Tables for ML1M.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question 1: Is active learning helping to boost recommendation general performance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 1. Iterative. Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>C</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Greedy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.169505</td>\n",
       "      <td>0.233142</td>\n",
       "      <td>0.567882</td>\n",
       "      <td>0.525302</td>\n",
       "      <td>0.525827</td>\n",
       "      <td>0.456693</td>\n",
       "      <td>0.037910</td>\n",
       "      <td>0.065046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ThompsonSampling</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.167140</td>\n",
       "      <td>0.230464</td>\n",
       "      <td>0.561318</td>\n",
       "      <td>0.520612</td>\n",
       "      <td>0.521890</td>\n",
       "      <td>0.453512</td>\n",
       "      <td>0.037526</td>\n",
       "      <td>0.064626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCB</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.168059</td>\n",
       "      <td>0.231867</td>\n",
       "      <td>0.568339</td>\n",
       "      <td>0.525290</td>\n",
       "      <td>0.531654</td>\n",
       "      <td>0.457402</td>\n",
       "      <td>0.038300</td>\n",
       "      <td>0.065245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.169060</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.569887</td>\n",
       "      <td>0.526123</td>\n",
       "      <td>0.526677</td>\n",
       "      <td>0.455874</td>\n",
       "      <td>0.037898</td>\n",
       "      <td>0.064948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UCB</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.168617</td>\n",
       "      <td>0.232875</td>\n",
       "      <td>0.573098</td>\n",
       "      <td>0.528425</td>\n",
       "      <td>0.531528</td>\n",
       "      <td>0.457291</td>\n",
       "      <td>0.038263</td>\n",
       "      <td>0.065235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model    C  R-Precision      NDCG     MAP@5    MAP@10  \\\n",
       "0            Greedy  0.0     0.169505  0.233142  0.567882  0.525302   \n",
       "1  ThompsonSampling  0.0     0.167140  0.230464  0.561318  0.520612   \n",
       "2               UCB  0.5     0.168059  0.231867  0.568339  0.525290   \n",
       "3               UCB  1.0     0.169060  0.233027  0.569887  0.526123   \n",
       "4               UCB  2.0     0.168617  0.232875  0.573098  0.528425   \n",
       "\n",
       "   Precision@5  Precision@10  Recall@5  Recall@10  \n",
       "0     0.525827      0.456693  0.037910   0.065046  \n",
       "1     0.521890      0.453512  0.037526   0.064626  \n",
       "2     0.531654      0.457402  0.038300   0.065245  \n",
       "3     0.526677      0.455874  0.037898   0.064948  \n",
       "4     0.531528      0.457291  0.038263   0.065235  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Iterative'] == True) & (df['SampleFromAll'] == True)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "\n",
    "# df[(df['Iterative'] == True) & (df['SampleFromAll'] == True)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table1.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 2. Iterative. Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>C</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Greedy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.233702</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.625411</td>\n",
       "      <td>0.584062</td>\n",
       "      <td>0.580913</td>\n",
       "      <td>0.522677</td>\n",
       "      <td>0.042423</td>\n",
       "      <td>0.075868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ThompsonSampling</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232349</td>\n",
       "      <td>0.299699</td>\n",
       "      <td>0.621474</td>\n",
       "      <td>0.582742</td>\n",
       "      <td>0.582173</td>\n",
       "      <td>0.522268</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>0.075856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCB</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.231266</td>\n",
       "      <td>0.300095</td>\n",
       "      <td>0.631832</td>\n",
       "      <td>0.589725</td>\n",
       "      <td>0.588819</td>\n",
       "      <td>0.527874</td>\n",
       "      <td>0.043138</td>\n",
       "      <td>0.076818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.232511</td>\n",
       "      <td>0.300145</td>\n",
       "      <td>0.624056</td>\n",
       "      <td>0.583961</td>\n",
       "      <td>0.581984</td>\n",
       "      <td>0.522803</td>\n",
       "      <td>0.042550</td>\n",
       "      <td>0.075978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UCB</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.232289</td>\n",
       "      <td>0.299984</td>\n",
       "      <td>0.625411</td>\n",
       "      <td>0.585460</td>\n",
       "      <td>0.582488</td>\n",
       "      <td>0.524126</td>\n",
       "      <td>0.042706</td>\n",
       "      <td>0.076199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model    C  R-Precision      NDCG     MAP@5    MAP@10  \\\n",
       "0            Greedy  0.0     0.233702  0.301108  0.625411  0.584062   \n",
       "1  ThompsonSampling  0.0     0.232349  0.299699  0.621474  0.582742   \n",
       "2               UCB  0.5     0.231266  0.300095  0.631832  0.589725   \n",
       "3               UCB  1.0     0.232511  0.300145  0.624056  0.583961   \n",
       "4               UCB  2.0     0.232289  0.299984  0.625411  0.585460   \n",
       "\n",
       "   Precision@5  Precision@10  Recall@5  Recall@10  \n",
       "0     0.580913      0.522677  0.042423   0.075868  \n",
       "1     0.582173      0.522268  0.042478   0.075856  \n",
       "2     0.588819      0.527874  0.043138   0.076818  \n",
       "3     0.581984      0.522803  0.042550   0.075978  \n",
       "4     0.582488      0.524126  0.042706   0.076199  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Iterative'] == True) & (df['SampleFromAll'] == False)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Iterative'] == True) & (df['SampleFromAll'] == False)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 3. Non-Iterative. Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>C</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Greedy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160961</td>\n",
       "      <td>0.224324</td>\n",
       "      <td>0.559470</td>\n",
       "      <td>0.516241</td>\n",
       "      <td>0.519055</td>\n",
       "      <td>0.446677</td>\n",
       "      <td>0.037405</td>\n",
       "      <td>0.063629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ThompsonSampling</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160909</td>\n",
       "      <td>0.217527</td>\n",
       "      <td>0.518442</td>\n",
       "      <td>0.474580</td>\n",
       "      <td>0.470425</td>\n",
       "      <td>0.407276</td>\n",
       "      <td>0.033785</td>\n",
       "      <td>0.057846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCB</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.161268</td>\n",
       "      <td>0.222113</td>\n",
       "      <td>0.548774</td>\n",
       "      <td>0.502971</td>\n",
       "      <td>0.499843</td>\n",
       "      <td>0.430551</td>\n",
       "      <td>0.035883</td>\n",
       "      <td>0.061287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.161174</td>\n",
       "      <td>0.221170</td>\n",
       "      <td>0.541444</td>\n",
       "      <td>0.498199</td>\n",
       "      <td>0.498898</td>\n",
       "      <td>0.430394</td>\n",
       "      <td>0.035883</td>\n",
       "      <td>0.061284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UCB</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.160291</td>\n",
       "      <td>0.218124</td>\n",
       "      <td>0.531462</td>\n",
       "      <td>0.484496</td>\n",
       "      <td>0.482457</td>\n",
       "      <td>0.411055</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.058493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model    C  R-Precision      NDCG     MAP@5    MAP@10  \\\n",
       "0            Greedy  0.0     0.160961  0.224324  0.559470  0.516241   \n",
       "1  ThompsonSampling  0.0     0.160909  0.217527  0.518442  0.474580   \n",
       "2               UCB  0.5     0.161268  0.222113  0.548774  0.502971   \n",
       "3               UCB  1.0     0.161174  0.221170  0.541444  0.498199   \n",
       "4               UCB  2.0     0.160291  0.218124  0.531462  0.484496   \n",
       "\n",
       "   Precision@5  Precision@10  Recall@5  Recall@10  \n",
       "0     0.519055      0.446677  0.037405   0.063629  \n",
       "1     0.470425      0.407276  0.033785   0.057846  \n",
       "2     0.499843      0.430551  0.035883   0.061287  \n",
       "3     0.498898      0.430394  0.035883   0.061284  \n",
       "4     0.482457      0.411055  0.034507   0.058493  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Iterative'] == False) & (df['SampleFromAll'] == True)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Iterative'] == False) & (df['SampleFromAll'] == True)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table3.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 4. Non-Iterative. Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>C</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Greedy</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.223278</td>\n",
       "      <td>0.293907</td>\n",
       "      <td>0.636387</td>\n",
       "      <td>0.599495</td>\n",
       "      <td>0.602961</td>\n",
       "      <td>0.539150</td>\n",
       "      <td>0.044158</td>\n",
       "      <td>0.078547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ThompsonSampling</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.218842</td>\n",
       "      <td>0.285719</td>\n",
       "      <td>0.613557</td>\n",
       "      <td>0.573595</td>\n",
       "      <td>0.573165</td>\n",
       "      <td>0.510961</td>\n",
       "      <td>0.042046</td>\n",
       "      <td>0.074824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UCB</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.221447</td>\n",
       "      <td>0.291819</td>\n",
       "      <td>0.638286</td>\n",
       "      <td>0.597731</td>\n",
       "      <td>0.598583</td>\n",
       "      <td>0.531654</td>\n",
       "      <td>0.043819</td>\n",
       "      <td>0.077472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UCB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.220472</td>\n",
       "      <td>0.289660</td>\n",
       "      <td>0.626317</td>\n",
       "      <td>0.587666</td>\n",
       "      <td>0.586835</td>\n",
       "      <td>0.526677</td>\n",
       "      <td>0.043156</td>\n",
       "      <td>0.076987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UCB</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.219830</td>\n",
       "      <td>0.288738</td>\n",
       "      <td>0.626614</td>\n",
       "      <td>0.587225</td>\n",
       "      <td>0.589291</td>\n",
       "      <td>0.522520</td>\n",
       "      <td>0.043310</td>\n",
       "      <td>0.076412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Model    C  R-Precision      NDCG     MAP@5    MAP@10  \\\n",
       "0            Greedy  0.0     0.223278  0.293907  0.636387  0.599495   \n",
       "1  ThompsonSampling  0.0     0.218842  0.285719  0.613557  0.573595   \n",
       "2               UCB  0.5     0.221447  0.291819  0.638286  0.597731   \n",
       "3               UCB  1.0     0.220472  0.289660  0.626317  0.587666   \n",
       "4               UCB  2.0     0.219830  0.288738  0.626614  0.587225   \n",
       "\n",
       "   Precision@5  Precision@10  Recall@5  Recall@10  \n",
       "0     0.602961      0.539150  0.044158   0.078547  \n",
       "1     0.573165      0.510961  0.042046   0.074824  \n",
       "2     0.598583      0.531654  0.043819   0.077472  \n",
       "3     0.586835      0.526677  0.043156   0.076987  \n",
       "4     0.589291      0.522520  0.043310   0.076412  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Iterative'] == False) & (df['SampleFromAll'] == False)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Iterative'] == False) & (df['SampleFromAll'] == False)].groupby(['Model', 'C'], as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Model', 'C', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table4.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question 2: Is doing active learning iteratively on active set helping recommender system's general performance?  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 5. Greedy Algorithm, Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.160961</td>\n",
       "      <td>0.224324</td>\n",
       "      <td>0.559470</td>\n",
       "      <td>0.516241</td>\n",
       "      <td>0.519055</td>\n",
       "      <td>0.446677</td>\n",
       "      <td>0.037405</td>\n",
       "      <td>0.063629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.169505</td>\n",
       "      <td>0.233142</td>\n",
       "      <td>0.567882</td>\n",
       "      <td>0.525302</td>\n",
       "      <td>0.525827</td>\n",
       "      <td>0.456693</td>\n",
       "      <td>0.037910</td>\n",
       "      <td>0.065046</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.160961  0.224324  0.559470  0.516241     0.519055   \n",
       "1       True     0.169505  0.233142  0.567882  0.525302     0.525827   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.446677  0.037405   0.063629  \n",
       "1      0.456693  0.037910   0.065046  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'Greedy') & (df['SampleFromAll'] == True)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'Greedy') & (df['SampleFromAll'] == True)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table5.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 6. Greedy Algorithm, Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.223278</td>\n",
       "      <td>0.293907</td>\n",
       "      <td>0.636387</td>\n",
       "      <td>0.599495</td>\n",
       "      <td>0.602961</td>\n",
       "      <td>0.539150</td>\n",
       "      <td>0.044158</td>\n",
       "      <td>0.078547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.233702</td>\n",
       "      <td>0.301108</td>\n",
       "      <td>0.625411</td>\n",
       "      <td>0.584062</td>\n",
       "      <td>0.580913</td>\n",
       "      <td>0.522677</td>\n",
       "      <td>0.042423</td>\n",
       "      <td>0.075868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.223278  0.293907  0.636387  0.599495     0.602961   \n",
       "1       True     0.233702  0.301108  0.625411  0.584062     0.580913   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.539150  0.044158   0.078547  \n",
       "1      0.522677  0.042423   0.075868  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'Greedy') & (df['SampleFromAll'] == False)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'Greedy') & (df['SampleFromAll'] == False)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table6.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 7. Thompson Sampling, Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.160909</td>\n",
       "      <td>0.217527</td>\n",
       "      <td>0.518442</td>\n",
       "      <td>0.474580</td>\n",
       "      <td>0.470425</td>\n",
       "      <td>0.407276</td>\n",
       "      <td>0.033785</td>\n",
       "      <td>0.057846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.167140</td>\n",
       "      <td>0.230464</td>\n",
       "      <td>0.561318</td>\n",
       "      <td>0.520612</td>\n",
       "      <td>0.521890</td>\n",
       "      <td>0.453512</td>\n",
       "      <td>0.037526</td>\n",
       "      <td>0.064626</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.160909  0.217527  0.518442  0.474580     0.470425   \n",
       "1       True     0.167140  0.230464  0.561318  0.520612     0.521890   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.407276  0.033785   0.057846  \n",
       "1      0.453512  0.037526   0.064626  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'ThompsonSampling') & (df['SampleFromAll'] == True)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'ThompsonSampling') & (df['SampleFromAll'] == True)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table7.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 8. Thompson Sampling, Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.218842</td>\n",
       "      <td>0.285719</td>\n",
       "      <td>0.613557</td>\n",
       "      <td>0.573595</td>\n",
       "      <td>0.573165</td>\n",
       "      <td>0.510961</td>\n",
       "      <td>0.042046</td>\n",
       "      <td>0.074824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.232349</td>\n",
       "      <td>0.299699</td>\n",
       "      <td>0.621474</td>\n",
       "      <td>0.582742</td>\n",
       "      <td>0.582173</td>\n",
       "      <td>0.522268</td>\n",
       "      <td>0.042478</td>\n",
       "      <td>0.075856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.218842  0.285719  0.613557  0.573595     0.573165   \n",
       "1       True     0.232349  0.299699  0.621474  0.582742     0.582173   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.510961  0.042046   0.074824  \n",
       "1      0.522268  0.042478   0.075856  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'ThompsonSampling') & (df['SampleFromAll'] == False)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'ThompsonSampling') & (df['SampleFromAll'] == False)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table8.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 9. UCB, C=0.5, Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.161268</td>\n",
       "      <td>0.222113</td>\n",
       "      <td>0.548774</td>\n",
       "      <td>0.502971</td>\n",
       "      <td>0.499843</td>\n",
       "      <td>0.430551</td>\n",
       "      <td>0.035883</td>\n",
       "      <td>0.061287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.168059</td>\n",
       "      <td>0.231867</td>\n",
       "      <td>0.568339</td>\n",
       "      <td>0.525290</td>\n",
       "      <td>0.531654</td>\n",
       "      <td>0.457402</td>\n",
       "      <td>0.038300</td>\n",
       "      <td>0.065245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.161268  0.222113  0.548774  0.502971     0.499843   \n",
       "1       True     0.168059  0.231867  0.568339  0.525290     0.531654   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.430551  0.035883   0.061287  \n",
       "1      0.457402  0.038300   0.065245  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 0.5)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 0.5)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table9.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 10. UCB, C=0.5, Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.221447</td>\n",
       "      <td>0.291819</td>\n",
       "      <td>0.638286</td>\n",
       "      <td>0.597731</td>\n",
       "      <td>0.598583</td>\n",
       "      <td>0.531654</td>\n",
       "      <td>0.043819</td>\n",
       "      <td>0.077472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.231266</td>\n",
       "      <td>0.300095</td>\n",
       "      <td>0.631832</td>\n",
       "      <td>0.589725</td>\n",
       "      <td>0.588819</td>\n",
       "      <td>0.527874</td>\n",
       "      <td>0.043138</td>\n",
       "      <td>0.076818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.221447  0.291819  0.638286  0.597731     0.598583   \n",
       "1       True     0.231266  0.300095  0.631832  0.589725     0.588819   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.531654  0.043819   0.077472  \n",
       "1      0.527874  0.043138   0.076818  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == False) & (df['C'] == 0.5)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == False) & (df['C'] == 0.5)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table10.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 11. UCB, C=1.0, Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.161174</td>\n",
       "      <td>0.221170</td>\n",
       "      <td>0.541444</td>\n",
       "      <td>0.498199</td>\n",
       "      <td>0.498898</td>\n",
       "      <td>0.430394</td>\n",
       "      <td>0.035883</td>\n",
       "      <td>0.061284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.169060</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.569887</td>\n",
       "      <td>0.526123</td>\n",
       "      <td>0.526677</td>\n",
       "      <td>0.455874</td>\n",
       "      <td>0.037898</td>\n",
       "      <td>0.064948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.161174  0.221170  0.541444  0.498199     0.498898   \n",
       "1       True     0.169060  0.233027  0.569887  0.526123     0.526677   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.430394  0.035883   0.061284  \n",
       "1      0.455874  0.037898   0.064948  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 1.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 1.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table11.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 12. UCB, C=1.0, Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.220472</td>\n",
       "      <td>0.289660</td>\n",
       "      <td>0.626317</td>\n",
       "      <td>0.587666</td>\n",
       "      <td>0.586835</td>\n",
       "      <td>0.526677</td>\n",
       "      <td>0.043156</td>\n",
       "      <td>0.076987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.232511</td>\n",
       "      <td>0.300145</td>\n",
       "      <td>0.624056</td>\n",
       "      <td>0.583961</td>\n",
       "      <td>0.581984</td>\n",
       "      <td>0.522803</td>\n",
       "      <td>0.042550</td>\n",
       "      <td>0.075978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.220472  0.289660  0.626317  0.587666     0.586835   \n",
       "1       True     0.232511  0.300145  0.624056  0.583961     0.581984   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.526677  0.043156   0.076987  \n",
       "1      0.522803  0.042550   0.075978  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == False) & (df['C'] == 1.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 1.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table12.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 13. UCB, C=2.0, Sample from all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.160291</td>\n",
       "      <td>0.218124</td>\n",
       "      <td>0.531462</td>\n",
       "      <td>0.484496</td>\n",
       "      <td>0.482457</td>\n",
       "      <td>0.411055</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.058493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.168617</td>\n",
       "      <td>0.232875</td>\n",
       "      <td>0.573098</td>\n",
       "      <td>0.528425</td>\n",
       "      <td>0.531528</td>\n",
       "      <td>0.457291</td>\n",
       "      <td>0.038263</td>\n",
       "      <td>0.065235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.160291  0.218124  0.531462  0.484496     0.482457   \n",
       "1       True     0.168617  0.232875  0.573098  0.528425     0.531528   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.411055  0.034507   0.058493  \n",
       "1      0.457291  0.038263   0.065235  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 2.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == True) & (df['C'] == 2.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table13.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table 14. UCB, C=2.0, Sample from only positive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        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>Iterative</th>\n",
       "      <th>R-Precision</th>\n",
       "      <th>NDCG</th>\n",
       "      <th>MAP@5</th>\n",
       "      <th>MAP@10</th>\n",
       "      <th>Precision@5</th>\n",
       "      <th>Precision@10</th>\n",
       "      <th>Recall@5</th>\n",
       "      <th>Recall@10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.219830</td>\n",
       "      <td>0.288738</td>\n",
       "      <td>0.626614</td>\n",
       "      <td>0.587225</td>\n",
       "      <td>0.589291</td>\n",
       "      <td>0.522520</td>\n",
       "      <td>0.043310</td>\n",
       "      <td>0.076412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.232289</td>\n",
       "      <td>0.299984</td>\n",
       "      <td>0.625411</td>\n",
       "      <td>0.585460</td>\n",
       "      <td>0.582488</td>\n",
       "      <td>0.524126</td>\n",
       "      <td>0.042706</td>\n",
       "      <td>0.076199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Iterative  R-Precision      NDCG     MAP@5    MAP@10  Precision@5  \\\n",
       "0      False     0.219830  0.288738  0.626614  0.587225     0.589291   \n",
       "1       True     0.232289  0.299984  0.625411  0.585460     0.582488   \n",
       "\n",
       "   Precision@10  Recall@5  Recall@10  \n",
       "0      0.522520  0.043310   0.076412  \n",
       "1      0.524126  0.042706   0.076199  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('final_result.csv', sep='\\t')\n",
    "df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == False) & (df['C'] == 2.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "'Recall@5', 'Recall@10']]\n",
    "\n",
    "# df[(df['Model'] == 'UCB') & (df['SampleFromAll'] == False) & (df['C'] == 2.0)].groupby('Iterative', as_index=False)['MAP@5', 'MAP@10', 'MAP@15', 'MAP@20', 'MAP@50', 'NDCG',\n",
    "# 'Precision@5', 'Precision@10', 'Precision@15', 'Precision@20', 'Precision@50', 'R-Precision', 'Recall@5',\n",
    "# 'Recall@10', 'Recall@15', 'Recall@20', 'Recall@50'].agg({'MAP@5': 'mean', 'MAP@10': 'mean', 'MAP@15': 'mean', \n",
    "# 'MAP@20': 'mean', 'MAP@50': 'mean', 'NDCG': 'mean', 'Precision@5': 'mean', 'Precision@10': 'mean', \n",
    "# 'Precision@15': 'mean', 'Precision@20': 'mean', 'Precision@50': 'mean', 'R-Precision': 'mean', 'Recall@5': 'mean',\n",
    "# 'Recall@10': 'mean', 'Recall@15': 'mean', 'Recall@20': 'mean', 'Recall@50': 'mean'})[['Iterative', \n",
    "# 'R-Precision', 'NDCG', 'MAP@5', 'MAP@10', 'Precision@5', 'Precision@10',\n",
    "# 'Recall@5', 'Recall@10']].to_csv('table14.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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