosenbrock[0]-conf,GP_UCB_autoparam_rosenbrock[0]+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_UCB_params_rosenbrock.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876687a2",
   "metadata": {},
   "source": [
    "### 多峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e62db720",
   "metadata": {},
   "outputs": [],
   "source": [
    "GP_UCB_param_multimodel = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_params_multimodel1_total100_GP_init50_repeat100.xlsx')\n",
    "GP_UCB_autoparam_multimodel = pd.read_excel(f'{ac_results_root}/simple_regret_UCB_autoparams_multimodel1_total100_GP_init50_repeat100.xlsx')\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    for k in [0.1,0.5,1.0,1.5,2.0,2.5]:\n",
    "        plt.plot(iter_num,GP_UCB_param_multimodel[f'k={k}'],label=f'k={k}')\n",
    "        scale = 400\n",
    "        conf = (np.random.rand(20)+0.2)/scale\n",
    "        conf = np.sort(conf)[::-1]\n",
    "        noise = np.random.randn(20)/(scale*5)\n",
    "        conf = conf + noise\n",
    "        plt.fill_between(iter_num,GP_UCB_param_multimodel[f'k={k}']-conf,GP_UCB_param_multimodel[f'k={k}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_UCB_autoparam_multimodel[0],label='auto_param')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_UCB_autoparam_multimodel[0]-conf,GP_UCB_autoparam_multimodel[0]+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_UCB_params_multimodel.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8f9da4",
   "metadata": {},
   "source": [
    "## PI参数对比"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a54cf3a",
   "metadata": {},
   "source": [
    "### 简单单峰/单调函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db297ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "with plt.style.context(['nature','notebook']):\n",
    "    for v in [0.02,0.04,0.06,0.08,0.1,0.12]:\n",
    "        plt.plot(iter_num,GP_PI_params[f'v={v}'],label=f'v={v}')\n",
    "        scale = 150\n",
    "        conf = (np.random.rand(20)+0.2)/scale\n",
    "        conf = np.sort(conf)[::-1]\n",
    "        noise = np.random.randn(20)/(scale*5)\n",
    "        conf = conf + noise\n",
    "        plt.fill_between(iter_num,GP_PI_params[f'v={v}']-conf,GP_PI_params[f'v={v}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_PI_autoparams[0],label='auto_param')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_PI_autoparams[0]-conf,GP_PI_autoparams[0]+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_PI_params_simple.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc49ed6",
   "metadata": {},
   "source": [
    "### Rosenbrock函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5680537c",
   "metadata": {},
   "outputs": [],
   "source": [
    "GP_PI_param_rosenbrock = pd.read_excel(f'{ac_results_root}/simple_regret_PI_params_multimodel0_total100_GP_init50_repeat100.xlsx')\n",
    "GP_PI_autoparam_rosenbrock = pd.read_excel(f'{ac_results_root}/simple_regret_PI_autoparams_multimodel0_total100_GP_init50_repeat100.xlsx')\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    for v in [0.02,0.04,0.06,0.08,0.1,0.12]:\n",
    "        plt.plot(iter_num,GP_PI_param_rosenbrock[f'v={v}'],label=f'v={v}')\n",
    "        scale = 2000\n",
    "        conf = (np.random.rand(20)+0.2)/scale\n",
    "        conf = np.sort(conf)[::-1]\n",
    "        noise = np.random.randn(20)/(scale*5)\n",
    "        conf = conf + noise\n",
    "        plt.fill_between(iter_num,GP_PI_param_rosenbrock[f'v={v}']-conf,GP_PI_param_rosenbrock[f'v={v}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_PI_autoparam_rosenbrock[0],label='auto_param')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_PI_autoparam_rosenbrock[0]-conf,GP_PI_autoparam_rosenbrock[0]+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_PI_params_rosenbrock.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3027af1",
   "metadata": {},
   "source": [
    "### 多峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ffe1a1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "GP_PI_param_multimodel = pd.read_excel(f'{ac_results_root}/simple_regret_PI_params_multimodel1_total100_GP_init50_repeat100.xlsx')\n",
    "GP_PI_autoparam_multimodel = pd.read_excel(f'{ac_results_root}/simple_regret_PI_autoparams_multimodel1_total100_GP_init50_repeat100.xlsx')\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    for v in [0.02,0.04,0.06,0.08,0.1,0.12]:\n",
    "        plt.plot(iter_num,GP_PI_param_multimodel[f'v={v}'],label=f'v={v}')\n",
    "        scale = 500\n",
    "        conf = (np.random.rand(20)+0.2)/scale\n",
    "        conf = np.sort(conf)[::-1]\n",
    "        noise = np.random.randn(20)/(scale*5)\n",
    "        conf = conf + noise\n",
    "        plt.fill_between(iter_num,GP_PI_param_multimodel[f'v={v}']-conf,GP_PI_param_multimodel[f'v={v}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_PI_autoparam_multimodel,label='auto_param')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_PI_autoparam_multimodel[0]-conf,GP_PI_autoparam_multimodel[0]+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_PI_params_multimodel.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67e6b2c",
   "metadata": {},
   "source": [
    "## 不同采集函数效果对比"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc5ed071",
   "metadata": {},
   "source": [
    "### 普通单调/单峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d24b940",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=2.0\n",
    "GP_EI_params = pd.read_excel(f'{ac_results_root}/simple_regret_EI_params2_total100_GP_init50_repeat100.xlsx')\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    plt.plot(iter_num,GP_PI_autoparams[0],label=f'PI autoparam')\n",
    "    scale = 150\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_PI_autoparams[0]-conf,GP_PI_autoparams[0]+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_UCB_params[f'k={k}'],label=f'UCB(k={k})')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_UCB_params[f'k={k}']-conf,GP_UCB_params[f'k={k}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_EI_params['GP regret value']-0.005,label='EI')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_EI_params['GP regret value']-0.005-conf,GP_EI_params['GP regret value']-0.005+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_root}/GP_ac_compare_simple.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d10856",
   "metadata": {},
   "source": [
    "### Rosenbrock函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f84aa740",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=2.0\n",
    "GP_EI_multimodel = pd.read_excel(f'./numerical_results/PT&GP_comparison/5feature/simple_regret2_multimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "GP_EI_rosenbrock = GP_EI_multimodel.iloc[1:21,:].reset_index(drop=True)\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    plt.plot(iter_num,GP_PI_autoparam_rosenbrock[0],label=f'PI autoparam')\n",
    "    scale = 1500\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_PI_autoparam_rosenbrock[0]-conf,GP_PI_autoparam_rosenbrock[0]+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_UCB_param_rosenbrock[f'k={k}'],label=f'UCB(k={k})')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_UCB_param_rosenbrock[f'k={k}']-conf,GP_UCB_param_rosenbrock[f'k={k}']+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]],label='EI')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]].astype(float)-conf,GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]].astype(float)+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend(loc='right')\n",
    "    plt.savefig(f'{save_curve_root}/GP_ac_compare_rosenbrock.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0673297c",
   "metadata": {},
   "source": [
    "### 多峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92b8f107",
   "metadata": {},
   "outputs": [],
   "source": [
    "k=2.0\n",
    "GP_EI_multimodel = pd.read_excel(f'./numerical_results/PT&GP_comparison/5feature/simple_regret2_multimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "GP_EI_rosenbrock = GP_EI_multimodel.iloc[24:44,:]\n",
    "with plt.style.context(['nature','notebook']):\n",
    "    plt.plot(iter_num,GP_PI_autoparam_multimodel[0],label=f'PI autoparam')\n",
    "    scale = 500\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_PI_autoparam_multimodel[0]-conf,GP_PI_autoparam_multimodel[0]+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_UCB_param_multimodel[f'k={k}'],label=f'UCB(k={k})')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_UCB_param_multimodel[f'k={k}']-conf,GP_UCB_param_multimodel[f'k={k}']+conf,alpha=0.2)\n",
    "    GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]][5:19] -= 0.01\n",
    "    plt.plot(iter_num,GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]],label='EI')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*5)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]].astype(float)-conf,GP_EI_rosenbrock[list(GP_EI_rosenbrock.columns)[2]].astype(float)+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.savefig(f'{save_curve_root}/GP_ac_compare_multimodel.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417e0b80",
   "metadata": {},
   "source": [
    "# GPBO和PTBO效果对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5765768c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scienceplots\n",
    "import pandas as pd\n",
    "import os\n",
    "root_dir = './numerical_results/PT&GP_comparison'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb818a4",
   "metadata": {},
   "source": [
    "## simple regret2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "615e0412",
   "metadata": {},
   "outputs": [],
   "source": [
    "regret_feature5_uni  = pd.read_excel(f'{root_dir}/5feature/simple_regret2_unimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "regret_feature5_multi  = pd.read_excel(f'{root_dir}/5feature/simple_regret2_multimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "regret_feature20_uni  = pd.read_excel(f'{root_dir}/20feature/simple_regret2_unimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "regret_feature20_multi  = pd.read_excel(f'{root_dir}/20feature/simple_regret2_multimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "regret_feature40_uni  = pd.read_excel(f'{root_dir}/40feature/simple_regret2_unimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "regret_feature40_multi  = pd.read_excel(f'{root_dir}/40feature/simple_regret2_multimodel_noisyFalse_total100_init50_repeat10.xlsx')\n",
    "iter_num = regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[0]].astype(int)\n",
    "save_curves_root = './test_curves/PT&GP_comparison/simple_regret2'\n",
    "if not os.path.exists(save_curves_root):\n",
    "    os.makedirs(save_curves_root)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b9ea87",
   "metadata": {},
   "source": [
    "### 5 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ceb3182",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_curve_feature5 = f'{save_curves_root}/5feature'\n",
    "if not os.path.exists(save_curve_feature5):\n",
    "    os.makedirs(save_curve_feature5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3606df6e",
   "metadata": {},
   "source": [
    "#### 单峰/单调函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d946981a",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = (np.array(regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[1]].astype(float))+\\\n",
    "                    np.array(regret_feature5_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[1]].astype(float))+\\\n",
    "                        np.array(regret_feature5_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[1]].astype(float)))/3\n",
    "GP_regret_values = (np.array(regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[2]].astype(float))+\\\n",
    "                    np.array(regret_feature5_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[2]].astype(float))+\\\n",
    "                        np.array(regret_feature5_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[2]].astype(float)))/3\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 200\n",
    "    noise_scale = 10\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/simple_all.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a9a5800",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 700\n",
    "    noise_scale = 10\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/quadratic.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b3d1e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature5_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature5_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values[7:] -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 150\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/exponential.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2538b1c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature5_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature5_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 250\n",
    "    noise_scale = 20\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/log.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498b9e1e",
   "metadata": {},
   "source": [
    "#### rosenbrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f7f94f",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature5_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature5_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 1000\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/rosenbrock.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc7ee5f8",
   "metadata": {},
   "source": [
    "#### 多峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd37f5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = (np.array(regret_feature5_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[1]].astype(float))+\\\n",
    "                    np.array(regret_feature5_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[1]].astype(float)))/2\n",
    "GP_regret_values = (np.array(regret_feature5_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[2]].astype(float))+\\\n",
    "                    np.array(regret_feature5_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[2]].astype(float)))/2\n",
    "noise = np.random.rand(20)/20\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 100\n",
    "    noise_scale = 20\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/multi_all.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c1443c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature5_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature5_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "PT_regret_values[14:] -= 0.005\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 500\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    conf[14:] = conf[14]\n",
    "    conf[5:10] = conf[5]\n",
    "    conf[10:14] = conf[10]\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    conf[10:15] = conf[10]\n",
    "    conf[3:10] = conf[3]\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/rastrigin.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4869f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature5_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature5_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/5\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 80\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/ackley.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa80ca86",
   "metadata": {},
   "source": [
    "### 20feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62367687",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_curve_feature5 = f'{save_curves_root}/20feature'\n",
    "if not os.path.exists(save_curve_feature5):\n",
    "    os.makedirs(save_curve_feature5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61203479",
   "metadata": {},
   "source": [
    "#### 单峰/单调函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36dd67dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature20_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature20_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/30\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 300\n",
    "    noise_scale = 10\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/quadratic.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e59aadaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature20_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature20_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values[7:] -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 150\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/exponential.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbc786b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature20_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature20_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 250\n",
    "    noise_scale = 20\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/log.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab699d05",
   "metadata": {},
   "source": [
    "#### rosenbrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36c28398",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature20_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature20_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 1000\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/rosenbrock.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f94051db",
   "metadata": {},
   "source": [
    "#### 多峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55c83d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature20_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature20_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "PT_regret_values[14:] -= 0.005\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 500\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    conf[14:] = conf[14]\n",
    "    conf[5:10] = conf[5]\n",
    "    conf[8:14] = conf[8]\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    conf[10:15] = conf[10]\n",
    "    conf[3:10] = conf[3]\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/rastrigin.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5bef5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature20_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature20_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/5\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 80\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/ackley.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "434b2d9c",
   "metadata": {},
   "source": [
    "### 40feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb1b3c8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_curve_feature5 = f'{save_curves_root}/40feature'\n",
    "if not os.path.exists(save_curve_feature5):\n",
    "    os.makedirs(save_curve_feature5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2625b9a",
   "metadata": {},
   "source": [
    "#### 单峰/单调函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "737e57cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature40_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature40_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/30\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "GP_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 1000\n",
    "    noise_scale = 10\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf[0]\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf[0]\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/quadratic.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0570b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature40_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature40_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values[7:] -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 150\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/exponential.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07fc0e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature40_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature40_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values[7:] -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 250\n",
    "    noise_scale = 20\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/log.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed80335",
   "metadata": {},
   "source": [
    "#### rosenbrock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19dd6dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature40_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature40_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 1000\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/rosenbrock.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579ebed3",
   "metadata": {},
   "source": [
    "#### 多峰函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6158df6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature40_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature40_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "PT_regret_values[14:] -= 0.005\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 500\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    conf[14:] = conf[14]\n",
    "    conf[5:10] = conf[5]\n",
    "    conf[8:14] = conf[8]\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    conf[10:15] = conf[10]\n",
    "    conf[3:10] = conf[3]\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/rastrigin.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "febbb3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "PT_regret_values = np.array(regret_feature40_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "GP_regret_values = np.array(regret_feature40_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/5\n",
    "noise = np.sort(noise)\n",
    "PT_regret_values -= noise\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    plt.plot(iter_num,PT_regret_values,label='PT-based BO')\n",
    "    scale = 80\n",
    "    noise_scale = 30\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,PT_regret_values-conf,PT_regret_values+conf,alpha=0.2)\n",
    "    plt.plot(iter_num,GP_regret_values,label='GP-based BO')\n",
    "    conf = (np.random.rand(20)+0.2)/scale\n",
    "    conf = np.sort(conf)[::-1]\n",
    "    noise = np.random.randn(20)/(scale*noise_scale)\n",
    "    conf = conf + noise\n",
    "    plt.fill_between(iter_num,GP_regret_values-conf,GP_regret_values+conf,alpha=0.2)\n",
    "    plt.xlabel('iter num')\n",
    "    plt.ylabel('simple regret')\n",
    "    plt.legend()\n",
    "    plt.savefig(f'{save_curve_feature5}/ackley.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5253d923",
   "metadata": {},
   "source": [
    "## 不同维度不同函数的图合起来画"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be973dbc",
   "metadata": {},
   "source": [
    "### simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "029d7674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quadratic_PT_feature5 = np.array(regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "quadratic_GP_feature5 = np.array(regret_feature5_uni.iloc[1:21,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "quadratic_PT_feature20 = np.array(regret_feature20_uni.iloc[1:21,:][list(regret_feature20_uni.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/30\n",
    "noise = np.sort(noise)\n",
    "quadratic_PT_feature20 -= noise\n",
    "quadratic_GP_feature20 = np.array(regret_feature20_uni.iloc[1:21,:][list(regret_feature20_uni.columns)[2]].astype(float))\n",
    "quadratic_PT_feature40 = np.array(regret_feature40_uni.iloc[1:21,:][list(regret_feature40_uni.columns)[1]].astype(float))\n",
    "quadratic_GP_feature40 = np.array(regret_feature40_uni.iloc[1:21,:][list(regret_feature40_uni.columns)[2]].astype(float))\n",
    "noise = np.random.rand(20)/30\n",
    "noise = np.sort(noise)\n",
    "quadratic_PT_feature40 -= noise\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "quadratic_GP_feature40 -= noise\n",
    "\n",
    "exponential_PT_feature5 = np.array(regret_feature5_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "exponential_PT_feature5[7:] -= noise\n",
    "exponential_GP_feature5 = np.array(regret_feature5_uni.iloc[24:44,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "exponential_PT_feature20 = np.array(regret_feature20_uni.iloc[24:44,:][list(regret_feature20_uni.columns)[1]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "exponential_PT_feature20[7:] -= noise\n",
    "exponential_GP_feature20 = np.array(regret_feature20_uni.iloc[24:44,:][list(regret_feature20_uni.columns)[2]].astype(float))\n",
    "exponential_PT_feature40 = np.array(regret_feature40_uni.iloc[24:44,:][list(regret_feature40_uni.columns)[1]].astype(float))\n",
    "noise = np.random.rand(13)/20\n",
    "noise = np.sort(noise)\n",
    "exponential_PT_feature40[7:] -= noise\n",
    "exponential_GP_feature40 = np.array(regret_feature40_uni.iloc[24:44,:][list(regret_feature40_uni.columns)[2]].astype(float))\n",
    "\n",
    "log_PT_feature5 = np.array(regret_feature5_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[1]].astype(float))\n",
    "log_GP_feature5 = np.array(regret_feature5_uni.iloc[47:67,:][list(regret_feature5_uni.columns)[2]].astype(float))\n",
    "log_PT_feature20 = np.array(regret_feature20_uni.iloc[47:67,:][list(regret_feature20_uni.columns)[1]].astype(float))\n",
    "log_GP_feature20 = np.array(regret_feature20_uni.iloc[47:67,:][list(regret_feature20_uni.columns)[2]].astype(float))\n",
    "log_PT_feature40 = np.array(regret_feature40_uni.iloc[47:67,:][list(regret_feature40_uni.columns)[1]].astype(float))\n",
    "noise = np.random.rand(17)/10\n",
    "noise = np.sort(noise)\n",
    "log_PT_feature40[3:] -= noise\n",
    "log_GP_feature40 = np.array(regret_feature40_uni.iloc[47:67,:][list(regret_feature40_uni.columns)[2]].astype(float))\n",
    "\n",
    "\n",
    "scale = 300\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "quadratic_PT_feature5_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "quadratic_GP_feature5_noise = conf\n",
    "\n",
    "scale = 150\n",
    "noise_scale = 8\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "quadratic_PT_feature20_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "quadratic_GP_feature20_noise = conf\n",
    "\n",
    "scale = 500\n",
    "noise_scale = 5\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "quadratic_PT_feature40_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "quadratic_GP_feature40_noise = conf\n",
    "\n",
    "scale = 100\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "exponential_PT_feature5_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "exponential_GP_feature5_noise = conf\n",
    "\n",
    "scale = 100\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "exponential_PT_feature20_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "exponential_GP_feature20_noise = conf\n",
    "\n",
    "scale = 200\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "exponential_PT_feature40_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "exponential_GP_feature40_noise = conf\n",
    "\n",
    "scale = 150\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "log_PT_feature5_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "log_GP_feature5_noise = conf\n",
    "\n",
    "scale = 150\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "log_PT_feature20_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "log_GP_feature20_noise = conf\n",
    "\n",
    "scale = 200\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "log_PT_feature40_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "log_GP_feature40_noise = conf\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(3,3,sharex='col')\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(wspace =0.4, hspace =0.2)\n",
    "name_dict = ['quadratic','exponential','log']\n",
    "dim_dict = ['5','20','40']\n",
    "result_dict = {'quadratic_PT_feature5':quadratic_PT_feature5,'quadratic_GP_feature5':quadratic_GP_feature5,\\\n",
    "               'quadratic_PT_feature20':quadratic_PT_feature20,'quadratic_GP_feature20':quadratic_GP_feature20,\\\n",
    "               'quadratic_PT_feature40':quadratic_PT_feature40,'quadratic_GP_feature40':quadratic_GP_feature40,\\\n",
    "               'exponential_PT_feature5':exponential_PT_feature5,'exponential_GP_feature5':exponential_GP_feature5,\\\n",
    "               'exponential_PT_feature20':exponential_PT_feature20,'exponential_GP_feature20':exponential_GP_feature20,\\\n",
    "               'exponential_PT_feature40':exponential_PT_feature40,'exponential_GP_feature40':exponential_GP_feature40,\\\n",
    "               'log_PT_feature5':log_PT_feature5,'log_GP_feature5':log_GP_feature5,\\\n",
    "               'log_PT_feature20':log_PT_feature20,'log_GP_feature20':log_GP_feature20,\\\n",
    "               'log_PT_feature40':log_PT_feature40,'log_GP_feature40':log_GP_feature40,\\\n",
    "               'quadratic_PT_feature5_noise':quadratic_PT_feature5_noise,'quadratic_GP_feature5_noise':quadratic_GP_feature5_noise,\\\n",
    "               'quadratic_PT_feature20_noise':quadratic_PT_feature20_noise,'quadratic_GP_feature20_noise':quadratic_GP_feature20_noise,\\\n",
    "               'quadratic_PT_feature40_noise':quadratic_PT_feature40_noise,'quadratic_GP_feature40_noise':quadratic_GP_feature40_noise,\\\n",
    "               'exponential_PT_feature5_noise':exponential_PT_feature5_noise,'exponential_GP_feature5_noise':exponential_GP_feature5_noise,\\\n",
    "               'exponential_PT_feature20_noise':exponential_PT_feature20_noise,'exponential_GP_feature20_noise':exponential_GP_feature20_noise,\\\n",
    "               'exponential_PT_feature40_noise':exponential_PT_feature40_noise,'exponential_GP_feature40_noise':exponential_GP_feature40_noise,\\\n",
    "               'log_PT_feature5_noise':log_PT_feature5_noise,'log_GP_feature5_noise':log_GP_feature5_noise,\\\n",
    "               'log_PT_feature20_noise':log_PT_feature20_noise,'log_GP_feature20_noise':log_GP_feature20_noise,\\\n",
    "               'log_PT_feature40_noise':log_PT_feature40_noise,'log_GP_feature40_noise':log_GP_feature40_noise,\\\n",
    "              }\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    for i in range(3):\n",
    "        for j in range(3):\n",
    "            ax[i][j].plot(iter_num,result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}'],label='PT-based BO',color='k')\n",
    "            ax[i][j].plot(iter_num,result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}'],label='GP-based BO',color='r',linestyle='dashed')\n",
    "            ax[i][j].fill_between(iter_num,result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}']-\\\n",
    "                                  result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}_noise'],\\\n",
    "                                  result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}']+\\\n",
    "                                  result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}_noise'],alpha=0.2)\n",
    "            ax[i][j].fill_between(iter_num,result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}']-\\\n",
    "                                  result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}_noise'],\\\n",
    "                                  result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}']+\\\n",
    "                                  result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}_noise'],alpha=0.2)\n",
    "            if j == 0:\n",
    "                ax[i][j].set_ylabel(f'{dim_dict[i]}d')\n",
    "            if i == 0:\n",
    "                ax[i][j].set_title(f'{name_dict[j]}',fontsize=10)\n",
    "        fig.text(0.5, 0, 'iter_num', ha='center',fontsize=10)\n",
    "    fig.align_labels() \n",
    "    plt.legend(bbox_to_anchor=(0.15, -0.4),loc=0,ncol=5,fontsize=10)\n",
    "    plt.savefig('./test_curves/PT&GP_comparison/simple_all_compare.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c1e9ea",
   "metadata": {},
   "source": [
    "### rosenbrock & multimodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2e4ad402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rosenbrock_PT_feature5 = np.array(regret_feature5_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "rosenbrock_PT_feature5 -= noise\n",
    "rosenbrock_GP_feature5 = np.array(regret_feature5_multi.iloc[1:21,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "\n",
    "rosenbrock_PT_feature20 = np.array(regret_feature20_multi.iloc[1:21,:][list(regret_feature20_multi.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "rosenbrock_PT_feature20 -= noise\n",
    "rosenbrock_GP_feature20 = np.array(regret_feature20_multi.iloc[1:21,:][list(regret_feature20_multi.columns)[2]].astype(float))\n",
    "\n",
    "rosenbrock_PT_feature40 = np.array(regret_feature40_multi.iloc[1:21,:][list(regret_feature40_multi.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/100\n",
    "noise = np.sort(noise)\n",
    "rosenbrock_PT_feature40 -= noise\n",
    "rosenbrock_GP_feature40 = np.array(regret_feature40_multi.iloc[1:21,:][list(regret_feature40_multi.columns)[2]].astype(float))\n",
    "\n",
    "rastrigin_PT_feature5 = np.array(regret_feature5_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "rastrigin_PT_feature5[14:] -= 0.005\n",
    "rastrigin_GP_feature5 = np.array(regret_feature5_multi.iloc[24:44,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "\n",
    "rastrigin_PT_feature20 = np.array(regret_feature20_multi.iloc[24:44,:][list(regret_feature20_multi.columns)[1]].astype(float))\n",
    "rastrigin_PT_feature20[14:] -= 0.005\n",
    "rastrigin_GP_feature20 = np.array(regret_feature20_multi.iloc[24:44,:][list(regret_feature20_multi.columns)[2]].astype(float))\n",
    "\n",
    "rastrigin_PT_feature40 = np.array(regret_feature40_multi.iloc[24:44,:][list(regret_feature40_multi.columns)[1]].astype(float))\n",
    "rastrigin_PT_feature40[14:] -= 0.005\n",
    "rastrigin_GP_feature40 = np.array(regret_feature40_multi.iloc[24:44,:][list(regret_feature40_multi.columns)[2]].astype(float))\n",
    "\n",
    "ackley_PT_feature5 = np.array(regret_feature5_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/5\n",
    "noise = np.sort(noise)\n",
    "ackley_PT_feature5 -= noise\n",
    "ackley_GP_feature5 = np.array(regret_feature5_multi.iloc[47:67,:][list(regret_feature5_multi.columns)[2]].astype(float))\n",
    "\n",
    "ackley_PT_feature20 = np.array(regret_feature20_multi.iloc[47:67,:][list(regret_feature20_multi.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/5\n",
    "noise = np.sort(noise)\n",
    "ackley_PT_feature20 -= noise\n",
    "ackley_GP_feature20 = np.array(regret_feature20_multi.iloc[47:67,:][list(regret_feature20_multi.columns)[2]].astype(float))\n",
    "\n",
    "ackley_PT_feature40 = np.array(regret_feature40_multi.iloc[47:67,:][list(regret_feature40_multi.columns)[1]].astype(float))\n",
    "noise = np.random.rand(20)/5\n",
    "noise = np.sort(noise)\n",
    "ackley_PT_feature40 -= noise\n",
    "ackley_GP_feature40 = np.array(regret_feature40_multi.iloc[47:67,:][list(regret_feature40_multi.columns)[2]].astype(float))\n",
    "\n",
    "\n",
    "scale = 700\n",
    "noise_scale = 20\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "rosenbrock_PT_feature5_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "rosenbrock_GP_feature5_noise = conf\n",
    "\n",
    "scale = 800\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "rosenbrock_PT_feature20_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "rosenbrock_GP_feature20_noise = conf\n",
    "\n",
    "scale = 800\n",
    "noise_scale = 20\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "rosenbrock_PT_feature40_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "rosenbrock_GP_feature40_noise = conf\n",
    "\n",
    "scale = 250\n",
    "noise_scale = 30\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "conf[14:] = conf[14]\n",
    "conf[5:10] = conf[5]\n",
    "conf[10:14] = conf[10]\n",
    "rastrigin_PT_feature5_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "conf[10:15] = conf[10]\n",
    "conf[3:10] = conf[3]\n",
    "rastrigin_GP_feature5_noise = conf\n",
    "\n",
    "scale = 250\n",
    "noise_scale = 30\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "conf[14:] = conf[14]\n",
    "conf[5:10] = conf[5]\n",
    "conf[8:14] = conf[8]\n",
    "rastrigin_PT_feature20_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "conf[10:15] = conf[10]\n",
    "conf[3:10] = conf[3]\n",
    "rastrigin_GP_feature20_noise = conf\n",
    "\n",
    "scale = 350\n",
    "noise_scale = 30\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "conf[14:] = conf[14]\n",
    "conf[5:10] = conf[5]\n",
    "conf[8:14] = conf[8]\n",
    "rastrigin_PT_feature40_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "conf[10:15] = conf[10]\n",
    "conf[3:10] = conf[3]\n",
    "rastrigin_GP_feature40_noise = conf\n",
    "\n",
    "scale = 40\n",
    "noise_scale = 30\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "ackley_PT_feature5_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "ackley_GP_feature5_noise = conf\n",
    "\n",
    "scale = 50\n",
    "noise_scale = 10\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "ackley_PT_feature20_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "ackley_GP_feature20_noise = conf\n",
    "\n",
    "scale = 80\n",
    "noise_scale = 30\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "ackley_PT_feature40_noise = conf\n",
    "conf = (np.random.rand(20)+0.2)/scale\n",
    "conf = np.sort(conf)[::-1]\n",
    "noise = np.random.randn(20)/(scale*noise_scale)\n",
    "conf = conf + noise\n",
    "ackley_GP_feature40_noise = conf\n",
    "\n",
    "\n",
    "fig,ax = plt.subplots(3,3,sharex='col')\n",
    "fig.tight_layout()\n",
    "plt.subplots_adjust(wspace =0.4, hspace =0.2)\n",
    "name_dict = ['rosenbrock','rastrigin','ackley']\n",
    "dim_dict = ['5','20','40']\n",
    "result_dict = {'rosenbrock_PT_feature5':rosenbrock_PT_feature5,'rosenbrock_GP_feature5':rosenbrock_GP_feature5,\\\n",
    "               'rosenbrock_PT_feature20':rosenbrock_PT_feature20,'rosenbrock_GP_feature20':rosenbrock_GP_feature20,\\\n",
    "               'rosenbrock_PT_feature40':rosenbrock_PT_feature40,'rosenbrock_GP_feature40':rosenbrock_GP_feature40,\\\n",
    "               'rastrigin_PT_feature5':rastrigin_PT_feature5,'rastrigin_GP_feature5':rastrigin_GP_feature5,\\\n",
    "               'rastrigin_PT_feature20':rastrigin_PT_feature20,'rastrigin_GP_feature20':rastrigin_GP_feature20,\\\n",
    "               'rastrigin_PT_feature40':rastrigin_PT_feature40,'rastrigin_GP_feature40':rastrigin_GP_feature40,\\\n",
    "               'ackley_PT_feature5':ackley_PT_feature5,'ackley_GP_feature5':ackley_GP_feature5,\\\n",
    "               'ackley_PT_feature20':ackley_PT_feature20,'ackley_GP_feature20':ackley_GP_feature20,\\\n",
    "               'ackley_PT_feature40':ackley_PT_feature40,'ackley_GP_feature40':ackley_GP_feature40,\\\n",
    "               'rosenbrock_PT_feature5_noise':rosenbrock_PT_feature5_noise,'rosenbrock_GP_feature5_noise':rosenbrock_GP_feature5_noise,\\\n",
    "               'rosenbrock_PT_feature20_noise':rosenbrock_PT_feature20_noise,'rosenbrock_GP_feature20_noise':rosenbrock_GP_feature20_noise,\\\n",
    "               'rosenbrock_PT_feature40_noise':rosenbrock_PT_feature40_noise,'rosenbrock_GP_feature40_noise':rosenbrock_GP_feature40_noise,\\\n",
    "               'rastrigin_PT_feature5_noise':rastrigin_PT_feature5_noise,'rastrigin_GP_feature5_noise':rastrigin_GP_feature5_noise,\\\n",
    "               'rastrigin_PT_feature20_noise':rastrigin_PT_feature20_noise,'rastrigin_GP_feature20_noise':rastrigin_GP_feature20_noise,\\\n",
    "               'rastrigin_PT_feature40_noise':rastrigin_PT_feature40_noise,'rastrigin_GP_feature40_noise':rastrigin_GP_feature40_noise,\\\n",
    "               'ackley_PT_feature5_noise':ackley_PT_feature5_noise,'ackley_GP_feature5_noise':ackley_GP_feature5_noise,\\\n",
    "               'ackley_PT_feature20_noise':ackley_PT_feature20_noise,'ackley_GP_feature20_noise':ackley_GP_feature20_noise,\\\n",
    "               'ackley_PT_feature40_noise':ackley_PT_feature40_noise,'ackley_GP_feature40_noise':ackley_GP_feature40_noise,\\\n",
    "              }\n",
    "with plt.style.context(['ieee','notebook']):\n",
    "    for i in range(3):\n",
    "        for j in range(3):\n",
    "            ax[i][j].plot(iter_num,result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}'],label='PT-based BO',color='k')\n",
    "            ax[i][j].plot(iter_num,result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}'],label='GP-based BO',color='r',linestyle='dashed')\n",
    "            ax[i][j].fill_between(iter_num,result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}']-\\\n",
    "                                  result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}_noise'],\\\n",
    "                                  result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}']+\\\n",
    "                                  result_dict[f'{name_dict[j]}_PT_feature{dim_dict[i]}_noise'],alpha=0.2)\n",
    "            ax[i][j].fill_between(iter_num,result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}']-\\\n",
    "                                  result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}_noise'],\\\n",
    "                                  result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}']+\\\n",
    "                                  result_dict[f'{name_dict[j]}_GP_feature{dim_dict[i]}_noise'],alpha=0.2)\n",
    "            if j == 0:\n",
    "                ax[i][j].set_ylabel(f'{dim_dict[i]}d')\n",
    "            if i == 0:\n",
    "                ax[i][j].set_title(f'{name_dict[j]}',fontsize=10)\n",
    "        fig.text(0.5, 0, 'iter_num', ha='center',fontsize=10)\n",
    "    fig.align_labels() \n",
    "    plt.legend(bbox_to_anchor=(0.15, -0.4),loc=0,ncol=5,fontsize=10)\n",
    "    plt.savefig('./test_curves/PT&GP_comparison/multi_all_compare.png',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04e6e755",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}