summarize_table.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/liangzhp/dp-SCO-streaming-code\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import _pickle as cPickle\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import os\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "dpsco-results/p=inf-d=50-T=10000-scale=0.8-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=50-T=2000-scale=0.6-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=5-T=5000-scale=0.1-noise_free=True-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=50-T=2000-scale=0.6-noise_free=False-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=50-T=10000-scale=0.7-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=10-T=10000-scale=0.2-noise_free=True-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=50-T=2000-scale=0.2-noise_free=True-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=5-T=1000-scale=0.4-noise_free=False-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=5-T=1000-scale=0.7-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=50-T=1000-scale=0.6-noise_free=False-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=10-T=1000-scale=0.8-noise_free=True-test_flag=False/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=50-T=2000-scale=0.3-noise_free=True-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=5-T=1000-scale=0.4-noise_free=True-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=20-T=2000-scale=0.7-noise_free=True-test_flag=False/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=50-T=10000-scale=0.2-noise_free=True-test_flag=True/OFW_ple2.pkl\n",
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      "dpsco-results/p=1.5-d=20-T=2000-scale=0.2-noise_free=True-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=20-T=10000-scale=0.4-noise_free=False-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=10-T=5000-scale=0.4-noise_free=True-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=20-T=10000-scale=0.6-noise_free=False-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=10-T=10000-scale=0.6-noise_free=False-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=20-T=1000-scale=1.0-noise_free=False-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=5-T=2000-scale=1.0-noise_free=False-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=20-T=1000-scale=0.8-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=20-T=1000-scale=1.0-noise_free=True-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=5-T=1000-scale=1.0-noise_free=False-test_flag=False/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=5-T=2000-scale=0.8-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=20-T=10000-scale=0.7-noise_free=True-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=10-T=2000-scale=0.7-noise_free=True-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=5-T=10000-scale=1.0-noise_free=False-test_flag=False/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=50-T=10000-scale=0.6-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=10-T=10000-scale=0.6-noise_free=False-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=5-T=5000-scale=0.4-noise_free=False-test_flag=True/OFW_ple2.pkl\n",
      "dpsco-results/p=1.5-d=20-T=2000-scale=0.1-noise_free=True-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=5-T=2000-scale=0.7-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=10-T=10000-scale=0.4-noise_free=True-test_flag=False/OFW_ple2.pkl\n",
      "dpsco-results/p=inf-d=20-T=2000-scale=0.7-noise_free=False-test_flag=False/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=10-T=10000-scale=1.0-noise_free=False-test_flag=True/OFW_pge2.pkl\n",
      "dpsco-results/p=inf-d=5-T=10000-scale=0.7-noise_free=True-test_flag=False/OFW_pge2.pkl\n",
      "dpsco-results/p=1.5-d=5-T=10000-scale=0.6-noise_free=False-test_flag=False/OFW_ple2.pkl\n"
     ]
    }
   ],
   "source": [
    "def paras(x):\n",
    "    return x.split('=')[-1]\n",
    "\n",
    "dfbandit = pd.DataFrame(columns = ['T', 'd', 'algo', 'scale'])\n",
    "dfsco = pd.DataFrame(columns = ['T', 'd', 'p', 'algo', 'scale', 'noise_free', 'test_flag'])\n",
    "repo_paths = ['dpsco-results']\n",
    "for repo_path in repo_paths:\n",
    "    root_path = '{}/*/*.pkl'.format(repo_path)\n",
    "    for item in glob.glob(root_path):\n",
    "        setting, algo = item.split('/')[-2], item.split('/')[-1]\n",
    "        p, d, T, scale, noise_free, test_flag = setting.split('-')\n",
    "        if p=='p=1.0':\n",
    "            with open(item, \"rb\")  as input_file:\n",
    "                dict_list = cPickle.load(input_file)\n",
    "                est_mean = np.mean([logger_dict['result']['record'][-1][1] for logger_dict in dict_list])\n",
    "                est_std = np.std([logger_dict['result']['record'][-1][1] for logger_dict in dict_list])\n",
    "                regret_mean = np.mean([np.sum(record[2] for record in logger_dict['result']['record']) for logger_dict in dict_list])\n",
    "                regret_std = np.std([np.sum(record[2] for record in logger_dict['result']['record']) for logger_dict in dict_list])\n",
    "                time_mean = np.mean([logger_dict['result']['record'][-1][3] for logger_dict in dict_list])\n",
    "                time_std = np.std([logger_dict['result']['record'][-1][3] for logger_dict in dict_list])\n",
    "            dfbandit = dfbandit.append(\n",
    "                {\"T\": paras(T),\n",
    "                \"d\": paras(d),\n",
    "                'algo': algo[:-4],\n",
    "                \"scale\": paras(scale),\n",
    "                \"est_mean\": est_mean,\n",
    "                \"est_std\": est_std,\n",
    "                \"regret_mean\": regret_mean,\n",
    "                \"regret_std\": regret_std,\n",
    "                \"time_mean\": time_mean,\n",
    "                \"time_std\": time_std\n",
    "                }, ignore_index=True)\n",
    "        else:\n",
    "            with open(item, \"rb\")  as input_file:\n",
    "                dict_list = cPickle.load(input_file)\n",
    "                if dict_list[0]['algo']['test_flag']==True:\n",
    "                    risk_mean = np.mean([logger_dict['result']['record'][-1][1] for logger_dict in dict_list])\n",
    "                    risk_std = np.std([logger_dict['result']['record'][-1][1] for logger_dict in dict_list])\n",
    "                    subopt_mean = np.mean([logger_dict['result']['record'][-1][2] for logger_dict in dict_list])\n",
    "                    subopt_std = np.std([logger_dict['result']['record'][-1][2] for logger_dict in dict_list])\n",
    "                    time_mean, time_std = -1, -1\n",
    "                else:\n",
    "                    time_mean = np.mean([logger_dict['result']['time'][-1] for logger_dict in dict_list])\n",
    "                    time_std = np.std([logger_dict['result']['time'][-1] for logger_dict in dict_list])\n",
    "                    risk_mean, risk_std, subopt_mean, subopt_std = -1, -1, -1, -1\n",
    "\n",
    "                dfsco = dfsco.append(\n",
    "                {\"T\": paras(T),\n",
    "                \"d\": paras(d),\n",
    "                \"p\": paras(p),\n",
    "                'algo': algo[:-4],\n",
    "                \"scale\": paras(scale),\n",
    "                \"noise_free\": paras(noise_free),\n",
    "                \"test_flag\": paras(test_flag),\n",
    "                \"risk_mean\": risk_mean,\n",
    "                \"subopt_mean\": subopt_mean,\n",
    "                \"time_mean\": time_mean,\n",
    "                \"risk_std\": risk_std,\n",
    "                \"subopt_std\": subopt_std,\n",
    "                \"time_std\": time_std\n",
    "                }, ignore_index=True)\n",
    "dfbandit = dfbandit.replace(-1, 10000)\n",
    "dfsco = dfsco.replace(-1, 10000)\n",
    "dfsco = dfsco.replace({\"OFW_ple2\":\"OFW\", \"OFW_pge2\":\"OFW\", \"Local_MD\":\"LocalMD\"})\n",
    "dfsco_time = dfsco[dfsco['test_flag'] == 'False']\n",
    "dfsco_risksub  = dfsco[dfsco['test_flag'] == 'True']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Risk</th>\n",
       "      <th>SubOpt</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <th>d</th>\n",
       "      <th>p</th>\n",
       "      <th>algo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">1000</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00353$\\pm$0.000474</td>\n",
       "      <td>0.00597$\\pm$0.00278</td>\n",
       "      <td>0.105$\\pm$0.0519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0272$\\pm$0.0153</td>\n",
       "      <td>0.0834$\\pm$0.0521</td>\n",
       "      <td>0.073$\\pm$0.0159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0154$\\pm$0.00332</td>\n",
       "      <td>0.132$\\pm$0.03</td>\n",
       "      <td>0.123$\\pm$0.0609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0479$\\pm$0.0175</td>\n",
       "      <td>0.598$\\pm$0.215</td>\n",
       "      <td>0.0647$\\pm$0.00787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0439$\\pm$0.00445</td>\n",
       "      <td>0.697$\\pm$0.0612</td>\n",
       "      <td>0.116$\\pm$0.0419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0318$\\pm$0.0031</td>\n",
       "      <td>1.53$\\pm$0.152</td>\n",
       "      <td>0.0781$\\pm$0.0288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">2000</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00285$\\pm$0.000243</td>\n",
       "      <td>0.0023$\\pm$0.00154</td>\n",
       "      <td>0.163$\\pm$0.0403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0139$\\pm$0.00387</td>\n",
       "      <td>0.0394$\\pm$0.0135</td>\n",
       "      <td>0.144$\\pm$0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00769$\\pm$0.00148</td>\n",
       "      <td>0.0546$\\pm$0.0158</td>\n",
       "      <td>0.186$\\pm$0.0312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0364$\\pm$0.0189</td>\n",
       "      <td>0.45$\\pm$0.243</td>\n",
       "      <td>0.156$\\pm$0.0365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0272$\\pm$0.00601</td>\n",
       "      <td>0.4$\\pm$0.0878</td>\n",
       "      <td>0.176$\\pm$0.0357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0278$\\pm$0.00373</td>\n",
       "      <td>1.26$\\pm$0.154</td>\n",
       "      <td>0.137$\\pm$0.0166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">5000</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00263$\\pm$0.000153</td>\n",
       "      <td>0.000887$\\pm$0.000461</td>\n",
       "      <td>0.437$\\pm$0.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00744$\\pm$0.00172</td>\n",
       "      <td>0.0167$\\pm$0.00537</td>\n",
       "      <td>0.289$\\pm$0.0283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00408$\\pm$0.000398</td>\n",
       "      <td>0.0163$\\pm$0.00387</td>\n",
       "      <td>0.473$\\pm$0.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0177$\\pm$0.00212</td>\n",
       "      <td>0.199$\\pm$0.0299</td>\n",
       "      <td>0.3$\\pm$0.0615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0104$\\pm$0.00227</td>\n",
       "      <td>0.135$\\pm$0.0427</td>\n",
       "      <td>0.462$\\pm$0.108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0198$\\pm$0.00573</td>\n",
       "      <td>0.915$\\pm$0.323</td>\n",
       "      <td>0.315$\\pm$0.0604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">10000</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00251$\\pm$3.98e-05</td>\n",
       "      <td>0.000233$\\pm$0.000111</td>\n",
       "      <td>0.859$\\pm$0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0035$\\pm$0.000452</td>\n",
       "      <td>0.00353$\\pm$0.0015</td>\n",
       "      <td>0.663$\\pm$0.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">10</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00294$\\pm$0.000163</td>\n",
       "      <td>0.00431$\\pm$0.000736</td>\n",
       "      <td>0.92$\\pm$0.243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00727$\\pm$0.00119</td>\n",
       "      <td>0.062$\\pm$0.0156</td>\n",
       "      <td>0.573$\\pm$0.0368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">20</th>\n",
       "      <th>1.5</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.00524$\\pm$0.000717</td>\n",
       "      <td>0.0465$\\pm$0.0104</td>\n",
       "      <td>0.903$\\pm$0.146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inf</th>\n",
       "      <th>OFW</th>\n",
       "      <td>0.0124$\\pm$0.0034</td>\n",
       "      <td>0.523$\\pm$0.168</td>\n",
       "      <td>0.564$\\pm$0.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Risk                 SubOpt  \\\n",
       "T     d  p   algo                                                \n",
       "1000  5  1.5 OFW   0.00353$\\pm$0.000474    0.00597$\\pm$0.00278   \n",
       "         inf OFW      0.0272$\\pm$0.0153      0.0834$\\pm$0.0521   \n",
       "      10 1.5 OFW     0.0154$\\pm$0.00332         0.132$\\pm$0.03   \n",
       "         inf OFW      0.0479$\\pm$0.0175        0.598$\\pm$0.215   \n",
       "      20 1.5 OFW     0.0439$\\pm$0.00445       0.697$\\pm$0.0612   \n",
       "         inf OFW      0.0318$\\pm$0.0031         1.53$\\pm$0.152   \n",
       "2000  5  1.5 OFW   0.00285$\\pm$0.000243     0.0023$\\pm$0.00154   \n",
       "         inf OFW     0.0139$\\pm$0.00387      0.0394$\\pm$0.0135   \n",
       "      10 1.5 OFW    0.00769$\\pm$0.00148      0.0546$\\pm$0.0158   \n",
       "         inf OFW      0.0364$\\pm$0.0189         0.45$\\pm$0.243   \n",
       "      20 1.5 OFW     0.0272$\\pm$0.00601         0.4$\\pm$0.0878   \n",
       "         inf OFW     0.0278$\\pm$0.00373         1.26$\\pm$0.154   \n",
       "5000  5  1.5 OFW   0.00263$\\pm$0.000153  0.000887$\\pm$0.000461   \n",
       "         inf OFW    0.00744$\\pm$0.00172     0.0167$\\pm$0.00537   \n",
       "      10 1.5 OFW   0.00408$\\pm$0.000398     0.0163$\\pm$0.00387   \n",
       "         inf OFW     0.0177$\\pm$0.00212       0.199$\\pm$0.0299   \n",
       "      20 1.5 OFW     0.0104$\\pm$0.00227       0.135$\\pm$0.0427   \n",
       "         inf OFW     0.0198$\\pm$0.00573        0.915$\\pm$0.323   \n",
       "10000 5  1.5 OFW   0.00251$\\pm$3.98e-05  0.000233$\\pm$0.000111   \n",
       "         inf OFW    0.0035$\\pm$0.000452     0.00353$\\pm$0.0015   \n",
       "      10 1.5 OFW   0.00294$\\pm$0.000163   0.00431$\\pm$0.000736   \n",
       "         inf OFW    0.00727$\\pm$0.00119       0.062$\\pm$0.0156   \n",
       "      20 1.5 OFW   0.00524$\\pm$0.000717      0.0465$\\pm$0.0104   \n",
       "         inf OFW      0.0124$\\pm$0.0034        0.523$\\pm$0.168   \n",
       "\n",
       "                                 Time  \n",
       "T     d  p   algo                      \n",
       "1000  5  1.5 OFW     0.105$\\pm$0.0519  \n",
       "         inf OFW     0.073$\\pm$0.0159  \n",
       "      10 1.5 OFW     0.123$\\pm$0.0609  \n",
       "         inf OFW   0.0647$\\pm$0.00787  \n",
       "      20 1.5 OFW     0.116$\\pm$0.0419  \n",
       "         inf OFW    0.0781$\\pm$0.0288  \n",
       "2000  5  1.5 OFW     0.163$\\pm$0.0403  \n",
       "         inf OFW      0.144$\\pm$0.028  \n",
       "      10 1.5 OFW     0.186$\\pm$0.0312  \n",
       "         inf OFW     0.156$\\pm$0.0365  \n",
       "      20 1.5 OFW     0.176$\\pm$0.0357  \n",
       "         inf OFW     0.137$\\pm$0.0166  \n",
       "5000  5  1.5 OFW      0.437$\\pm$0.117  \n",
       "         inf OFW     0.289$\\pm$0.0283  \n",
       "      10 1.5 OFW      0.473$\\pm$0.142  \n",
       "         inf OFW       0.3$\\pm$0.0615  \n",
       "      20 1.5 OFW      0.462$\\pm$0.108  \n",
       "         inf OFW     0.315$\\pm$0.0604  \n",
       "10000 5  1.5 OFW      0.859$\\pm$0.143  \n",
       "         inf OFW      0.663$\\pm$0.112  \n",
       "      10 1.5 OFW       0.92$\\pm$0.243  \n",
       "         inf OFW     0.573$\\pm$0.0368  \n",
       "      20 1.5 OFW      0.903$\\pm$0.146  \n",
       "         inf OFW       0.564$\\pm$0.13  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def analyze_sco(df_time, df_risksub):\n",
    "    # df_groupby = df.groupby(by=['T','d','p', 'algo', 'noise_free', 'scale', 'test_flag']).agg({'risk_mean': ['min'], 'subopt_mean': 'min', 'time_mean': 'min'})\n",
    "    # df_groupby_testfalse = df_groupby.loc[(slice(None), slice(None), slice(None), slice(None), [\"False\"], slice(None), ['True']), :].droplevel([\"noise_free\"])\n",
    "    df_risksub = df_risksub.sort_values('risk_mean', ascending=True).drop_duplicates(['T','d','p', 'algo', 'noise_free'])\n",
    "    df_risksub = df_risksub[df_risksub['noise_free']=='False']\n",
    "    df_time = df_time[df_time['noise_free'] == 'False']\n",
    "    df_summary = df_risksub[['T','d','p', 'algo', 'scale', 'risk_mean', 'risk_std', 'subopt_mean', 'subopt_std']].merge(df_time[['T','d','p', 'algo', 'scale', 'time_mean', 'time_std']], left_on=['T','d','p', 'algo', 'scale'], right_on=['T','d','p', 'algo', 'scale'], how = 'left')\n",
    "    df_temp = df_summary.groupby(by = ['T','d','p', 'algo']).mean().loc[([\"1000\", \"2000\", \"5000\", \"10000\"], [\"5\", \"10\", \"20\"], slice(None), slice(None))]\n",
    "    df_temp[['risk_mean', 'risk_std', 'subopt_mean', 'subopt_std', 'time_mean', 'time_std']] = df_temp[['risk_mean', 'risk_std', 'subopt_mean', 'subopt_std', 'time_mean', 'time_std']].applymap('{:,.3g}'.format)\n",
    "    df_temp_str = df_temp[['risk_mean', 'risk_std', 'subopt_mean', 'subopt_std', 'time_mean', 'time_std']].astype(str)\n",
    "    df_temp_str['link'] = '$\\pm$'\n",
    "    df_temp_str['Risk'] = df_temp_str['risk_mean'] + df_temp_str['link'] + df_temp_str['risk_std']\n",
    "    df_temp_str['SubOpt'] = df_temp_str['subopt_mean'] + df_temp_str['link'] + df_temp_str['subopt_std']\n",
    "    df_temp_str['Time'] = df_temp_str['time_mean'] + df_temp_str['link'] + df_temp_str['time_std']\n",
    "    # df_temp_str.loc[(slice(None), slice(None), slice(None), 'LocalMD')] = '-'\n",
    "    return df_temp_str\n",
    "\n",
    "df_temp_str  = analyze_sco(dfsco_time, dfsco_risksub)\n",
    "# df_temp_str[['Risk', 'SubOpt', 'Time']].to_latex(buf = '../preprint/entries/exp_table.tex', multirow=True, escape=False, label = 'tab:exp', caption = 'Experiment Results')\n",
    "df_temp_str[['Risk', 'SubOpt', 'Time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'regret_mean'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/home/liangzhp/dp-SCO-streaming-code/summarize_table.ipynb Cell 4'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bampere/home/liangzhp/dp-SCO-streaming-code/summarize_table.ipynb#ch0000003vscode-remote?line=0'>1</a>\u001b[0m dfbandit \u001b[39m=\u001b[39m dfbandit\u001b[39m.\u001b[39;49msort_values(\u001b[39m'\u001b[39;49m\u001b[39mregret_mean\u001b[39;49m\u001b[39m'\u001b[39;49m, ascending\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\u001b[39m.\u001b[39mdrop_duplicates([\u001b[39m'\u001b[39m\u001b[39mT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39md\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39malgo\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bampere/home/liangzhp/dp-SCO-streaming-code/summarize_table.ipynb#ch0000003vscode-remote?line=1'>2</a>\u001b[0m df_temp \u001b[39m=\u001b[39m dfbandit\u001b[39m.\u001b[39mgroupby(by \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mT\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39md\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39malgo\u001b[39m\u001b[39m'\u001b[39m])\u001b[39m.\u001b[39mmean()\u001b[39m.\u001b[39mloc[([\u001b[39m\"\u001b[39m\u001b[39m1000\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m2000\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m5000\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m10000\u001b[39m\u001b[39m\"\u001b[39m], [\u001b[39m\"\u001b[39m\u001b[39m10\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m20\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m50\u001b[39m\u001b[39m\"\u001b[39m], \u001b[39mslice\u001b[39m(\u001b[39mNone\u001b[39;00m), \u001b[39mslice\u001b[39m(\u001b[39mNone\u001b[39;00m))]\n\u001b[1;32m      <a href='vscode-notebook-cell://ssh-remote%2Bampere/home/liangzhp/dp-SCO-streaming-code/summarize_table.ipynb#ch0000003vscode-remote?line=2'>3</a>\u001b[0m feature_subset \u001b[39m=\u001b[39m [\u001b[39m'\u001b[39m\u001b[39mregret_mean\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mregret_std\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mtime_mean\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mtime_std\u001b[39m\u001b[39m'\u001b[39m]\n",
      "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[1;32m    306\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    307\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39marguments),\n\u001b[1;32m    308\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m    309\u001b[0m         stacklevel\u001b[39m=\u001b[39mstacklevel,\n\u001b[1;32m    310\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:6307\u001b[0m, in \u001b[0;36mDataFrame.sort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position, ignore_index, key)\u001b[0m\n\u001b[1;32m   6303\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mlen\u001b[39m(by):\n\u001b[1;32m   6304\u001b[0m     \u001b[39m# len(by) == 1\u001b[39;00m\n\u001b[1;32m   6306\u001b[0m     by \u001b[39m=\u001b[39m by[\u001b[39m0\u001b[39m]\n\u001b[0;32m-> 6307\u001b[0m     k \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_label_or_level_values(by, axis\u001b[39m=\u001b[39;49maxis)\n\u001b[1;32m   6309\u001b[0m     \u001b[39m# need to rewrap column in Series to apply key function\u001b[39;00m\n\u001b[1;32m   6310\u001b[0m     \u001b[39mif\u001b[39;00m key \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   6311\u001b[0m         \u001b[39m# error: Incompatible types in assignment (expression has type\u001b[39;00m\n\u001b[1;32m   6312\u001b[0m         \u001b[39m# \"Series\", variable has type \"ndarray\")\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/generic.py:1848\u001b[0m, in \u001b[0;36mNDFrame._get_label_or_level_values\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1846\u001b[0m     values \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39maxes[axis]\u001b[39m.\u001b[39mget_level_values(key)\u001b[39m.\u001b[39m_values\n\u001b[1;32m   1847\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1848\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key)\n\u001b[1;32m   1850\u001b[0m \u001b[39m# Check for duplicates\u001b[39;00m\n\u001b[1;32m   1851\u001b[0m \u001b[39mif\u001b[39;00m values\u001b[39m.\u001b[39mndim \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n",
      "\u001b[0;31mKeyError\u001b[0m: 'regret_mean'"
     ]
    }
   ],
   "source": [
    "dfbandit = dfbandit.sort_values('regret_mean', ascending=True).drop_duplicates(['T','d','algo'])\n",
    "df_temp = dfbandit.groupby(by = ['T','d', 'algo']).mean().loc[([\"1000\", \"2000\", \"5000\", \"10000\"], [\"10\", \"20\", \"50\"], slice(None), slice(None))]\n",
    "feature_subset = ['regret_mean', 'regret_std', 'time_mean', 'time_std']\n",
    "df_temp[feature_subset] = df_temp[feature_subset].applymap('{:,.3g}'.format)\n",
    "df_temp_str = df_temp[feature_subset].astype(str)\n",
    "df_temp_str['link'] = '$\\pm$'\n",
    "df_temp_str['Regret'] = df_temp_str['regret_mean'] + df_temp_str['link'] + df_temp_str['regret_std']\n",
    "df_temp_str['Time'] = df_temp_str['time_mean'] + df_temp_str['link'] + df_temp_str['time_std']\n",
    "df_temp_str[['Regret', 'Time']]"
   ]
  }
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