Analisis_Output_BendersPy_FLcvar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "70959e20",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from plotnine import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7295a17e",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('outFLcvar_221014.txt', sep=' ', names=['file','it','LB','UB','Gap','FeasCut','OptCut','PartSize','time'])\n",
    "df['seed'] = df['file'].str.extract(r'_(\\d*).out').astype('int')\n",
    "df['nScen'] = df['file'].str.extract(r'txt_(\\d*)_').astype('int')\n",
    "df['method'] = df['file'].str.extract(r'_([ampsf])_\\d*.out')\n",
    "df['instance'] = df['file'].str.extract(r'FLCVAR_(.*).txt')\n",
    "det = df['file'].str.split('_', expand=True)\n",
    "df['family']=det[1]\n",
    "df['pNumber']=det[2]\n",
    "df['config']=det[3].str.extract(r'(\\d)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9de0a74c",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "df['instanceFamily'] = df['instance'].str.slice(start=0, stop=4) + 'x'\n",
    "df['method'] = pd.Categorical(df['method'], categories=['f','a','m','s'], ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "36a777f3",
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "#df['time'].isna()\n",
    "#df['instance'] = pd.Categorical(df['instance'], categories=['cap61','cap62','cap63','cap64','cap101','cap102','cap103','cap104','cap131','cap132','cap133','cap134'])\n",
    "df['instance'] = pd.Categorical(df['instance'], categories=['cap41','cap42','cap43','cap44','cap61','cap62','cap63','cap64','cap71','cap72','cap73','cap74'])\n",
    "#df['nScen']  = pd.Categorical(df['nScen'], categories=['100','500','1000','2500','5000'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5ea84819",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['time'] > 86400,'time']=np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e2f3a236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>file</th>\n",
       "      <th>it</th>\n",
       "      <th>LB</th>\n",
       "      <th>UB</th>\n",
       "      <th>Gap</th>\n",
       "      <th>FeasCut</th>\n",
       "      <th>OptCut</th>\n",
       "      <th>PartSize</th>\n",
       "      <th>time</th>\n",
       "      <th>seed</th>\n",
       "      <th>nScen</th>\n",
       "      <th>method</th>\n",
       "      <th>instance</th>\n",
       "      <th>family</th>\n",
       "      <th>pNumber</th>\n",
       "      <th>config</th>\n",
       "      <th>instanceFamily</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>outFLCVAR221014/FLCVAR_cap41.txt_100_a_1.out:F...</td>\n",
       "      <td>7</td>\n",
       "      <td>643401.317969</td>\n",
       "      <td>643401.317969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6296</td>\n",
       "      <td>27</td>\n",
       "      <td>33.589583</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>a</td>\n",
       "      <td>cap41</td>\n",
       "      <td>cap41.txt</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cap4x</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                 file  it             LB  \\\n",
       "41  outFLCVAR221014/FLCVAR_cap41.txt_100_a_1.out:F...   7  643401.317969   \n",
       "\n",
       "               UB  Gap  FeasCut  OptCut  PartSize       time  seed  nScen  \\\n",
       "41  643401.317969  0.0        0    6296        27  33.589583     1    100   \n",
       "\n",
       "   method instance     family pNumber config instanceFamily  \n",
       "41      a    cap41  cap41.txt     100    NaN          cap4x  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['nScen']==100) & (df['method']=='a') & (df['seed']==1) & (df['instance']=='cap41')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "10cce1bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nScen  method\n",
       "100    f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "500    f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "1000   f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "2500   f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "Name: LB, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['nScen','method'], dropna=True)['LB'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "28f2b33f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instance  nScen\n",
       "cap71     100      3.902102e+08\n",
       "cap61     100      3.902102e+08\n",
       "cap41     100      3.863222e+08\n",
       "cap72     100      3.729228e+08\n",
       "cap62     100      3.729228e+08\n",
       "cap42     100      3.703945e+08\n",
       "cap43     100      3.650789e+08\n",
       "cap44     100      3.650789e+08\n",
       "cap63     100      3.524109e+08\n",
       "cap64     100      3.507739e+08\n",
       "cap74     100      3.389383e+08\n",
       "cap73     100      3.326525e+08\n",
       "cap63     2500     1.976795e+08\n",
       "cap73     2500     2.927969e+07\n",
       "cap64     2500     2.834146e+07\n",
       "cap74     500      1.482549e+07\n",
       "cap64     500      1.433924e+07\n",
       "cap63     500      1.428830e+07\n",
       "cap73     500      1.359665e+07\n",
       "cap71     500      1.352632e+07\n",
       "cap61     500      1.352632e+07\n",
       "cap41     500      1.333775e+07\n",
       "cap72     500      1.253424e+07\n",
       "cap62     500      1.253424e+07\n",
       "cap42     500      1.214077e+07\n",
       "cap43     500      1.202281e+07\n",
       "cap44     500      1.202281e+07\n",
       "cap74     1000     8.449923e+06\n",
       "cap41     1000     7.962232e+06\n",
       "cap63     1000     7.798693e+06\n",
       "cap64     1000     7.782703e+06\n",
       "cap73     1000     7.582138e+06\n",
       "cap71     1000     7.513178e+06\n",
       "cap61     1000     7.513178e+06\n",
       "cap42     1000     7.351860e+06\n",
       "cap44     1000     7.242375e+06\n",
       "cap43     1000     7.242344e+06\n",
       "cap72     1000     6.707783e+06\n",
       "cap62     1000     6.707783e+06\n",
       "cap72     2500     5.063613e+06\n",
       "cap62     2500     5.063610e+06\n",
       "cap41     2500     4.479698e+06\n",
       "cap42     2500     4.457481e+06\n",
       "cap43     2500     4.382172e+06\n",
       "cap44     2500     4.381695e+06\n",
       "cap71     2500     4.322737e+06\n",
       "cap61     2500     4.322715e+06\n",
       "cap74     2500     4.127582e+06\n",
       "Name: LB, dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['instance','nScen'])['LB'].var().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "e6b041de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nScen  method\n",
       "100    f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "500    f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "1000   f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "2500   f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "Name: time, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['nScen','method'])['time'].count() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "6fbf9d1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['file', 'it', 'LB', 'UB', 'Gap', 'FeasCut', 'OptCut', 'PartSize',\n",
       "       'time', 'seed', 'nScen', 'method', 'instance', 'family', 'pNumber',\n",
       "       'config', 'instanceFamily'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "e7e7da27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nScen  method\n",
       "100    f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "500    f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "1000   f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "2500   f         120\n",
       "       a         120\n",
       "       m         120\n",
       "       s         120\n",
       "Name: time, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['nScen','method'])['time'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d66b5310",
   "metadata": {},
   "outputs": [],
   "source": [
    "nSeeds = 10\n",
    "nRuns = nSeeds * df['instance'].nunique()\n",
    "df['rank']=df.groupby(['nScen','method'])['time'].rank('min')/nRuns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6121b136",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxRank = df.groupby(['nScen','method'])['rank'].max().reset_index()\n",
    "df2 = df.copy()\n",
    "#for row in maxRank.iterrows():\n",
    "#    df2.loc[len(df2)] = [None,None,None,None,None,None,None,None,0,row[1]['nScen'],row[1]['method'],None,None,None,None,None,0]\n",
    "#     if (row[1]['rank']<1):\n",
    "#         df2.loc[len(df2)] = [None,None,None,None,None,None,None,None,7200,row[1]['nScen'],row[1]['method'],None,None,None,None,None,row[1]['rank']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "10f1afe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Algorithm'] = df2['method'].replace({'a': 'Adaptive', 'm':'Multi', 's':'Single', 'f':'Det. Equiv.'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a110c515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Adaptive', 'Adaptive', 'Adaptive', 'Adaptive', 'Adaptive', ..., 'Single', 'Single', 'Single', 'Single', 'Single']\n",
       "Length: 1920\n",
       "Categories (4, object): ['Adaptive' < 'Multi' < 'Single' < 'Det. Equiv.']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Algorithm'] = pd.Categorical(df2['Algorithm'], categories=['Adaptive','Multi','Single','Det. Equiv.'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "56c02f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/ggplot.py:718: PlotnineWarning: Saving 10 x 2 in image.\n",
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Filename: FLcvar_profile.pdf\n",
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/scales/scale_linetype.py:46: PlotnineWarning: Using linetype for an ordinal variable is not advised.\n",
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/scales/scale_linetype.py:46: PlotnineWarning: Using linetype for an ordinal variable is not advised.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (680339061)>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg = ggplot(df2[df2['nScen']!=5000],\n",
    "            aes(x='time', y='rank', color='Algorithm', linetype='Algorithm', group='Algorithm'))\\\n",
    "+ geom_line() + facet_wrap('nScen', scales='fixed', nrow=1)\\\n",
    "+ scale_color_hue()\\\n",
    "+ xlab('Time') + ylab('% instances') + theme_bw() + ylim(0,1)\\\n",
    "+ scale_x_continuous(breaks=[0,3600,10800, 21600, 32400, 43200], \\\n",
    "                     labels=['', '1h', '3h', '6h', '9h', '12h'])\\\n",
    "+ theme(figure_size=(10,2), legend_position='bottom', legend_title=None)\n",
    "#+ scale_x_log10(breaks=[10,60,300, 900, 3600]) \n",
    "gg.save('FLcvar_profile.pdf')\n",
    "gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "822e45a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/geoms/geom_path.py:75: PlotnineWarning: geom_path: Removed 120 rows containing missing values.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (672145313)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df2[df2['nScen']!=5000], aes(x='time', y='rank', color='Algorithm', group='Algorithm')) + geom_line() + facet_wrap('nScen')\\\n",
    "+ xlab('Time') + ylab('% instances') + theme_bw() + ylim(0,1)\\\n",
    "+ scale_x_log10(breaks=[10,60,300, 900, 3600]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99a7f804",
   "metadata": {},
   "outputs": [],
   "source": [
    "timeA = df[df['method']=='a'].groupby(['instance','nScen','seed'])['time'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3eb022f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['timeRel'] = df.apply(lambda x : x['time']/timeA.loc[x['instance'],x['nScen'],x['seed']], axis=1)\n",
    "df['logtimeRel'] = np.log(df['timeRel'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "22024eb6",
   "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>method</th>\n",
       "      <th>f</th>\n",
       "      <th>a</th>\n",
       "      <th>m</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instanceFamily</th>\n",
       "      <th>nScen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap4x</th>\n",
       "      <th>100</th>\n",
       "      <td>0.20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.73</td>\n",
       "      <td>5.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.33</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.04</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>0.94</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.47</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap6x</th>\n",
       "      <th>100</th>\n",
       "      <td>0.38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.77</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.88</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.87</td>\n",
       "      <td>7.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>1.54</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.79</td>\n",
       "      <td>6.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>4.92</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.42</td>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap7x</th>\n",
       "      <th>100</th>\n",
       "      <td>0.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.25</td>\n",
       "      <td>12.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>2.74</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.57</td>\n",
       "      <td>11.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>5.56</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.88</td>\n",
       "      <td>10.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>14.42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.04</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "method                    f    a     m      s\n",
       "instanceFamily nScen                         \n",
       "cap4x          100     0.20  1.0  2.73   5.57\n",
       "               500     0.33  1.0  3.30   6.68\n",
       "               1000    0.46  1.0  3.04   6.82\n",
       "               2500    0.94  1.0  3.47   6.50\n",
       "cap6x          100     0.38  1.0  2.77   8.10\n",
       "               500     0.88  1.0  2.87   7.09\n",
       "               1000    1.54  1.0  2.79   6.13\n",
       "               2500    4.92  1.0  3.42   5.87\n",
       "cap7x          100     0.95  1.0  3.25  12.19\n",
       "               500     2.74  1.0  3.57  11.45\n",
       "               1000    5.56  1.0  3.88  10.13\n",
       "               2500   14.42  1.0  4.04   9.95"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PTrelTimes = np.exp(df.pivot_table(index=['instanceFamily','nScen'], columns='method', values='logtimeRel', aggfunc='mean')).round(2)\n",
    "PTrelTimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17ad0868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['file', 'it', 'LB', 'UB', 'Gap', 'FeasCut', 'OptCut', 'PartSize',\n",
       "       'time', 'seed', 'nScen', 'method', 'instance', 'family', 'pNumber',\n",
       "       'config', 'instanceFamily', 'rank', 'timeRel', 'logtimeRel'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "98d17001",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"4\" valign=\"top\">cap4x</th>\n",
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       "      <td>2856.450</td>\n",
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       "      <th>500</th>\n",
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       "      <td>129223.050</td>\n",
       "      <td>3370.700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.075</td>\n",
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       "      <th>1000</th>\n",
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       "      <td>229659.750</td>\n",
       "      <td>3643.400</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>823.066519</td>\n",
       "      <td>2363.863420</td>\n",
       "      <td>5037.669020</td>\n",
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       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>208466.275</td>\n",
       "      <td>587637.275</td>\n",
       "      <td>3818.125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.725</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1776.817783</td>\n",
       "      <td>2020.414497</td>\n",
       "      <td>7203.296494</td>\n",
       "      <td>13013.624034</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap6x</th>\n",
       "      <th>100</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10601.625</td>\n",
       "      <td>23881.175</td>\n",
       "      <td>3223.025</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.555051</td>\n",
       "      <td>63.940738</td>\n",
       "      <td>181.541732</td>\n",
       "      <td>495.550275</td>\n",
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       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>49267.575</td>\n",
       "      <td>125685.050</td>\n",
       "      <td>3745.475</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.975</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>338.196329</td>\n",
       "      <td>412.474833</td>\n",
       "      <td>1292.430123</td>\n",
       "      <td>2667.953488</td>\n",
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       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>94975.375</td>\n",
       "      <td>235090.200</td>\n",
       "      <td>3784.975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.125</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1404.328248</td>\n",
       "      <td>937.413778</td>\n",
       "      <td>2641.139915</td>\n",
       "      <td>5508.688965</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>190432.975</td>\n",
       "      <td>587653.125</td>\n",
       "      <td>3612.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11667.889465</td>\n",
       "      <td>2449.700847</td>\n",
       "      <td>9020.406364</td>\n",
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       "      <th rowspan=\"4\" valign=\"top\">cap7x</th>\n",
       "      <th>100</th>\n",
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       "      <td>13696.150</td>\n",
       "      <td>2417.400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.225</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.226104</td>\n",
       "      <td>28.835798</td>\n",
       "      <td>90.630346</td>\n",
       "      <td>337.882893</td>\n",
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       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22435.050</td>\n",
       "      <td>69123.875</td>\n",
       "      <td>2635.275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>550.241762</td>\n",
       "      <td>168.133331</td>\n",
       "      <td>580.591356</td>\n",
       "      <td>1875.762334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>42239.725</td>\n",
       "      <td>138971.525</td>\n",
       "      <td>2626.550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2452.983525</td>\n",
       "      <td>354.183544</td>\n",
       "      <td>1319.335987</td>\n",
       "      <td>3452.165439</td>\n",
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       "      <th>2500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>95468.025</td>\n",
       "      <td>324070.975</td>\n",
       "      <td>2955.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.575</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19975.840455</td>\n",
       "      <td>1098.068688</td>\n",
       "      <td>4074.413803</td>\n",
       "      <td>10227.232951</td>\n",
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      ],
      "text/plain": [
       "                     OptCut                                     it          \\\n",
       "method                    f           a           m         s    f       a   \n",
       "instanceFamily nScen                                                         \n",
       "cap4x          100      0.0   14725.725   24056.050  2856.450  0.0   8.625   \n",
       "               500      0.0   53182.575  129223.050  3370.700  0.0  15.075   \n",
       "               1000     0.0  106139.925  229659.750  3643.400  0.0  18.200   \n",
       "               2500     0.0  208466.275  587637.275  3818.125  0.0  23.725   \n",
       "cap6x          100      0.0   10601.625   23881.175  3223.025  0.0   8.950   \n",
       "               500      0.0   49267.575  125685.050  3745.475  0.0  15.975   \n",
       "               1000     0.0   94975.375  235090.200  3784.975  0.0  19.125   \n",
       "               2500     0.0  190432.975  587653.125  3612.000  0.0  24.350   \n",
       "cap7x          100      0.0    5344.800   13696.150  2417.400  0.0   9.225   \n",
       "               500      0.0   22435.050   69123.875  2635.275  0.0  16.050   \n",
       "               1000     0.0   42239.725  138971.525  2626.550  0.0  20.000   \n",
       "               2500     0.0   95468.025  324070.975  2955.875  0.0  24.575   \n",
       "\n",
       "                                        time                            \\\n",
       "method                  m    s             f            a            m   \n",
       "instanceFamily nScen                                                     \n",
       "cap4x          100    1.0  1.0     13.239091    81.049182   201.789682   \n",
       "               500    1.0  1.0    105.315264   361.527724  1223.907948   \n",
       "               1000   1.0  1.0    310.997257   823.066519  2363.863420   \n",
       "               2500   1.0  1.0   1776.817783  2020.414497  7203.296494   \n",
       "cap6x          100    1.0  1.0     22.555051    63.940738   181.541732   \n",
       "               500    1.0  1.0    338.196329   412.474833  1292.430123   \n",
       "               1000   1.0  1.0   1404.328248   937.413778  2641.139915   \n",
       "               2500   1.0  1.0  11667.889465  2449.700847  9020.406364   \n",
       "cap7x          100    1.0  1.0     27.226104    28.835798    90.630346   \n",
       "               500    1.0  1.0    550.241762   168.133331   580.591356   \n",
       "               1000   1.0  1.0   2452.983525   354.183544  1319.335987   \n",
       "               2500   1.0  1.0  19975.840455  1098.068688  4074.413803   \n",
       "\n",
       "                                    \n",
       "method                           s  \n",
       "instanceFamily nScen                \n",
       "cap4x          100      421.454805  \n",
       "               500     2285.740220  \n",
       "               1000    5037.669020  \n",
       "               2500   13013.624034  \n",
       "cap6x          100      495.550275  \n",
       "               500     2667.953488  \n",
       "               1000    5508.688965  \n",
       "               2500   13861.256674  \n",
       "cap7x          100      337.882893  \n",
       "               500     1875.762334  \n",
       "               1000    3452.165439  \n",
       "               2500   10227.232951  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PTresults = df.pivot_table(index=['instanceFamily','nScen'], columns='method', values=['time','it','OptCut'])\n",
    "PTresults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e275b5f8",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap4x</th>\n",
       "      <th>100</th>\n",
       "      <td>81.0</td>\n",
       "      <td>8.6</td>\n",
       "      <td>14726.0</td>\n",
       "      <td>201.8</td>\n",
       "      <td>(2.73)</td>\n",
       "      <td>24056.0</td>\n",
       "      <td>421.5</td>\n",
       "      <td>(5.57)</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>13.2</td>\n",
       "      <td>(0.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>361.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>53183.0</td>\n",
       "      <td>1223.9</td>\n",
       "      <td>(3.3)</td>\n",
       "      <td>129223.0</td>\n",
       "      <td>2285.7</td>\n",
       "      <td>(6.68)</td>\n",
       "      <td>3371.0</td>\n",
       "      <td>105.3</td>\n",
       "      <td>(0.33)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>823.1</td>\n",
       "      <td>18.2</td>\n",
       "      <td>106140.0</td>\n",
       "      <td>2363.9</td>\n",
       "      <td>(3.04)</td>\n",
       "      <td>229660.0</td>\n",
       "      <td>5037.7</td>\n",
       "      <td>(6.82)</td>\n",
       "      <td>3643.0</td>\n",
       "      <td>311.0</td>\n",
       "      <td>(0.46)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>2020.4</td>\n",
       "      <td>23.7</td>\n",
       "      <td>208466.0</td>\n",
       "      <td>7203.3</td>\n",
       "      <td>(3.47)</td>\n",
       "      <td>587637.0</td>\n",
       "      <td>13013.6</td>\n",
       "      <td>(6.5)</td>\n",
       "      <td>3818.0</td>\n",
       "      <td>1776.8</td>\n",
       "      <td>(0.94)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap6x</th>\n",
       "      <th>100</th>\n",
       "      <td>63.9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10602.0</td>\n",
       "      <td>181.5</td>\n",
       "      <td>(2.77)</td>\n",
       "      <td>23881.0</td>\n",
       "      <td>495.6</td>\n",
       "      <td>(8.1)</td>\n",
       "      <td>3223.0</td>\n",
       "      <td>22.6</td>\n",
       "      <td>(0.38)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>412.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>49268.0</td>\n",
       "      <td>1292.4</td>\n",
       "      <td>(2.87)</td>\n",
       "      <td>125685.0</td>\n",
       "      <td>2668.0</td>\n",
       "      <td>(7.09)</td>\n",
       "      <td>3745.0</td>\n",
       "      <td>338.2</td>\n",
       "      <td>(0.88)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>937.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>94975.0</td>\n",
       "      <td>2641.1</td>\n",
       "      <td>(2.79)</td>\n",
       "      <td>235090.0</td>\n",
       "      <td>5508.7</td>\n",
       "      <td>(6.13)</td>\n",
       "      <td>3785.0</td>\n",
       "      <td>1404.3</td>\n",
       "      <td>(1.54)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>2449.7</td>\n",
       "      <td>24.4</td>\n",
       "      <td>190433.0</td>\n",
       "      <td>9020.4</td>\n",
       "      <td>(3.42)</td>\n",
       "      <td>587653.0</td>\n",
       "      <td>13861.3</td>\n",
       "      <td>(5.87)</td>\n",
       "      <td>3612.0</td>\n",
       "      <td>11667.9</td>\n",
       "      <td>(4.92)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">cap7x</th>\n",
       "      <th>100</th>\n",
       "      <td>28.8</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5345.0</td>\n",
       "      <td>90.6</td>\n",
       "      <td>(3.25)</td>\n",
       "      <td>13696.0</td>\n",
       "      <td>337.9</td>\n",
       "      <td>(12.19)</td>\n",
       "      <td>2417.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>(0.95)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>168.1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22435.0</td>\n",
       "      <td>580.6</td>\n",
       "      <td>(3.57)</td>\n",
       "      <td>69124.0</td>\n",
       "      <td>1875.8</td>\n",
       "      <td>(11.45)</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>550.2</td>\n",
       "      <td>(2.74)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>354.2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>42240.0</td>\n",
       "      <td>1319.3</td>\n",
       "      <td>(3.88)</td>\n",
       "      <td>138972.0</td>\n",
       "      <td>3452.2</td>\n",
       "      <td>(10.13)</td>\n",
       "      <td>2627.0</td>\n",
       "      <td>2453.0</td>\n",
       "      <td>(5.56)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500</th>\n",
       "      <td>1098.1</td>\n",
       "      <td>24.6</td>\n",
       "      <td>95468.0</td>\n",
       "      <td>4074.4</td>\n",
       "      <td>(4.04)</td>\n",
       "      <td>324071.0</td>\n",
       "      <td>10227.2</td>\n",
       "      <td>(9.95)</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>19975.8</td>\n",
       "      <td>(14.42)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      (time, a)  (it, a)  (OptCut, a)  (time, m)       m  \\\n",
       "instanceFamily nScen                                                       \n",
       "cap4x          100         81.0      8.6      14726.0      201.8  (2.73)   \n",
       "               500        361.5     15.1      53183.0     1223.9   (3.3)   \n",
       "               1000       823.1     18.2     106140.0     2363.9  (3.04)   \n",
       "               2500      2020.4     23.7     208466.0     7203.3  (3.47)   \n",
       "cap6x          100         63.9      9.0      10602.0      181.5  (2.77)   \n",
       "               500        412.5     16.0      49268.0     1292.4  (2.87)   \n",
       "               1000       937.4     19.1      94975.0     2641.1  (2.79)   \n",
       "               2500      2449.7     24.4     190433.0     9020.4  (3.42)   \n",
       "cap7x          100         28.8      9.2       5345.0       90.6  (3.25)   \n",
       "               500        168.1     16.0      22435.0      580.6  (3.57)   \n",
       "               1000       354.2     20.0      42240.0     1319.3  (3.88)   \n",
       "               2500      1098.1     24.6      95468.0     4074.4  (4.04)   \n",
       "\n",
       "                      (OptCut, m)  (time, s)        s  (OptCut, s)  (time, f)  \\\n",
       "instanceFamily nScen                                                            \n",
       "cap4x          100        24056.0      421.5   (5.57)       2856.0       13.2   \n",
       "               500       129223.0     2285.7   (6.68)       3371.0      105.3   \n",
       "               1000      229660.0     5037.7   (6.82)       3643.0      311.0   \n",
       "               2500      587637.0    13013.6    (6.5)       3818.0     1776.8   \n",
       "cap6x          100        23881.0      495.6    (8.1)       3223.0       22.6   \n",
       "               500       125685.0     2668.0   (7.09)       3745.0      338.2   \n",
       "               1000      235090.0     5508.7   (6.13)       3785.0     1404.3   \n",
       "               2500      587653.0    13861.3   (5.87)       3612.0    11667.9   \n",
       "cap7x          100        13696.0      337.9  (12.19)       2417.0       27.2   \n",
       "               500        69124.0     1875.8  (11.45)       2635.0      550.2   \n",
       "               1000      138972.0     3452.2  (10.13)       2627.0     2453.0   \n",
       "               2500      324071.0    10227.2   (9.95)       2956.0    19975.8   \n",
       "\n",
       "                            f  \n",
       "instanceFamily nScen           \n",
       "cap4x          100      (0.2)  \n",
       "               500     (0.33)  \n",
       "               1000    (0.46)  \n",
       "               2500    (0.94)  \n",
       "cap6x          100     (0.38)  \n",
       "               500     (0.88)  \n",
       "               1000    (1.54)  \n",
       "               2500    (4.92)  \n",
       "cap7x          100     (0.95)  \n",
       "               500     (2.74)  \n",
       "               1000    (5.56)  \n",
       "               2500   (14.42)  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableToPaper = pd.concat([\n",
    "    PTresults.loc[:,('time','a')].round(1),\n",
    "    PTresults.loc[:,('it','a')].round(1),\n",
    "    PTresults.loc[:,('OptCut','a')].round(0),\n",
    "    PTresults.loc[:,('time','m')].round(1),\n",
    "    ('('+PTrelTimes.loc[:,'m'].astype('string')+')'),\n",
    "    PTresults.loc[:,('OptCut','m')].round(0),\n",
    "    PTresults.loc[:,('time','s')].round(1),\n",
    "    ('('+PTrelTimes.loc[:,'s'].astype('string')+')'),\n",
    "    PTresults.loc[:,('OptCut','s')].round(0),\n",
    "    PTresults.loc[:,('time','f')].round(1),\n",
    "    ('('+PTrelTimes.loc[:,'f'].astype('string')+')'),\n",
    "], axis=1)\n",
    "tableToPaper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "478ad3ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrrrrlrrlrrl}\n",
      "\\toprule\n",
      "      &      &  (time, a) &  (it, a) &  (OptCut, a) &  (time, m) &       m &  (OptCut, m) &  (time, s) &        s &  (OptCut, s) &  (time, f) &        f \\\\\n",
      "instanceFamily & nScen &            &          &              &            &         &              &            &          &              &            &          \\\\\n",
      "\\midrule\n",
      "cap4x & 100  &       81.0 &      8.6 &      14726.0 &      201.8 &  (2.73) &      24056.0 &      421.5 &   (5.57) &       2856.0 &       13.2 &    (0.2) \\\\\n",
      "      & 500  &      361.5 &     15.1 &      53183.0 &     1223.9 &   (3.3) &     129223.0 &     2285.7 &   (6.68) &       3371.0 &      105.3 &   (0.33) \\\\\n",
      "      & 1000 &      823.1 &     18.2 &     106140.0 &     2363.9 &  (3.04) &     229660.0 &     5037.7 &   (6.82) &       3643.0 &      311.0 &   (0.46) \\\\\n",
      "      & 2500 &     2020.4 &     23.7 &     208466.0 &     7203.3 &  (3.47) &     587637.0 &    13013.6 &    (6.5) &       3818.0 &     1776.8 &   (0.94) \\\\\n",
      "cap6x & 100  &       63.9 &      9.0 &      10602.0 &      181.5 &  (2.77) &      23881.0 &      495.6 &    (8.1) &       3223.0 &       22.6 &   (0.38) \\\\\n",
      "      & 500  &      412.5 &     16.0 &      49268.0 &     1292.4 &  (2.87) &     125685.0 &     2668.0 &   (7.09) &       3745.0 &      338.2 &   (0.88) \\\\\n",
      "      & 1000 &      937.4 &     19.1 &      94975.0 &     2641.1 &  (2.79) &     235090.0 &     5508.7 &   (6.13) &       3785.0 &     1404.3 &   (1.54) \\\\\n",
      "      & 2500 &     2449.7 &     24.4 &     190433.0 &     9020.4 &  (3.42) &     587653.0 &    13861.3 &   (5.87) &       3612.0 &    11667.9 &   (4.92) \\\\\n",
      "cap7x & 100  &       28.8 &      9.2 &       5345.0 &       90.6 &  (3.25) &      13696.0 &      337.9 &  (12.19) &       2417.0 &       27.2 &   (0.95) \\\\\n",
      "      & 500  &      168.1 &     16.0 &      22435.0 &      580.6 &  (3.57) &      69124.0 &     1875.8 &  (11.45) &       2635.0 &      550.2 &   (2.74) \\\\\n",
      "      & 1000 &      354.2 &     20.0 &      42240.0 &     1319.3 &  (3.88) &     138972.0 &     3452.2 &  (10.13) &       2627.0 &     2453.0 &   (5.56) \\\\\n",
      "      & 2500 &     1098.1 &     24.6 &      95468.0 &     4074.4 &  (4.04) &     324071.0 &    10227.2 &   (9.95) &       2956.0 &    19975.8 &  (14.42) \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l3/bdd4cn2n54n618r3kn2qtq6w0000gn/T/ipykernel_21636/1502265818.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n"
     ]
    }
   ],
   "source": [
    "print(tableToPaper.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c90c9dd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/facets/facet.py:440: PlotnineWarning: If you need more space for the x-axis tick text use ... + theme(subplots_adjust={'wspace': 0.25}). Choose an appropriate value for 'wspace'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (672416685)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ggplot(df[df['method'].isin(['a','m'])], aes(x='time', y='OptCut', color='method'))\\\n",
    "+ geom_point() + facet_wrap('instanceFamily', scales='free')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2dd3831",
   "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>method</th>\n",
       "      <th>f</th>\n",
       "      <th>a</th>\n",
       "      <th>m</th>\n",
       "      <th>s</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nScen</th>\n",
       "      <th>instanceFamily</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">100</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>13.2</td>\n",
       "      <td>81.0</td>\n",
       "      <td>201.8</td>\n",
       "      <td>421.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>22.6</td>\n",
       "      <td>63.9</td>\n",
       "      <td>181.5</td>\n",
       "      <td>495.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>27.2</td>\n",
       "      <td>28.8</td>\n",
       "      <td>90.6</td>\n",
       "      <td>337.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">500</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>105.3</td>\n",
       "      <td>361.5</td>\n",
       "      <td>1223.9</td>\n",
       "      <td>2285.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>338.2</td>\n",
       "      <td>412.5</td>\n",
       "      <td>1292.4</td>\n",
       "      <td>2668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>550.2</td>\n",
       "      <td>168.1</td>\n",
       "      <td>580.6</td>\n",
       "      <td>1875.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1000</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>311.0</td>\n",
       "      <td>823.1</td>\n",
       "      <td>2363.9</td>\n",
       "      <td>5037.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>1404.3</td>\n",
       "      <td>937.4</td>\n",
       "      <td>2641.1</td>\n",
       "      <td>5508.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>2453.0</td>\n",
       "      <td>354.2</td>\n",
       "      <td>1319.3</td>\n",
       "      <td>3452.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2500</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>1776.8</td>\n",
       "      <td>2020.4</td>\n",
       "      <td>7203.3</td>\n",
       "      <td>13013.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>11667.9</td>\n",
       "      <td>2449.7</td>\n",
       "      <td>9020.4</td>\n",
       "      <td>13861.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>19975.8</td>\n",
       "      <td>1098.1</td>\n",
       "      <td>4074.4</td>\n",
       "      <td>10227.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "method                      f       a       m        s\n",
       "nScen instanceFamily                                  \n",
       "100   cap4x              13.2    81.0   201.8    421.5\n",
       "      cap6x              22.6    63.9   181.5    495.6\n",
       "      cap7x              27.2    28.8    90.6    337.9\n",
       "500   cap4x             105.3   361.5  1223.9   2285.7\n",
       "      cap6x             338.2   412.5  1292.4   2668.0\n",
       "      cap7x             550.2   168.1   580.6   1875.8\n",
       "1000  cap4x             311.0   823.1  2363.9   5037.7\n",
       "      cap6x            1404.3   937.4  2641.1   5508.7\n",
       "      cap7x            2453.0   354.2  1319.3   3452.2\n",
       "2500  cap4x            1776.8  2020.4  7203.3  13013.6\n",
       "      cap6x           11667.9  2449.7  9020.4  13861.3\n",
       "      cap7x           19975.8  1098.1  4074.4  10227.2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ptTime = df.pivot_table(index=['nScen','instanceFamily'],\\\n",
    "            columns='method',values='time', aggfunc='mean').round(1)\n",
    "ptTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "759541f6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f8cd50d0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>a</th>\n",
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       "      <th>s</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">100</th>\n",
       "      <th>cap4x</th>\n",
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       "      <td>24056.0</td>\n",
       "      <td>2856.4</td>\n",
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       "      <th>cap6x</th>\n",
       "      <td>10601.6</td>\n",
       "      <td>23881.2</td>\n",
       "      <td>3223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>5344.8</td>\n",
       "      <td>13696.2</td>\n",
       "      <td>2417.4</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">500</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>53182.6</td>\n",
       "      <td>129223.0</td>\n",
       "      <td>3370.7</td>\n",
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       "    <tr>\n",
       "      <th>cap6x</th>\n",
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       "      <td>3745.5</td>\n",
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       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>22435.0</td>\n",
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       "      <td>2635.3</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1000</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>106139.9</td>\n",
       "      <td>229659.8</td>\n",
       "      <td>3643.4</td>\n",
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       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>94975.4</td>\n",
       "      <td>235090.2</td>\n",
       "      <td>3785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>42239.7</td>\n",
       "      <td>138971.5</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2500</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>190433.0</td>\n",
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       "      <td>3612.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>95468.0</td>\n",
       "      <td>324071.0</td>\n",
       "      <td>2955.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "method                       a         m       s\n",
       "nScen instanceFamily                            \n",
       "100   cap4x            14725.7   24056.0  2856.4\n",
       "      cap6x            10601.6   23881.2  3223.0\n",
       "      cap7x             5344.8   13696.2  2417.4\n",
       "500   cap4x            53182.6  129223.0  3370.7\n",
       "      cap6x            49267.6  125685.0  3745.5\n",
       "      cap7x            22435.0   69123.9  2635.3\n",
       "1000  cap4x           106139.9  229659.8  3643.4\n",
       "      cap6x            94975.4  235090.2  3785.0\n",
       "      cap7x            42239.7  138971.5  2626.6\n",
       "2500  cap4x           208466.3  587637.3  3818.1\n",
       "      cap6x           190433.0  587653.1  3612.0\n",
       "      cap7x            95468.0  324071.0  2955.9"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ptCuts = df[df['method']!='f'].pivot_table(index=['nScen','instanceFamily'],\\\n",
    "            columns='method',values='OptCut', aggfunc='mean').round(1)\n",
    "ptCuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "379124b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>PartSizeRel</th>\n",
       "      <th>it</th>\n",
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       "    <tr>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">100</th>\n",
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       "      <th>cap6x</th>\n",
       "      <td>0.14</td>\n",
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       "      <th>cap7x</th>\n",
       "      <td>0.15</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2500</th>\n",
       "      <th>cap4x</th>\n",
       "      <td>0.12</td>\n",
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       "      <th>cap6x</th>\n",
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       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>0.14</td>\n",
       "      <td>24.58</td>\n",
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      ],
      "text/plain": [
       "                     PartSizeRel     it\n",
       "method                         a      a\n",
       "nScen instanceFamily                   \n",
       "100   cap4x                 0.22   8.62\n",
       "      cap6x                 0.22   8.95\n",
       "      cap7x                 0.22   9.22\n",
       "500   cap4x                 0.15  15.08\n",
       "      cap6x                 0.16  15.98\n",
       "      cap7x                 0.16  16.05\n",
       "1000  cap4x                 0.14  18.20\n",
       "      cap6x                 0.14  19.12\n",
       "      cap7x                 0.15  20.00\n",
       "2500  cap4x                 0.12  23.72\n",
       "      cap6x                 0.12  24.35\n",
       "      cap7x                 0.14  24.58"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PartSizeRel'] = df['PartSize']/df['nScen'].astype('int')\n",
    "#df[df['method']=='a'].pivot(index='instance',columns='nScen', values=['it','PartSizeRel'])\n",
    "ptAdap = df[df['method']=='a'].pivot_table(index=['nScen','instanceFamily'],\\\n",
    "                columns='method', values=['it','PartSizeRel']).round(2)\n",
    "ptAdap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d8992f50",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.22</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">500</th>\n",
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       "      <th>cap7x</th>\n",
       "      <td>550.2</td>\n",
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       "      <th>cap7x</th>\n",
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       "      <td>3452.2</td>\n",
       "      <td>42239.7</td>\n",
       "      <td>138971.5</td>\n",
       "      <td>2626.6</td>\n",
       "      <td>0.15</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">2500</th>\n",
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       "      <td>7203.3</td>\n",
       "      <td>13013.6</td>\n",
       "      <td>208466.3</td>\n",
       "      <td>587637.3</td>\n",
       "      <td>3818.1</td>\n",
       "      <td>0.12</td>\n",
       "      <td>23.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap6x</th>\n",
       "      <td>11667.9</td>\n",
       "      <td>2449.7</td>\n",
       "      <td>9020.4</td>\n",
       "      <td>13861.3</td>\n",
       "      <td>190433.0</td>\n",
       "      <td>587653.1</td>\n",
       "      <td>3612.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>24.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cap7x</th>\n",
       "      <td>19975.8</td>\n",
       "      <td>1098.1</td>\n",
       "      <td>4074.4</td>\n",
       "      <td>10227.2</td>\n",
       "      <td>95468.0</td>\n",
       "      <td>324071.0</td>\n",
       "      <td>2955.9</td>\n",
       "      <td>0.14</td>\n",
       "      <td>24.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            f       a       m        s         a         m  \\\n",
       "nScen instanceFamily                                                         \n",
       "100   cap4x              13.2    81.0   201.8    421.5   14725.7   24056.0   \n",
       "      cap6x              22.6    63.9   181.5    495.6   10601.6   23881.2   \n",
       "      cap7x              27.2    28.8    90.6    337.9    5344.8   13696.2   \n",
       "500   cap4x             105.3   361.5  1223.9   2285.7   53182.6  129223.0   \n",
       "      cap6x             338.2   412.5  1292.4   2668.0   49267.6  125685.0   \n",
       "      cap7x             550.2   168.1   580.6   1875.8   22435.0   69123.9   \n",
       "1000  cap4x             311.0   823.1  2363.9   5037.7  106139.9  229659.8   \n",
       "      cap6x            1404.3   937.4  2641.1   5508.7   94975.4  235090.2   \n",
       "      cap7x            2453.0   354.2  1319.3   3452.2   42239.7  138971.5   \n",
       "2500  cap4x            1776.8  2020.4  7203.3  13013.6  208466.3  587637.3   \n",
       "      cap6x           11667.9  2449.7  9020.4  13861.3  190433.0  587653.1   \n",
       "      cap7x           19975.8  1098.1  4074.4  10227.2   95468.0  324071.0   \n",
       "\n",
       "                           s  (PartSizeRel, a)  (it, a)  \n",
       "nScen instanceFamily                                     \n",
       "100   cap4x           2856.4              0.22     8.62  \n",
       "      cap6x           3223.0              0.22     8.95  \n",
       "      cap7x           2417.4              0.22     9.22  \n",
       "500   cap4x           3370.7              0.15    15.08  \n",
       "      cap6x           3745.5              0.16    15.98  \n",
       "      cap7x           2635.3              0.16    16.05  \n",
       "1000  cap4x           3643.4              0.14    18.20  \n",
       "      cap6x           3785.0              0.14    19.12  \n",
       "      cap7x           2626.6              0.15    20.00  \n",
       "2500  cap4x           3818.1              0.12    23.72  \n",
       "      cap6x           3612.0              0.12    24.35  \n",
       "      cap7x           2955.9              0.14    24.58  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([ptTime,ptCuts,ptAdap], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9cd8903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llrrrrrrrrr}\n",
      "\\toprule\n",
      "     &       &        f &       a &       m &        s &         a &         m &       s &  (PartSizeRel, a) &  (it, a) \\\\\n",
      "nScen & instanceFamily &          &         &         &          &           &           &         &                   &          \\\\\n",
      "\\midrule\n",
      "100  & cap4x &     13.2 &    81.0 &   201.8 &    421.5 &   14725.7 &   24056.0 &  2856.4 &              0.22 &     8.62 \\\\\n",
      "     & cap6x &     22.6 &    63.9 &   181.5 &    495.6 &   10601.6 &   23881.2 &  3223.0 &              0.22 &     8.95 \\\\\n",
      "     & cap7x &     27.2 &    28.8 &    90.6 &    337.9 &    5344.8 &   13696.2 &  2417.4 &              0.22 &     9.22 \\\\\n",
      "500  & cap4x &    105.3 &   361.5 &  1223.9 &   2285.7 &   53182.6 &  129223.0 &  3370.7 &              0.15 &    15.08 \\\\\n",
      "     & cap6x &    338.2 &   412.5 &  1292.4 &   2668.0 &   49267.6 &  125685.0 &  3745.5 &              0.16 &    15.98 \\\\\n",
      "     & cap7x &    550.2 &   168.1 &   580.6 &   1875.8 &   22435.0 &   69123.9 &  2635.3 &              0.16 &    16.05 \\\\\n",
      "1000 & cap4x &    311.0 &   823.1 &  2363.9 &   5037.7 &  106139.9 &  229659.8 &  3643.4 &              0.14 &    18.20 \\\\\n",
      "     & cap6x &   1404.3 &   937.4 &  2641.1 &   5508.7 &   94975.4 &  235090.2 &  3785.0 &              0.14 &    19.12 \\\\\n",
      "     & cap7x &   2453.0 &   354.2 &  1319.3 &   3452.2 &   42239.7 &  138971.5 &  2626.6 &              0.15 &    20.00 \\\\\n",
      "2500 & cap4x &   1776.8 &  2020.4 &  7203.3 &  13013.6 &  208466.3 &  587637.3 &  3818.1 &              0.12 &    23.72 \\\\\n",
      "     & cap6x &  11667.9 &  2449.7 &  9020.4 &  13861.3 &  190433.0 &  587653.1 &  3612.0 &              0.12 &    24.35 \\\\\n",
      "     & cap7x &  19975.8 &  1098.1 &  4074.4 &  10227.2 &   95468.0 &  324071.0 &  2955.9 &              0.14 &    24.58 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l3/bdd4cn2n54n618r3kn2qtq6w0000gn/T/ipykernel_21636/3836756010.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([ptTime,ptCuts,ptAdap], axis=1).to_latex()) #float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ddb3bad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('OptCut', 'a'),\n",
       "            ('OptCut', 'm'),\n",
       "            ('OptCut', 's'),\n",
       "            (  'time', 'a'),\n",
       "            (  'time', 'm'),\n",
       "            (  'time', 's')],\n",
       "           names=[None, 'method'])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = df[df['method']!='f'].pivot_table(index='nScen', columns='method', values=['time','OptCut'])\n",
    "res.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c30b2d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrr}\n",
      "\\toprule\n",
      "method & \\multicolumn{2}{l}{a} & \\multicolumn{2}{l}{m} & \\multicolumn{2}{l}{s} \\\\\n",
      "{} &    OptCut &    time &    OptCut &    time &  OptCut &     time \\\\\n",
      "nScen &           &         &           &         &         &          \\\\\n",
      "\\midrule\n",
      "100   &  10224.05 &   57.94 &  20544.46 &  157.99 & 2832.29 &   418.30 \\\\\n",
      "500   &  41628.40 &  314.05 & 108010.66 & 1032.31 & 3250.48 &  2276.49 \\\\\n",
      "1000  &  81118.34 &  704.89 & 201240.49 & 2108.11 & 3351.64 &  4666.17 \\\\\n",
      "2500  & 164789.09 & 1856.06 & 499787.12 & 6766.04 & 3462.00 & 12367.37 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l3/bdd4cn2n54n618r3kn2qtq6w0000gn/T/ipykernel_21636/2149590436.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n"
     ]
    }
   ],
   "source": [
    "print(res.swaplevel(axis=1).sort_index(axis=1).to_latex(float_format=\"%.2f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e34bfaff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['nScenText'] = df['nScen'].replace({100:'0.1', 500: '0.5', 1000: '1.0', 2500:'2.5'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1c484202",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/ggplot.py:718: PlotnineWarning: Saving 10 x 5 in image.\n",
      "/opt/homebrew/lib/python3.11/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Filename: FLcvar_objective1.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (674606185)>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg = ggplot(df[df['method']=='a'], aes(x='nScenText', y='LB/10**5', fill='nScenText'))\\\n",
    "+ geom_boxplot() + facet_wrap('instance', scales='free_y', nrow=3)\\\n",
    "+ scale_y_continuous() + scale_fill_hue(name='# Scenarios', guide=False)\\\n",
    "+ xlab('Number of scenarios (thousands)') + ylab('Objective value (1E5)')\\\n",
    "+ theme_bw() + theme(figure_size=(10,5),subplots_adjust={'wspace': 0.25}) \n",
    "gg.save('FLcvar_objective1.pdf')\n",
    "gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "c13b96b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/plotnine/facets/facet.py:390: PlotnineWarning: If you need more space for the x-axis tick text use ... + theme(subplots_adjust={'wspace': 0.25}). Choose an appropriate value for 'wspace'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (328503802)>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg = ggplot(df[df['method']=='a'], aes(x='factor(nScen)', y='LB', fill='factor(nScen)'))\\\n",
    "+ geom_jitter() + facet_wrap('instance', scales='free_y')\\\n",
    "+ scale_y_continuous(breaks=None) + scale_fill_hue(name='# Scenarios')\\\n",
    "+ ggtitle('CVaR objective value') + xlab('Number of scenarios') + ylab('Objective value')\\\n",
    "+ theme(figure_size=(9,4)) + theme_bw()\n",
    "gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b13db0d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.9/site-packages/plotnine/ggplot.py:719: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/usr/local/lib/python3.9/site-packages/plotnine/ggplot.py:722: PlotnineWarning: Filename: FLcvar_objective2.pdf\n",
      "/usr/local/lib/python3.9/site-packages/plotnine/facets/facet.py:390: PlotnineWarning: If you need more space for the x-axis tick text use ... + theme(subplots_adjust={'wspace': 0.25}). Choose an appropriate value for 'wspace'.\n",
      "/usr/local/lib/python3.9/site-packages/plotnine/facets/facet.py:390: PlotnineWarning: If you need more space for the x-axis tick text use ... + theme(subplots_adjust={'wspace': 0.25}). Choose an appropriate value for 'wspace'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (319523021)>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg= ggplot(df[(df['method']=='a') & (df['nScen']!=100)], aes(x='factor(nScen)', y='LB', fill='factor(nScen)'))\\\n",
    "+ geom_boxplot() + facet_wrap('instance', scales='free_y')\\\n",
    "+ scale_y_continuous(breaks=None) + scale_fill_hue(name='# Scenarios')\\\n",
    "+ ggtitle('CVaR objective value') + xlab('Number of scenarios') + ylab('Objective value')\n",
    "gg.save('FLcvar_objective2.pdf')\n",
    "gg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3a4b81dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfSol = pd.read_csv('outsolutions.txt', sep=':', names=['file','xx1','tau','sol'])\n",
    "dfSol['seed'] = dfSol['file'].str.extract(r'_(\\d*).out').astype('int')\n",
    "dfSol['nScen'] = dfSol['file'].str.extract(r'txt_(\\d*)_').astype('int')\n",
    "dfSol['method'] = dfSol['file'].str.extract(r'_([ampsf])_\\d*.out')\n",
    "dfSol['instance'] = dfSol['file'].str.extract(r'FLCVAR_(.*).txt')\n",
    "det = dfSol['file'].str.split('_', expand=True)\n",
    "dfSol['family']=det[1]\n",
    "dfSol['pNumber']=det[2]\n",
    "dfSol['config']=det[3].str.extract(r'(\\d*).out')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "f3337ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nScen  instance\n",
       "100    cap41       4\n",
       "       cap42       4\n",
       "       cap43       4\n",
       "       cap44       4\n",
       "       cap61       1\n",
       "       cap62       2\n",
       "       cap63       3\n",
       "       cap64       3\n",
       "       cap71       1\n",
       "       cap72       2\n",
       "       cap73       1\n",
       "       cap74       1\n",
       "500    cap41       2\n",
       "       cap42       2\n",
       "       cap43       2\n",
       "       cap44       2\n",
       "       cap61       1\n",
       "       cap62       1\n",
       "       cap63       3\n",
       "       cap64       3\n",
       "       cap71       1\n",
       "       cap72       1\n",
       "       cap73       1\n",
       "       cap74       2\n",
       "1000   cap41       2\n",
       "       cap42       2\n",
       "       cap43       2\n",
       "       cap44       2\n",
       "       cap61       1\n",
       "       cap62       1\n",
       "       cap63       2\n",
       "       cap64       2\n",
       "       cap71       1\n",
       "       cap72       1\n",
       "       cap73       1\n",
       "       cap74       1\n",
       "2500   cap41       1\n",
       "       cap42       1\n",
       "       cap43       1\n",
       "       cap44       1\n",
       "       cap61       1\n",
       "       cap62       1\n",
       "       cap63       2\n",
       "       cap64       2\n",
       "       cap71       1\n",
       "       cap72       1\n",
       "       cap73       1\n",
       "       cap74       1\n",
       "Name: sol, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfSol[dfSol['method']=='a'].groupby(['nScen','instance'])['sol'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d75bfc06",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}