Vis-Revision.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "22a62c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "from json import loads, dumps\n",
    "import math\n",
    "import matplotlib\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308d3e9a",
   "metadata": {},
   "source": [
    "# Benchmark - Acc + Feat selection time - Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "id": "f268e990",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RandomForest', 'LightGBM', 'ExtraTrees', 'XGBoost', 'KNeighbors', 'LinearModel']\n",
      "['steel', 'jannis', 'miniboone', 'eyemove', 'bioresponse', 'credit', 'covertype', 'school', 'yprop', 'superconduct']\n",
      "['AutoFeat', 'ARDA', 'MAB', 'BASE', 'JoinAll', 'JoinAll+F']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "folder_name = os.path.abspath(os.path.abspath(''))\n",
    "\n",
    "dataset_path = \"results/revision/final/benchmark-final\"\n",
    "tfd_filename = \"results_autofeat.csv\"\n",
    "arda_filename = \"results_arda.csv\"\n",
    "mab_filename = \"results_mab.csv\"\n",
    "base_filename = \"results_base.csv\"\n",
    "join_all_filename = \"results_join_all.csv\"\n",
    "\n",
    "\n",
    "# Read data\n",
    "df_tfd = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{tfd_filename}\"), header=0)\n",
    "df_arda = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{arda_filename}\"), header=0)\n",
    "df_mab = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{mab_filename}\"), header=0)\n",
    "df_base = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{base_filename}\"), header=0)\n",
    "df_ja = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{join_all_filename}\"), header=0)\n",
    "\n",
    "\n",
    "# Create a big dataframe\n",
    "dataframe = pd.concat([df_tfd, df_arda, df_mab, df_base, df_ja])\n",
    "# dataframe = pd.concat([df_tfd, df_arda, df_mab, df_base])\n",
    "\n",
    "\n",
    "# Remove unused columns\n",
    "dataframe.drop(columns=['join_time', 'depth', 'redundancy_threshold'], inplace=True)\n",
    "\n",
    "\n",
    "# Process columns\n",
    "# number_of_features, features = parse_feature_importance(dataframe)\n",
    "# dataframe['number_features_importance'] = number_of_features\n",
    "\n",
    "# number_of_features, jp_features = parse_join_path_features(dataframe)\n",
    "# dataframe['number_join_path_features'] = number_of_features\n",
    "\n",
    "# nr_common_features, difference = determine_common_features(dataframe)\n",
    "# dataframe['nr_common_features'] = nr_common_features\n",
    "# dataframe['different_features'] = difference\n",
    "\n",
    "dataframe.loc[dataframe['approach']=='TFD_BFS', 'approach'] = 'AutoFeat'\n",
    "dataframe.loc[dataframe['approach']=='Join_All_BFS', 'approach'] = 'JoinAll'\n",
    "dataframe.loc[dataframe['approach']=='Join_All_BFS_Filter', 'approach'] = 'JoinAll+F'\n",
    "\n",
    "\n",
    "\n",
    "# Get globnal variables \n",
    "algs = list(dataframe['algorithm'].unique())\n",
    "print(algs)\n",
    "\n",
    "tables = list(dataframe['data_label'].unique())\n",
    "print(tables)\n",
    "\n",
    "approach = list(dataframe['approach'].unique())\n",
    "print(approach)\n",
    "\n",
    "\n",
    "dataframe.reset_index(inplace=True)\n",
    "dataframe.drop(columns=['index'], inplace=True)\n",
    "dataframe['feature_selection_time'] = dataframe['feature_selection_time'].fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "8b9a16cb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style=\"whitegrid\")\n",
    "pal = sns.color_palette(\"pastel\")\n",
    "pal2 = sns.color_palette('tab10')\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n",
    "\n",
    "# df_basic.sort_values('data_label')\n",
    "\n",
    "time_variable = 'feature_selection_time'\n",
    "df = dataframe[dataframe['algorithm'].isin(['RandomForest', 'LightGBM', 'ExtraTrees', 'XGBoost'])]\n",
    "\n",
    "# df = dataframe\n",
    "\n",
    "\n",
    "# total_time = dataframe.groupby(['data_label', 'approach', 'algorithm', time_variable])[['accuracy']].idxmax()\n",
    "total_time = df.loc[df.groupby(by=['data_label', 'approach', 'algorithm'])['accuracy'].idxmax()]\n",
    "taux = total_time[~total_time['data_label'].isin(['air', 'superconduct', 'yprop'])]\n",
    "# taux = taux[~taux['approach'].isin(['BASE'])]\n",
    "\n",
    "accuracy_cls = df.loc[df.groupby(by=['data_label', 'approach', 'algorithm'])['accuracy'].idxmax()]\n",
    "\n",
    "# accuracy_cls = dataframe.groupby(['data_label', 'approach', 'algorithm'])[['accuracy']].max().reset_index()\n",
    "aux = accuracy_cls[~accuracy_cls['data_label'].isin(['air', 'superconduct', 'yprop'])]\n",
    "\n",
    "\n",
    "ax1 = sns.barplot(x=\"data_label\", y='total_time', hue=\"approach\", \n",
    "                data=taux, \n",
    "#                 palette=[pal[4], pal[0], pal[1], pal[2]], hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'], ax=ax1)\n",
    "                palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAll+F', 'AutoFeat'], ax=ax1)\n",
    "\n",
    "ax1 = sns.barplot(x=\"data_label\", y=time_variable, hue=\"approach\", \n",
    "                data=taux, \n",
    "#                 palette=[pal[4], pal[0], pal[1], pal[2]], hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'], ax=ax1)\n",
    "                palette=[pal2[4], pal2[0], pal2[1], pal2[5], pal2[6], pal2[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAll+F', 'AutoFeat'], ax=ax1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax1.set_title(\"Average total time and feature selection time per dataset\")\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Seconds')\n",
    "ax1.get_legend().remove()\n",
    "ax1.set_yscale('symlog')\n",
    "# ax1.set_ylim()\n",
    "locmin = matplotlib.ticker.LogLocator(base=10, subs=np.arange(0.1,1,0.1),numticks=10)  \n",
    "ax1.yaxis.set_minor_locator(locmin)\n",
    "ax1.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "ax1.grid(True,which=\"both\",ls=\"--\",c='lightgray')\n",
    "ax1.bar_label(ax1.containers[3], labels=['', '', '', '', '', '', 'X', ''], label_type='edge')\n",
    "\n",
    "\n",
    "\n",
    "ax2 = sns.barplot(x=\"data_label\", y=\"accuracy\", hue=\"approach\", \n",
    "                data=aux, \n",
    "#                 palette=[pal[4], pal[0], pal[1], pal[2]], hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'], ax=ax2)\n",
    "                  palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAll+F', 'AutoFeat'], ax=ax2)\n",
    "\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.set_title(\"Average accuracy per dataset\") \n",
    "ax2.set_xticklabels(labels, rotation=30)\n",
    "ax2.set_yticks(np.arange(0, 1.1, 0.25))\n",
    "\n",
    "\n",
    "autofeat_tables = [1, 8, 2, 4, 1, 2, 1, 14]\n",
    "mab_tables = [3, 3, 2, 3, 3, 3, 2, 3]\n",
    "arda_tables = [3, 3, 2, 3, 3, 3, 16, 3]\n",
    "total_tables = [39, 11, 4, 5, 11, 14, '', 14]\n",
    "\n",
    "ax2.bar_label(ax2.containers[1], labels=arda_tables, label_type='center', padding=15)\n",
    "ax2.bar_label(ax2.containers[2], labels=mab_tables, label_type='center')\n",
    "ax2.bar_label(ax2.containers[3], labels=total_tables, label_type='center', padding=10)\n",
    "ax2.bar_label(ax2.containers[3], labels=['', '', '', '', '', '', 'X', ''], label_type='edge', padding=-2)\n",
    "ax2.bar_label(ax2.containers[5], labels=autofeat_tables, label_type='center')\n",
    "\n",
    "ax2.annotate('',xy=(0.5, 0.885),xytext=(-0.5, 0.885), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(1.5, 0.99),xytext=(0.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(2.5, 0.99),xytext=(1.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(3.5, 0.894),xytext=(2.5, 0.894), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(4.5, 0.875),xytext=(3.5, 0.875), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(5.5, 0.9465),xytext=(4.5, 0.9465), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(6.5, 0.831),xytext=(5.5, 0.831), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(7.5, 1.0),xytext=(6.5, 1.0), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "\n",
    "\n",
    "loc, labels = plt.xticks()\n",
    "hh, ll = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "plt.legend(hh, ll, bbox_to_anchor=(1, -0.45), ncol=6, title=\"Approach\", fontsize='x-small', title_fontsize='small')\n",
    "\n",
    "plt.savefig('plots/revision/benchmark-all.pdf', dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "fdcc2a18",
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"6\" valign=\"top\">miniboone</th>\n",
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      ],
      "text/plain": [
       "                        total_time  feature_selection_time  accuracy  \\\n",
       "data_label  approach                                                   \n",
       "bioresponse ARDA         53.262697               51.701897  0.623333   \n",
       "            AutoFeat     54.803325               39.627401  0.784571   \n",
       "            BASE          1.669011                0.000000  0.569141   \n",
       "            JoinAll      43.905720                0.000000  0.783115   \n",
       "            JoinAll+F    32.702948                0.074846  0.788937   \n",
       "            MAB          93.240000               93.240000  0.675400   \n",
       "covertype   ARDA         44.188069               42.662697  0.556667   \n",
       "            AutoFeat    160.609625                6.942177  0.970544   \n",
       "            BASE         47.317562                0.000000  0.503623   \n",
       "            JoinAll     391.620282                0.000000  0.974202   \n",
       "            JoinAll+F   330.105919                0.804420  0.931824   \n",
       "            MAB         725.180000              725.180000  0.634040   \n",
       "credit      ARDA          4.657694                3.390326  0.690000   \n",
       "            AutoFeat      1.850615                0.388358  0.750000   \n",
       "            BASE          1.278346                0.000000  0.690000   \n",
       "            JoinAll       1.693252                0.000000  0.745000   \n",
       "            JoinAll+F     1.604757                0.003011  0.775000   \n",
       "            MAB           9.110000                9.110000  0.685000   \n",
       "eyemove     ARDA         48.619539               47.100549  0.511667   \n",
       "            AutoFeat      2.727467                0.459549  0.658344   \n",
       "            BASE          1.880908                0.000000  0.513141   \n",
       "            JoinAll       3.814097                0.000000  0.670171   \n",
       "            JoinAll+F     3.558273                0.011015  0.622865   \n",
       "            MAB          44.130000               44.130000  0.566360   \n",
       "jannis      ARDA         48.578483               47.017040  0.573333   \n",
       "            AutoFeat     17.596032                2.136092  0.803143   \n",
       "            BASE          7.060490                0.000000  0.567906   \n",
       "            JoinAll      51.531589                0.000000  0.798367   \n",
       "            JoinAll+F    44.899263                0.254710  0.804272   \n",
       "            MAB         221.820000              221.820000  0.559222   \n",
       "miniboone   ARDA         22.161828               20.608685  0.890000   \n",
       "            AutoFeat     22.438813                2.376915  0.939726   \n",
       "            BASE          7.686720                0.000000  0.702603   \n",
       "            JoinAll      77.430370                0.000000  0.942055   \n",
       "            JoinAll+F    71.179759                0.364339  0.921096   \n",
       "            MAB         168.290000              168.290000  0.879795   \n",
       "school      ARDA        155.690314              153.726229  0.833803   \n",
       "            AutoFeat     13.522206               11.954049  0.709859   \n",
       "            BASE          1.384611                0.000000  0.709859   \n",
       "            MAB        2726.070000             2726.070000  0.695775   \n",
       "steel       ARDA          9.873774                8.403135  0.727506   \n",
       "            AutoFeat      2.723553                1.036712  1.000000   \n",
       "            BASE          1.359065                0.000000  0.668380   \n",
       "            JoinAll       3.098659                0.000000  1.000000   \n",
       "            JoinAll+F     2.872779                0.009243  1.000000   \n",
       "            MAB           9.180000                9.180000  0.714653   \n",
       "\n",
       "                       train_time  cutoff_threshold      rank  top_k  \n",
       "data_label  approach                                                  \n",
       "bioresponse ARDA         1.560799               NaN       NaN    NaN  \n",
       "            AutoFeat    15.175924              0.65  0.863356   15.0  \n",
       "            BASE         1.669011               NaN       NaN    NaN  \n",
       "            JoinAll     20.050879               NaN       NaN    NaN  \n",
       "            JoinAll+F    8.773261               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "covertype   ARDA         1.525372               NaN       NaN    NaN  \n",
       "            AutoFeat   153.667447              0.65  1.004754   15.0  \n",
       "            BASE        47.317562               NaN       NaN    NaN  \n",
       "            JoinAll    169.313221               NaN       NaN    NaN  \n",
       "            JoinAll+F  106.994438               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "credit      ARDA         1.267368               NaN       NaN    NaN  \n",
       "            AutoFeat     1.462257              0.65  0.966662   15.0  \n",
       "            BASE         1.278346               NaN       NaN    NaN  \n",
       "            JoinAll      1.486092               NaN       NaN    NaN  \n",
       "            JoinAll+F    1.394586               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "eyemove     ARDA         1.518989               NaN       NaN    NaN  \n",
       "            AutoFeat     2.267918              0.65  0.974232   15.0  \n",
       "            BASE         1.880908               NaN       NaN    NaN  \n",
       "            JoinAll      2.242173               NaN       NaN    NaN  \n",
       "            JoinAll+F    1.975333               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "jannis      ARDA         1.561443               NaN       NaN    NaN  \n",
       "            AutoFeat    15.459940              0.65  1.009946   15.0  \n",
       "            BASE         7.060490               NaN       NaN    NaN  \n",
       "            JoinAll     16.446340               NaN       NaN    NaN  \n",
       "            JoinAll+F    9.559304               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "miniboone   ARDA         1.553143               NaN       NaN    NaN  \n",
       "            AutoFeat    20.061898              0.65  1.039905   15.0  \n",
       "            BASE         7.686720               NaN       NaN    NaN  \n",
       "            JoinAll     17.732193               NaN       NaN    NaN  \n",
       "            JoinAll+F   11.117242               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "school      ARDA         1.964085               NaN       NaN    NaN  \n",
       "            AutoFeat     1.568158              0.65  1.096625   15.0  \n",
       "            BASE         1.384611               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "steel       ARDA         1.470639               NaN       NaN    NaN  \n",
       "            AutoFeat     1.686841              0.65  1.023269   15.0  \n",
       "            BASE         1.359065               NaN       NaN    NaN  \n",
       "            JoinAll      1.798311               NaN       NaN    NaN  \n",
       "            JoinAll+F    1.563188               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  "
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aux.groupby(by=['data_label', 'approach']).max('accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "f2b27f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgd_all = []\n",
    "\n",
    "# a = dataframe.groupby(['approach', 'algorithm', 'data_label',])[['accuracy']].max().reset_index(level=['algorithm', 'data_label'])\n",
    "a = dataframe.groupby(['approach', 'algorithm', \n",
    "                       'data_label'])[['feature_selection_time', \n",
    "                                       'train_time', 'accuracy']].max().reset_index(level=['algorithm', \n",
    "                                                                                           'data_label'])\n",
    "a = a[~a['algorithm'].isin(['KNeighbors', 'LinearModel'])]\n",
    "a = a[~a['data_label'].isin(['superconduct', 'air', 'yprop'])]\n",
    "\n",
    "\n",
    "\n",
    "ARDA = a.loc['ARDA']\n",
    "# ARDA = ARDA[~ARDA['data_label'].isin(['superconduct', 'yprop'])]\n",
    "\n",
    "\n",
    "avgd = {}\n",
    "avgd['approach'] = 'ARDA'\n",
    "avgd['avg_acc'] = ARDA['accuracy'].mean()\n",
    "avgd['avg_feat_sel_time'] = ARDA['feature_selection_time'].mean()\n",
    "avgd['avg_train_time'] = ARDA['train_time'].mean()\n",
    "\n",
    "avgd_all.append(avgd)\n",
    "\n",
    "\n",
    "MAB = a.loc['MAB']\n",
    "# MAB = MAB[~MAB['data_label'].isin(['superconduct', 'yprop'])]\n",
    "\n",
    "avgd = {}\n",
    "avgd['approach'] = 'MAB'\n",
    "avgd['avg_acc'] = MAB['accuracy'].mean()\n",
    "avgd['avg_feat_sel_time'] = MAB['feature_selection_time'].mean()\n",
    "avgd['avg_train_time'] = MAB['train_time'].mean()\n",
    "avgd_all.append(avgd)\n",
    "\n",
    "\n",
    "TFD_BFS = a.loc['AutoFeat']\n",
    "# TFD_BFS = TFD_BFS[~TFD_BFS['data_label'].isin(['superconduct', 'yprop'])]\n",
    "\n",
    "avgd = {}\n",
    "avgd['approach'] = 'AutoFeat'\n",
    "avgd['avg_acc'] = TFD_BFS['accuracy'].mean()\n",
    "avgd['avg_feat_sel_time'] = TFD_BFS['feature_selection_time'].mean()\n",
    "avgd['avg_train_time'] = TFD_BFS['train_time'].mean()\n",
    "avgd_all.append(avgd)\n",
    "\n",
    "\n",
    "avgdf = pd.DataFrame(avgd_all)\n",
    "\n",
    "\n",
    "pal = sns.color_palette(\"pastel\")\n",
    "sns.set(font_scale=1, style=\"whitegrid\")\n",
    "\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 2), sharey=True)\n",
    "\n",
    "rects1 = ax1.barh(avgdf['approach'], avgdf['avg_feat_sel_time'], align='center', height=0.5, \n",
    "                  color=[pal[0], pal[1], pal[2]], \n",
    "                  )\n",
    "\n",
    "rects2 = ax2.barh(avgdf['approach'], avgdf['avg_acc'], align='center', height=0.5,\n",
    "                 color=[pal[0], pal[1], pal[2]],)\n",
    "\n",
    "large_percentiles = [round(p, 2) if p > 50 else '' for p in avgdf['avg_feat_sel_time']]\n",
    "small_percentiles = [round(p, 2) if p <= 50 else '' for p in avgdf['avg_feat_sel_time']]\n",
    "ax1.bar_label(rects1, small_percentiles,\n",
    "              padding=5, color='black', fontweight='bold')\n",
    "ax1.bar_label(rects1, large_percentiles,\n",
    "              padding=-42, color='white', fontweight='bold')\n",
    "\n",
    "\n",
    "large_percentiles = [round(p, 2) if p > 0.7 else '' for p in avgdf['avg_acc']]\n",
    "small_percentiles = [round(p, 2) if p <= 0.7 else '' for p in avgdf['avg_acc']]\n",
    "ax2.bar_label(rects2, small_percentiles,\n",
    "              padding=-32, color='white', fontweight='bold')\n",
    "ax2.bar_label(rects2, large_percentiles,\n",
    "              padding=-32, color='black', fontweight='bold')\n",
    "\n",
    "\n",
    "ax1.set_xlabel('Seconds')\n",
    "ax2.set_xlabel('Accuracy')\n",
    "\n",
    "ax1.set_title('Avg feature discovery time')\n",
    "ax2.set_title('Avg accuracy')\n",
    "\n",
    "ax1.annotate('',xy=(100, 0.4),xytext=(0, 0.4), arrowprops=dict(arrowstyle=\"<->\", color='black'))\n",
    "ax1.annotate(\"5.7x\", xy=(60, 0.5))\n",
    "\n",
    "ax1.annotate('',xy=(350, 1.4),xytext=(0, 1.4), arrowprops=dict(arrowstyle=\"<->\", color='black'))\n",
    "ax1.annotate(\"44x\", xy=(160, 1.5))\n",
    "\n",
    "ax2.annotate('',xy=(0.81, 0),xytext=(0.65, 0), arrowprops=dict(arrowstyle=\"<->\", color='black'))\n",
    "ax2.annotate(\"16%\", xy=(0.66, 0.1))\n",
    "\n",
    "ax2.annotate('',xy=(0.81, 1),xytext=(0.65, 1), arrowprops=dict(arrowstyle=\"<->\", color='black'))\n",
    "ax2.annotate(\"16%\", xy=(0.66, 1.1))\n",
    "\n",
    "\n",
    "plt.savefig('plots/revision/crown.pdf', dpi=300, bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "26e1f15c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.04667797258922\n",
      "22.16886681318283\n",
      "59.37442684173584\n",
      "0.21736928394862579\n",
      "8.115156769752502\n"
     ]
    }
   ],
   "source": [
    "print(total_time[total_time['approach'] == 'JoinAll']['total_time'].mean())\n",
    "\n",
    "print(total_time[total_time['approach'] == 'AutoFeat']['total_time'].mean())\n",
    "\n",
    "print(total_time[total_time['approach'] == 'JoinAllF']['total_time'].mean())\n",
    "\n",
    "\n",
    "\n",
    "print(total_time[total_time['approach'] == 'JoinAllF']['feature_selection_time'].mean())\n",
    "\n",
    "print(total_time[total_time['approach'] == 'AutoFeat']['feature_selection_time'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "254d0542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_label    approach \n",
       "bioresponse   ARDA          0.608750\n",
       "              AutoFeat      0.780568\n",
       "              BASE          0.544396\n",
       "              JoinAll       0.775837\n",
       "              JoinAll+F     0.778748\n",
       "              MAB           0.670670\n",
       "covertype     ARDA          0.548750\n",
       "              AutoFeat      0.938751\n",
       "              BASE          0.502744\n",
       "              JoinAll       0.953526\n",
       "              JoinAll+F     0.911723\n",
       "              MAB           0.621604\n",
       "credit        ARDA          0.680000\n",
       "              AutoFeat      0.747500\n",
       "              BASE          0.643750\n",
       "              JoinAll       0.735000\n",
       "              JoinAll+F     0.745000\n",
       "              MAB           0.670000\n",
       "eyemove       ARDA          0.505000\n",
       "              AutoFeat      0.643068\n",
       "              BASE          0.506899\n",
       "              JoinAll       0.657359\n",
       "              JoinAll+F     0.619251\n",
       "              MAB           0.548784\n",
       "jannis        ARDA          0.551250\n",
       "              AutoFeat      0.794026\n",
       "              BASE          0.556335\n",
       "              JoinAll       0.791681\n",
       "              JoinAll+F     0.799952\n",
       "              MAB           0.552080\n",
       "miniboone     ARDA          0.877500\n",
       "              AutoFeat      0.931387\n",
       "              BASE          0.694161\n",
       "              JoinAll       0.933048\n",
       "              JoinAll+F     0.916524\n",
       "              MAB           0.876729\n",
       "school        ARDA          0.813380\n",
       "              AutoFeat      0.697887\n",
       "              BASE          0.660563\n",
       "              MAB           0.683803\n",
       "steel         ARDA          0.715938\n",
       "              AutoFeat      0.995501\n",
       "              BASE          0.598972\n",
       "              JoinAll       0.997429\n",
       "              JoinAll+F     0.998072\n",
       "              MAB           0.694087\n",
       "superconduct  MAB          10.134031\n",
       "yprop         MAB           0.028885\n",
       "Name: accuracy, dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total_time[total_time['data_label'] == 'jannis']\n",
    "\n",
    "total_time.groupby(by=['data_label', 'approach'])['accuracy'].mean('accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ab64a0",
   "metadata": {},
   "source": [
    "# Benchmark - KNN + Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "ac3199d8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style=\"whitegrid\")\n",
    "pal = sns.color_palette(\"pastel\")\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n",
    "\n",
    "\n",
    "df_time = dataframe[dataframe['algorithm']=='KNeighbors']\n",
    "\n",
    "accuracy_cls = df_time.groupby(['data_label', 'approach'])['accuracy'].max().reset_index()\n",
    "\n",
    "ax1 = sns.barplot(x=\"data_label\", y=\"accuracy\", hue=\"approach\", \n",
    "                data=accuracy_cls, \n",
    "#                   palette=[pal[4], pal[0], pal[1], pal[2]], \n",
    "#                   hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'],\n",
    "#                  ax=ax1)\n",
    "                  palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAll+F', 'AutoFeat'], ax=ax1)\n",
    "\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Accuracy')\n",
    "ax1.set_title(\"KNN - Accuracy per dataset\")  \n",
    "ax1.get_legend().remove()\n",
    "ax1.bar_label(ax1.containers[3], labels=['', '', '', '', '', '', 'X', ''], label_type='edge', padding=-2)\n",
    "\n",
    "autofeat_tables = [1, 8, 2, 4, 1, 2, 1, 14]\n",
    "mab_tables = [3, 3, 2, 3, 3, 3, 2, 3]\n",
    "arda_tables = [3, 3, 2, 3, 3, 3, 16, 3]\n",
    "total_tables = [39, 11, 4, 5, 11, 14, '', 14]\n",
    "\n",
    "ax1.bar_label(ax1.containers[1], labels=arda_tables, label_type='center', padding=15)\n",
    "ax1.bar_label(ax1.containers[2], labels=mab_tables, label_type='center')\n",
    "ax1.bar_label(ax1.containers[3], labels=total_tables, label_type='center', padding=10)\n",
    "ax1.bar_label(ax1.containers[3], labels=['', '', '', '', '', '', 'X', ''], label_type='edge', padding=-2)\n",
    "ax1.bar_label(ax1.containers[5], labels=autofeat_tables, label_type='center')\n",
    "\n",
    "ax1.annotate('',xy=(0.5, 0.885),xytext=(-0.5, 0.885), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(1.5, 0.99),xytext=(0.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(2.5, 0.99),xytext=(1.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(3.5, 0.894),xytext=(2.5, 0.894), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(4.5, 0.875),xytext=(3.5, 0.875), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(5.5, 0.9465),xytext=(4.5, 0.9465), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(6.5, 0.831),xytext=(5.5, 0.831), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(7.5, 1.0),xytext=(6.5, 1.0), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "\n",
    "\n",
    "df_time = dataframe[dataframe['algorithm']=='LinearModel']\n",
    "\n",
    "accuracy_cls = df_time.groupby(['data_label', 'approach'])['accuracy'].max().reset_index()\n",
    "\n",
    "ax2 = sns.barplot(x=\"data_label\", y=\"accuracy\", hue=\"approach\", \n",
    "                data=accuracy_cls,\n",
    "                 palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAll+F', 'AutoFeat'], ax=ax2)\n",
    "#                  ax=ax2)\n",
    "\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.set_title(\"Linear - Accuracy per dataset\")  \n",
    "ax2.bar_label(ax2.containers[3], labels=['', '', '', '', '', '', 'X', ''], label_type='edge', padding=-2)\n",
    "\n",
    "ax2.bar_label(ax2.containers[1], labels=arda_tables, label_type='center', padding=15)\n",
    "ax2.bar_label(ax2.containers[2], labels=mab_tables, label_type='center')\n",
    "ax2.bar_label(ax2.containers[3], labels=total_tables, label_type='center', padding=10)\n",
    "ax2.bar_label(ax2.containers[3], labels=['', '', '', '', '', '', 'X', ''], label_type='edge', padding=-2)\n",
    "ax2.bar_label(ax2.containers[5], labels=autofeat_tables, label_type='center')\n",
    "\n",
    "ax2.annotate('',xy=(0.5, 0.885),xytext=(-0.5, 0.885), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(1.5, 0.99),xytext=(0.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(2.5, 0.99),xytext=(1.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(3.5, 0.894),xytext=(2.5, 0.894), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(4.5, 0.875),xytext=(3.5, 0.875), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(5.5, 0.9465),xytext=(4.5, 0.9465), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(6.5, 0.831),xytext=(5.5, 0.831), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(7.5, 1.0),xytext=(6.5, 1.0), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "\n",
    "\n",
    "loc, labels = plt.xticks()\n",
    "hh, ll = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "ax2.set_xticklabels(labels, rotation=30)\n",
    "plt.legend(hh, ll, bbox_to_anchor=(1, -0.45), ncol=6, title=\"Approach\", fontsize='x-small', title_fontsize='small')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('plots/revision/benchmark_linear_knn_accuracy.pdf', dpi=300, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1bfc144b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>total_time</th>\n",
       "      <th>feature_selection_time</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>train_time</th>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          total_time  feature_selection_time  accuracy  train_time  \\\n",
       "approach                                                             \n",
       "ARDA        3.536389                3.390326     0.645    0.146063   \n",
       "AutoFeat    0.615278                0.388358     0.664    0.226920   \n",
       "BASE        0.142320                0.000000     0.590    0.142320   \n",
       "JoinAll     0.457000                0.000000     0.695    0.249840   \n",
       "JoinAllF    0.395900                0.003011     0.695    0.185729   \n",
       "MAB         2.880000                2.880000     0.655    0.000000   \n",
       "\n",
       "          cutoff_threshold     rank  top_k  \n",
       "approach                                    \n",
       "ARDA                   NaN      NaN    NaN  \n",
       "AutoFeat              0.65  0.97164   15.0  \n",
       "BASE                   NaN      NaN    NaN  \n",
       "JoinAll                NaN      NaN    NaN  \n",
       "JoinAllF               NaN      NaN    NaN  \n",
       "MAB                    NaN      NaN    NaN  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = dataframe[dataframe['algorithm']=='KNeighbors']\n",
    "knn[knn['data_label'] == 'credit'].groupby(by=['approach']).mean('accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "0f9862a1",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.250458</td>\n",
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       "      <td>11.337067</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">credit</th>\n",
       "      <th>ARDA</th>\n",
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       "      <td>0.645000</td>\n",
       "      <td>0.146063</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.142320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.695000</td>\n",
       "      <td>0.249840</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>2.880000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>0.655000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">eyemove</th>\n",
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       "      <td>47.100549</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.184840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>0.504599</td>\n",
       "      <td>0.198689</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>JoinAll</th>\n",
       "      <td>3.009727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.532194</td>\n",
       "      <td>1.437803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>2.417155</td>\n",
       "      <td>0.011015</td>\n",
       "      <td>0.512484</td>\n",
       "      <td>0.834216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">jannis</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>47.386161</td>\n",
       "      <td>47.017040</td>\n",
       "      <td>0.493333</td>\n",
       "      <td>0.369122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>1.012449</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.604366</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.506339</td>\n",
       "      <td>0.604366</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>67.557085</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.644234</td>\n",
       "      <td>32.471836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>50.926710</td>\n",
       "      <td>0.254710</td>\n",
       "      <td>0.720042</td>\n",
       "      <td>15.586750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>769.100000</td>\n",
       "      <td>769.100000</td>\n",
       "      <td>0.501476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">miniboone</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>20.828696</td>\n",
       "      <td>20.608685</td>\n",
       "      <td>0.506667</td>\n",
       "      <td>0.220012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>27.934596</td>\n",
       "      <td>2.376915</td>\n",
       "      <td>0.505384</td>\n",
       "      <td>25.557681</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.146406</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>1.577182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.507808</td>\n",
       "      <td>1.577182</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>104.973732</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.506164</td>\n",
       "      <td>45.275554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>80.889637</td>\n",
       "      <td>0.364339</td>\n",
       "      <td>0.863219</td>\n",
       "      <td>20.827121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>795.420000</td>\n",
       "      <td>795.420000</td>\n",
       "      <td>0.503836</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">school</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>154.437392</td>\n",
       "      <td>153.726229</td>\n",
       "      <td>0.673239</td>\n",
       "      <td>0.711164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>12.186289</td>\n",
       "      <td>11.954049</td>\n",
       "      <td>0.651643</td>\n",
       "      <td>0.232241</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.940010</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.150284</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.645070</td>\n",
       "      <td>0.150284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>12865.680000</td>\n",
       "      <td>12865.680000</td>\n",
       "      <td>0.650704</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">steel</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>8.620846</td>\n",
       "      <td>8.403135</td>\n",
       "      <td>0.632391</td>\n",
       "      <td>0.217711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>1.680152</td>\n",
       "      <td>1.036712</td>\n",
       "      <td>0.585261</td>\n",
       "      <td>0.643440</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.911395</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.126623</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.606684</td>\n",
       "      <td>0.126623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>2.354235</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.596401</td>\n",
       "      <td>1.053888</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>1.625201</td>\n",
       "      <td>0.009243</td>\n",
       "      <td>0.596401</td>\n",
       "      <td>0.315611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>2.510000</td>\n",
       "      <td>2.510000</td>\n",
       "      <td>0.578406</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         total_time  feature_selection_time  accuracy  \\\n",
       "data_label  approach                                                    \n",
       "bioresponse ARDA          52.001227               51.701897  0.486667   \n",
       "            AutoFeat      43.377727               39.627401  0.491800   \n",
       "            BASE           0.206107                0.000000  0.509461   \n",
       "            JoinAll       40.419623                0.000000  0.534207   \n",
       "            JoinAll+F     29.534172                0.074846  0.720524   \n",
       "            MAB          249.550000              249.550000  0.481805   \n",
       "covertype   ARDA          42.913154               42.662697  0.493333   \n",
       "            AutoFeat      14.466271                6.942177  0.500531   \n",
       "            BASE           1.101622                0.000000  0.502289   \n",
       "            JoinAll      233.644128                0.000000  0.973057   \n",
       "            JoinAll+F    228.389618                0.804420  0.887061   \n",
       "            MAB           54.750000               54.750000  0.500189   \n",
       "credit      ARDA           3.536389                3.390326  0.645000   \n",
       "            AutoFeat       0.615278                0.388358  0.664000   \n",
       "            BASE           0.142320                0.000000  0.590000   \n",
       "            JoinAll        0.457000                0.000000  0.695000   \n",
       "            JoinAll+F      0.395900                0.003011  0.695000   \n",
       "            MAB            2.880000                2.880000  0.655000   \n",
       "eyemove     ARDA          47.285389               47.100549  0.498333   \n",
       "            AutoFeat       1.290622                0.459549  0.502081   \n",
       "            BASE           0.198689                0.000000  0.504599   \n",
       "            JoinAll        3.009727                0.000000  0.532194   \n",
       "            JoinAll+F      2.417155                0.011015  0.512484   \n",
       "            MAB           23.360000               23.360000  0.479632   \n",
       "jannis      ARDA          47.386161               47.017040  0.493333   \n",
       "            AutoFeat      21.687951                2.136092  0.501570   \n",
       "            BASE           0.604366                0.000000  0.506339   \n",
       "            JoinAll       67.557085                0.000000  0.644234   \n",
       "            JoinAll+F     50.926710                0.254710  0.720042   \n",
       "            MAB          769.100000              769.100000  0.501476   \n",
       "miniboone   ARDA          20.828696               20.608685  0.506667   \n",
       "            AutoFeat      27.934596                2.376915  0.505384   \n",
       "            BASE           1.577182                0.000000  0.507808   \n",
       "            JoinAll      104.973732                0.000000  0.506164   \n",
       "            JoinAll+F     80.889637                0.364339  0.863219   \n",
       "            MAB          795.420000              795.420000  0.503836   \n",
       "school      ARDA         154.437392              153.726229  0.673239   \n",
       "            AutoFeat      12.186289               11.954049  0.651643   \n",
       "            BASE           0.150284                0.000000  0.645070   \n",
       "            MAB        12865.680000            12865.680000  0.650704   \n",
       "steel       ARDA           8.620846                8.403135  0.632391   \n",
       "            AutoFeat       1.680152                1.036712  0.585261   \n",
       "            BASE           0.126623                0.000000  0.606684   \n",
       "            JoinAll        2.354235                0.000000  0.596401   \n",
       "            JoinAll+F      1.625201                0.009243  0.596401   \n",
       "            MAB            2.510000                2.510000  0.578406   \n",
       "\n",
       "                       train_time  cutoff_threshold      rank  top_k  \n",
       "data_label  approach                                                  \n",
       "bioresponse ARDA         0.299330               NaN       NaN    NaN  \n",
       "            AutoFeat     3.750326              0.65  0.874201   15.0  \n",
       "            BASE         0.206107               NaN       NaN    NaN  \n",
       "            JoinAll     16.564781               NaN       NaN    NaN  \n",
       "            JoinAll+F    5.604485               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "covertype   ARDA         0.250458               NaN       NaN    NaN  \n",
       "            AutoFeat     7.524093              0.65  0.991596   15.0  \n",
       "            BASE         1.101622               NaN       NaN    NaN  \n",
       "            JoinAll     11.337067               NaN       NaN    NaN  \n",
       "            JoinAll+F    5.278137               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "credit      ARDA         0.146063               NaN       NaN    NaN  \n",
       "            AutoFeat     0.226920              0.65  0.971640   15.0  \n",
       "            BASE         0.142320               NaN       NaN    NaN  \n",
       "            JoinAll      0.249840               NaN       NaN    NaN  \n",
       "            JoinAll+F    0.185729               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "eyemove     ARDA         0.184840               NaN       NaN    NaN  \n",
       "            AutoFeat     0.831073              0.65  0.978251   15.0  \n",
       "            BASE         0.198689               NaN       NaN    NaN  \n",
       "            JoinAll      1.437803               NaN       NaN    NaN  \n",
       "            JoinAll+F    0.834216               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "jannis      ARDA         0.369122               NaN       NaN    NaN  \n",
       "            AutoFeat    19.551859              0.65  1.012449   15.0  \n",
       "            BASE         0.604366               NaN       NaN    NaN  \n",
       "            JoinAll     32.471836               NaN       NaN    NaN  \n",
       "            JoinAll+F   15.586750               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "miniboone   ARDA         0.220012               NaN       NaN    NaN  \n",
       "            AutoFeat    25.557681              0.65  1.146406   15.0  \n",
       "            BASE         1.577182               NaN       NaN    NaN  \n",
       "            JoinAll     45.275554               NaN       NaN    NaN  \n",
       "            JoinAll+F   20.827121               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "school      ARDA         0.711164               NaN       NaN    NaN  \n",
       "            AutoFeat     0.232241              0.65  0.940010   15.0  \n",
       "            BASE         0.150284               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "steel       ARDA         0.217711               NaN       NaN    NaN  \n",
       "            AutoFeat     0.643440              0.65  0.911395   15.0  \n",
       "            BASE         0.126623               NaN       NaN    NaN  \n",
       "            JoinAll      1.053888               NaN       NaN    NaN  \n",
       "            JoinAll+F    0.315611               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  "
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = dataframe[dataframe['algorithm']=='KNeighbors']\n",
    "knn.groupby(by=['data_label', 'approach']).mean('train_time')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "6f1b9f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_time</th>\n",
       "      <th>feature_selection_time</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>train_time</th>\n",
       "      <th>cutoff_threshold</th>\n",
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       "      <th rowspan=\"6\" valign=\"top\">bioresponse</th>\n",
       "      <th>ARDA</th>\n",
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       "      <td>51.701897</td>\n",
       "      <td>0.608750</td>\n",
       "      <td>0.935838</td>\n",
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       "      <td>0.544396</td>\n",
       "      <td>0.956758</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.775837</td>\n",
       "      <td>15.459605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>30.392714</td>\n",
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       "      <td>0.778748</td>\n",
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       "      <td>42.662697</td>\n",
       "      <td>0.548750</td>\n",
       "      <td>0.913428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>AutoFeat</th>\n",
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       "      <td>6.942177</td>\n",
       "      <td>0.938751</td>\n",
       "      <td>76.108269</td>\n",
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       "      <td>0.939158</td>\n",
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       "      <td>14.407986</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>ARDA</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.670000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">eyemove</th>\n",
       "      <th>ARDA</th>\n",
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       "      <td>47.100549</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.904197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>2.060858</td>\n",
       "      <td>0.459549</td>\n",
       "      <td>0.643068</td>\n",
       "      <td>1.601309</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.974232</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>1.100143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.506899</td>\n",
       "      <td>1.100143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>3.311424</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.657359</td>\n",
       "      <td>1.739500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>2.858266</td>\n",
       "      <td>0.011015</td>\n",
       "      <td>0.619251</td>\n",
       "      <td>1.275326</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>30.775000</td>\n",
       "      <td>30.775000</td>\n",
       "      <td>0.548784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">jannis</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>48.002004</td>\n",
       "      <td>47.017040</td>\n",
       "      <td>0.551250</td>\n",
       "      <td>0.984964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>12.376502</td>\n",
       "      <td>2.136092</td>\n",
       "      <td>0.794026</td>\n",
       "      <td>10.240410</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.997592</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>3.423156</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.556335</td>\n",
       "      <td>3.423156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>44.988886</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.791681</td>\n",
       "      <td>9.903637</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>41.117793</td>\n",
       "      <td>0.254710</td>\n",
       "      <td>0.799952</td>\n",
       "      <td>5.777834</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>110.470000</td>\n",
       "      <td>110.470000</td>\n",
       "      <td>0.552080</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">miniboone</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>21.593658</td>\n",
       "      <td>20.608685</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>0.984973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>15.849688</td>\n",
       "      <td>2.376915</td>\n",
       "      <td>0.931387</td>\n",
       "      <td>13.472773</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.039905</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>3.593893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.694161</td>\n",
       "      <td>3.593893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>71.671914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.933048</td>\n",
       "      <td>11.973737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>66.453631</td>\n",
       "      <td>0.364339</td>\n",
       "      <td>0.916524</td>\n",
       "      <td>6.391115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>99.985000</td>\n",
       "      <td>99.985000</td>\n",
       "      <td>0.876729</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">school</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>155.374549</td>\n",
       "      <td>153.726229</td>\n",
       "      <td>0.813380</td>\n",
       "      <td>1.648320</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>12.925998</td>\n",
       "      <td>11.954049</td>\n",
       "      <td>0.697887</td>\n",
       "      <td>0.971950</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.897293</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.804906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.660563</td>\n",
       "      <td>0.804906</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>2216.057500</td>\n",
       "      <td>2216.057500</td>\n",
       "      <td>0.683803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">steel</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>9.356218</td>\n",
       "      <td>8.403135</td>\n",
       "      <td>0.715938</td>\n",
       "      <td>0.953083</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>2.176424</td>\n",
       "      <td>1.036712</td>\n",
       "      <td>0.995501</td>\n",
       "      <td>1.139712</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.023269</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.770668</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.598972</td>\n",
       "      <td>0.770668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>2.538519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.997429</td>\n",
       "      <td>1.238172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>2.249018</td>\n",
       "      <td>0.009243</td>\n",
       "      <td>0.998072</td>\n",
       "      <td>0.939427</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>6.497500</td>\n",
       "      <td>6.497500</td>\n",
       "      <td>0.694087</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>superconduct</th>\n",
       "      <th>MAB</th>\n",
       "      <td>111.512500</td>\n",
       "      <td>111.512500</td>\n",
       "      <td>10.134031</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yprop</th>\n",
       "      <th>MAB</th>\n",
       "      <td>36.135000</td>\n",
       "      <td>36.135000</td>\n",
       "      <td>0.028885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         total_time  feature_selection_time   accuracy  \\\n",
       "data_label   approach                                                    \n",
       "bioresponse  ARDA         52.637735               51.701897   0.608750   \n",
       "             AutoFeat     47.576943               39.627401   0.780568   \n",
       "             BASE          0.956758                0.000000   0.544396   \n",
       "             JoinAll      39.314447                0.000000   0.775837   \n",
       "             JoinAll+F    30.392714                0.074846   0.778748   \n",
       "             MAB          67.472500               67.472500   0.670670   \n",
       "covertype    ARDA         43.576124               42.662697   0.548750   \n",
       "             AutoFeat     83.050447                6.942177   0.938751   \n",
       "             BASE         14.407986                0.000000   0.502744   \n",
       "             JoinAll     299.344607                0.000000   0.953526   \n",
       "             JoinAll+F   271.498218                0.804420   0.911723   \n",
       "             MAB         314.847500              314.847500   0.621604   \n",
       "credit       ARDA          4.129512                3.390326   0.680000   \n",
       "             AutoFeat      1.334074                0.388358   0.747500   \n",
       "             BASE          0.767558                0.000000   0.643750   \n",
       "             JoinAll       1.156948                0.000000   0.735000   \n",
       "             JoinAll+F     1.051348                0.003011   0.745000   \n",
       "             MAB           7.950000                7.950000   0.670000   \n",
       "eyemove      ARDA         48.004746               47.100549   0.505000   \n",
       "             AutoFeat      2.060858                0.459549   0.643068   \n",
       "             BASE          1.100143                0.000000   0.506899   \n",
       "             JoinAll       3.311424                0.000000   0.657359   \n",
       "             JoinAll+F     2.858266                0.011015   0.619251   \n",
       "             MAB          30.775000               30.775000   0.548784   \n",
       "jannis       ARDA         48.002004               47.017040   0.551250   \n",
       "             AutoFeat     12.376502                2.136092   0.794026   \n",
       "             BASE          3.423156                0.000000   0.556335   \n",
       "             JoinAll      44.988886                0.000000   0.791681   \n",
       "             JoinAll+F    41.117793                0.254710   0.799952   \n",
       "             MAB         110.470000              110.470000   0.552080   \n",
       "miniboone    ARDA         21.593658               20.608685   0.877500   \n",
       "             AutoFeat     15.849688                2.376915   0.931387   \n",
       "             BASE          3.593893                0.000000   0.694161   \n",
       "             JoinAll      71.671914                0.000000   0.933048   \n",
       "             JoinAll+F    66.453631                0.364339   0.916524   \n",
       "             MAB          99.985000               99.985000   0.876729   \n",
       "school       ARDA        155.374549              153.726229   0.813380   \n",
       "             AutoFeat     12.925998               11.954049   0.697887   \n",
       "             BASE          0.804906                0.000000   0.660563   \n",
       "             MAB        2216.057500             2216.057500   0.683803   \n",
       "steel        ARDA          9.356218                8.403135   0.715938   \n",
       "             AutoFeat      2.176424                1.036712   0.995501   \n",
       "             BASE          0.770668                0.000000   0.598972   \n",
       "             JoinAll       2.538519                0.000000   0.997429   \n",
       "             JoinAll+F     2.249018                0.009243   0.998072   \n",
       "             MAB           6.497500                6.497500   0.694087   \n",
       "superconduct MAB         111.512500              111.512500  10.134031   \n",
       "yprop        MAB          36.135000               36.135000   0.028885   \n",
       "\n",
       "                        train_time  cutoff_threshold      rank  top_k  \n",
       "data_label   approach                                                  \n",
       "bioresponse  ARDA         0.935838               NaN       NaN    NaN  \n",
       "             AutoFeat     7.949542              0.65  0.854333   15.0  \n",
       "             BASE         0.956758               NaN       NaN    NaN  \n",
       "             JoinAll     15.459605               NaN       NaN    NaN  \n",
       "             JoinAll+F    6.463027               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "covertype    ARDA         0.913428               NaN       NaN    NaN  \n",
       "             AutoFeat    76.108269              0.65  0.939158   15.0  \n",
       "             BASE        14.407986               NaN       NaN    NaN  \n",
       "             JoinAll     77.037547               NaN       NaN    NaN  \n",
       "             JoinAll+F   48.386737               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "credit       ARDA         0.739186               NaN       NaN    NaN  \n",
       "             AutoFeat     0.945716              0.65  0.953445   15.0  \n",
       "             BASE         0.767558               NaN       NaN    NaN  \n",
       "             JoinAll      0.949788               NaN       NaN    NaN  \n",
       "             JoinAll+F    0.841178               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "eyemove      ARDA         0.904197               NaN       NaN    NaN  \n",
       "             AutoFeat     1.601309              0.65  0.974232   15.0  \n",
       "             BASE         1.100143               NaN       NaN    NaN  \n",
       "             JoinAll      1.739500               NaN       NaN    NaN  \n",
       "             JoinAll+F    1.275326               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "jannis       ARDA         0.984964               NaN       NaN    NaN  \n",
       "             AutoFeat    10.240410              0.65  0.997592   15.0  \n",
       "             BASE         3.423156               NaN       NaN    NaN  \n",
       "             JoinAll      9.903637               NaN       NaN    NaN  \n",
       "             JoinAll+F    5.777834               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "miniboone    ARDA         0.984973               NaN       NaN    NaN  \n",
       "             AutoFeat    13.472773              0.65  1.039905   15.0  \n",
       "             BASE         3.593893               NaN       NaN    NaN  \n",
       "             JoinAll     11.973737               NaN       NaN    NaN  \n",
       "             JoinAll+F    6.391115               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "school       ARDA         1.648320               NaN       NaN    NaN  \n",
       "             AutoFeat     0.971950              0.65  0.897293   15.0  \n",
       "             BASE         0.804906               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "steel        ARDA         0.953083               NaN       NaN    NaN  \n",
       "             AutoFeat     1.139712              0.65  1.023269   15.0  \n",
       "             BASE         0.770668               NaN       NaN    NaN  \n",
       "             JoinAll      1.238172               NaN       NaN    NaN  \n",
       "             JoinAll+F    0.939427               NaN       NaN    NaN  \n",
       "             MAB          0.000000               NaN       NaN    NaN  \n",
       "superconduct MAB          0.000000               NaN       NaN    NaN  \n",
       "yprop        MAB          0.000000               NaN       NaN    NaN  "
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_time.groupby(by=['data_label', 'approach']).mean('train_time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "d47e08b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>total_time</th>\n",
       "      <th>feature_selection_time</th>\n",
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       "      <th rowspan=\"6\" valign=\"top\">bioresponse</th>\n",
       "      <th>ARDA</th>\n",
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       "      <td>51.701897</td>\n",
       "      <td>0.526667</td>\n",
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       "      <th>BASE</th>\n",
       "      <td>0.481316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.542940</td>\n",
       "      <td>0.481316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>59.390390</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.745269</td>\n",
       "      <td>35.535548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>35.099364</td>\n",
       "      <td>0.074846</td>\n",
       "      <td>0.745269</td>\n",
       "      <td>11.169677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>41.790000</td>\n",
       "      <td>41.790000</td>\n",
       "      <td>0.535662</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">covertype</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>43.035694</td>\n",
       "      <td>42.662697</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.372997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>11.575961</td>\n",
       "      <td>6.942177</td>\n",
       "      <td>0.683982</td>\n",
       "      <td>4.633783</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.991596</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>1.385387</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505334</td>\n",
       "      <td>1.385387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>228.890242</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.771254</td>\n",
       "      <td>6.583181</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>226.449679</td>\n",
       "      <td>0.804420</td>\n",
       "      <td>0.762073</td>\n",
       "      <td>3.338198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>35.500000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>0.554983</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">credit</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>3.680638</td>\n",
       "      <td>3.390326</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.290312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>0.800559</td>\n",
       "      <td>0.388358</td>\n",
       "      <td>0.713000</td>\n",
       "      <td>0.412201</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.971640</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.411224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.690000</td>\n",
       "      <td>0.411224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>0.773468</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.566308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>0.603274</td>\n",
       "      <td>0.003011</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.393103</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.685000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">eyemove</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>47.474378</td>\n",
       "      <td>47.100549</td>\n",
       "      <td>0.518333</td>\n",
       "      <td>0.373829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>1.286808</td>\n",
       "      <td>0.459549</td>\n",
       "      <td>0.524967</td>\n",
       "      <td>0.827259</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.978251</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.375251</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.494087</td>\n",
       "      <td>0.375251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>2.776435</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.579501</td>\n",
       "      <td>1.204511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>2.222487</td>\n",
       "      <td>0.011015</td>\n",
       "      <td>0.577530</td>\n",
       "      <td>0.639548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>12.440000</td>\n",
       "      <td>12.440000</td>\n",
       "      <td>0.501971</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">jannis</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>47.503077</td>\n",
       "      <td>47.017040</td>\n",
       "      <td>0.531667</td>\n",
       "      <td>0.486037</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>7.088823</td>\n",
       "      <td>2.136092</td>\n",
       "      <td>0.672123</td>\n",
       "      <td>4.952731</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.012449</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.817550</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.499739</td>\n",
       "      <td>0.817550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>42.637084</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.744964</td>\n",
       "      <td>7.551835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>38.770698</td>\n",
       "      <td>0.254710</td>\n",
       "      <td>0.745137</td>\n",
       "      <td>3.430739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>16.910000</td>\n",
       "      <td>16.910000</td>\n",
       "      <td>0.494443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">miniboone</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>21.185937</td>\n",
       "      <td>20.608685</td>\n",
       "      <td>0.843333</td>\n",
       "      <td>0.577253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>9.823765</td>\n",
       "      <td>2.376915</td>\n",
       "      <td>0.864489</td>\n",
       "      <td>7.446850</td>\n",
       "      <td>0.65</td>\n",
       "      <td>1.146406</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.965925</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.669726</td>\n",
       "      <td>0.965925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>75.082443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.899315</td>\n",
       "      <td>15.384265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>66.229652</td>\n",
       "      <td>0.364339</td>\n",
       "      <td>0.874384</td>\n",
       "      <td>6.167136</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>21.310000</td>\n",
       "      <td>21.310000</td>\n",
       "      <td>0.833767</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">school</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>155.030168</td>\n",
       "      <td>153.726229</td>\n",
       "      <td>0.828169</td>\n",
       "      <td>1.303939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>12.399270</td>\n",
       "      <td>11.954049</td>\n",
       "      <td>0.728638</td>\n",
       "      <td>0.445221</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.940010</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.316623</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.738028</td>\n",
       "      <td>0.316623</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>12499.000000</td>\n",
       "      <td>12499.000000</td>\n",
       "      <td>0.653521</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">steel</th>\n",
       "      <th>ARDA</th>\n",
       "      <td>8.898667</td>\n",
       "      <td>8.403135</td>\n",
       "      <td>0.670951</td>\n",
       "      <td>0.495532</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AutoFeat</th>\n",
       "      <td>1.635783</td>\n",
       "      <td>1.036712</td>\n",
       "      <td>0.724079</td>\n",
       "      <td>0.599071</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.911395</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BASE</th>\n",
       "      <td>0.248111</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.668380</td>\n",
       "      <td>0.248111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll</th>\n",
       "      <td>2.352386</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.052038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoinAll+F</th>\n",
       "      <td>1.804025</td>\n",
       "      <td>0.009243</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.494434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MAB</th>\n",
       "      <td>2.590000</td>\n",
       "      <td>2.590000</td>\n",
       "      <td>0.634961</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         total_time  feature_selection_time  accuracy  \\\n",
       "data_label  approach                                                    \n",
       "bioresponse ARDA          52.192118               51.701897  0.526667   \n",
       "            AutoFeat      47.246706               39.627401  0.645706   \n",
       "            BASE           0.481316                0.000000  0.542940   \n",
       "            JoinAll       59.390390                0.000000  0.745269   \n",
       "            JoinAll+F     35.099364                0.074846  0.745269   \n",
       "            MAB           41.790000               41.790000  0.535662   \n",
       "covertype   ARDA          43.035694               42.662697  0.475000   \n",
       "            AutoFeat      11.575961                6.942177  0.683982   \n",
       "            BASE           1.385387                0.000000  0.505334   \n",
       "            JoinAll      228.890242                0.000000  0.771254   \n",
       "            JoinAll+F    226.449679                0.804420  0.762073   \n",
       "            MAB           35.500000               35.500000  0.554983   \n",
       "credit      ARDA           3.680638                3.390326  0.690000   \n",
       "            AutoFeat       0.800559                0.388358  0.713000   \n",
       "            BASE           0.411224                0.000000  0.690000   \n",
       "            JoinAll        0.773468                0.000000  0.750000   \n",
       "            JoinAll+F      0.603274                0.003011  0.780000   \n",
       "            MAB            4.000000                4.000000  0.685000   \n",
       "eyemove     ARDA          47.474378               47.100549  0.518333   \n",
       "            AutoFeat       1.286808                0.459549  0.524967   \n",
       "            BASE           0.375251                0.000000  0.494087   \n",
       "            JoinAll        2.776435                0.000000  0.579501   \n",
       "            JoinAll+F      2.222487                0.011015  0.577530   \n",
       "            MAB           12.440000               12.440000  0.501971   \n",
       "jannis      ARDA          47.503077               47.017040  0.531667   \n",
       "            AutoFeat       7.088823                2.136092  0.672123   \n",
       "            BASE           0.817550                0.000000  0.499739   \n",
       "            JoinAll       42.637084                0.000000  0.744964   \n",
       "            JoinAll+F     38.770698                0.254710  0.745137   \n",
       "            MAB           16.910000               16.910000  0.494443   \n",
       "miniboone   ARDA          21.185937               20.608685  0.843333   \n",
       "            AutoFeat       9.823765                2.376915  0.864489   \n",
       "            BASE           0.965925                0.000000  0.669726   \n",
       "            JoinAll       75.082443                0.000000  0.899315   \n",
       "            JoinAll+F     66.229652                0.364339  0.874384   \n",
       "            MAB           21.310000               21.310000  0.833767   \n",
       "school      ARDA         155.030168              153.726229  0.828169   \n",
       "            AutoFeat      12.399270               11.954049  0.728638   \n",
       "            BASE           0.316623                0.000000  0.738028   \n",
       "            MAB        12499.000000            12499.000000  0.653521   \n",
       "steel       ARDA           8.898667                8.403135  0.670951   \n",
       "            AutoFeat       1.635783                1.036712  0.724079   \n",
       "            BASE           0.248111                0.000000  0.668380   \n",
       "            JoinAll        2.352386                0.000000  1.000000   \n",
       "            JoinAll+F      1.804025                0.009243  1.000000   \n",
       "            MAB            2.590000                2.590000  0.634961   \n",
       "\n",
       "                       train_time  cutoff_threshold      rank  top_k  \n",
       "data_label  approach                                                  \n",
       "bioresponse ARDA         0.490221               NaN       NaN    NaN  \n",
       "            AutoFeat     7.619305              0.65  0.874201   15.0  \n",
       "            BASE         0.481316               NaN       NaN    NaN  \n",
       "            JoinAll     35.535548               NaN       NaN    NaN  \n",
       "            JoinAll+F   11.169677               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "covertype   ARDA         0.372997               NaN       NaN    NaN  \n",
       "            AutoFeat     4.633783              0.65  0.991596   15.0  \n",
       "            BASE         1.385387               NaN       NaN    NaN  \n",
       "            JoinAll      6.583181               NaN       NaN    NaN  \n",
       "            JoinAll+F    3.338198               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "credit      ARDA         0.290312               NaN       NaN    NaN  \n",
       "            AutoFeat     0.412201              0.65  0.971640   15.0  \n",
       "            BASE         0.411224               NaN       NaN    NaN  \n",
       "            JoinAll      0.566308               NaN       NaN    NaN  \n",
       "            JoinAll+F    0.393103               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "eyemove     ARDA         0.373829               NaN       NaN    NaN  \n",
       "            AutoFeat     0.827259              0.65  0.978251   15.0  \n",
       "            BASE         0.375251               NaN       NaN    NaN  \n",
       "            JoinAll      1.204511               NaN       NaN    NaN  \n",
       "            JoinAll+F    0.639548               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "jannis      ARDA         0.486037               NaN       NaN    NaN  \n",
       "            AutoFeat     4.952731              0.65  1.012449   15.0  \n",
       "            BASE         0.817550               NaN       NaN    NaN  \n",
       "            JoinAll      7.551835               NaN       NaN    NaN  \n",
       "            JoinAll+F    3.430739               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "miniboone   ARDA         0.577253               NaN       NaN    NaN  \n",
       "            AutoFeat     7.446850              0.65  1.146406   15.0  \n",
       "            BASE         0.965925               NaN       NaN    NaN  \n",
       "            JoinAll     15.384265               NaN       NaN    NaN  \n",
       "            JoinAll+F    6.167136               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "school      ARDA         1.303939               NaN       NaN    NaN  \n",
       "            AutoFeat     0.445221              0.65  0.940010   15.0  \n",
       "            BASE         0.316623               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  \n",
       "steel       ARDA         0.495532               NaN       NaN    NaN  \n",
       "            AutoFeat     0.599071              0.65  0.911395   15.0  \n",
       "            BASE         0.248111               NaN       NaN    NaN  \n",
       "            JoinAll      1.052038               NaN       NaN    NaN  \n",
       "            JoinAll+F    0.494434               NaN       NaN    NaN  \n",
       "            MAB          0.000000               NaN       NaN    NaN  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = dataframe[dataframe['algorithm']=='LinearModel']\n",
    "lr.groupby(by=['data_label', 'approach']).mean('train_time')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5885c34",
   "metadata": {},
   "source": [
    "# Ablation - Relevance, redundancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "9a214fad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['RandomForest', 'LightGBM', 'ExtraTrees', 'XGBoost', 'KNeighbors', 'LinearModel']\n",
      "['steel', 'jannis', 'miniboone', 'eyemove', 'bioresponse', 'credit', 'covertype', 'school']\n",
      "['AutoFeat-Pearson-MRMR', 'AutoFeat-Pearson-JMI', 'AutoFeat-Spearman-JMI', 'AutoFeat_Red', 'AutoFeat_Rel']\n"
     ]
    }
   ],
   "source": [
    "folder_name = os.path.abspath(os.path.abspath(''))\n",
    "\n",
    "dataset_path = \"results/revision/final/benchmark-final\"\n",
    "filename = \"results_tfd_ablation.csv\"\n",
    "autofeat_filename = \"results_autofeat.csv\"\n",
    "\n",
    "\n",
    "# Read data\n",
    "ablation_df = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{filename}\"), header=0)\n",
    "autofeat_df = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{autofeat_filename}\"), header=0)\n",
    "\n",
    "ablation_df.drop(columns=['join_time', 'depth', 'redundancy_threshold'], inplace=True)\n",
    "autofeat_df.drop(columns=['join_time', 'depth', 'redundancy_threshold'], inplace=True)\n",
    "autofeat_df.loc[autofeat_df['approach']=='TFD_BFS', 'approach'] = 'Spearman-MRMR'\n",
    "\n",
    "\n",
    "# Get globnal variables \n",
    "algs = list(ablation_df['algorithm'].unique())\n",
    "print(algs)\n",
    "\n",
    "tables = list(ablation_df['data_label'].unique())\n",
    "print(tables)\n",
    "\n",
    "approach = list(ablation_df['approach'].unique())\n",
    "print(approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "5e48a39c",
   "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>data_label</th>\n",
       "      <th>approach</th>\n",
       "      <th>total_time</th>\n",
       "      <th>feature_selection_time</th>\n",
       "      <th>train_time</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>bioresponse</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>54.803325</td>\n",
       "      <td>39.627401</td>\n",
       "      <td>15.175924</td>\n",
       "      <td>0.784571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>covertype</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>160.609625</td>\n",
       "      <td>6.942177</td>\n",
       "      <td>153.667447</td>\n",
       "      <td>0.967452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>credit</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.824797</td>\n",
       "      <td>0.388358</td>\n",
       "      <td>0.436439</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>eyemove</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>1.567921</td>\n",
       "      <td>0.459549</td>\n",
       "      <td>1.108371</td>\n",
       "      <td>0.658344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>jannis</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>9.068763</td>\n",
       "      <td>2.136092</td>\n",
       "      <td>6.932671</td>\n",
       "      <td>0.800278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>miniboone</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>15.512172</td>\n",
       "      <td>2.376915</td>\n",
       "      <td>13.135257</td>\n",
       "      <td>0.939726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>school</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>12.351127</td>\n",
       "      <td>11.954049</td>\n",
       "      <td>0.397079</td>\n",
       "      <td>0.709859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>steel</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>1.667572</td>\n",
       "      <td>1.036712</td>\n",
       "      <td>0.630860</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_label  approach  total_time  feature_selection_time  train_time  \\\n",
       "327  bioresponse  AutoFeat   54.803325               39.627401   15.175924   \n",
       "477    covertype  AutoFeat  160.609625                6.942177  153.667447   \n",
       "393       credit  AutoFeat    0.824797                0.388358    0.436439   \n",
       "273      eyemove  AutoFeat    1.567921                0.459549    1.108371   \n",
       "135       jannis  AutoFeat    9.068763                2.136092    6.932671   \n",
       "243    miniboone  AutoFeat   15.512172                2.376915   13.135257   \n",
       "489       school  AutoFeat   12.351127               11.954049    0.397079   \n",
       "9          steel  AutoFeat    1.667572                1.036712    0.630860   \n",
       "\n",
       "     accuracy  \n",
       "327  0.784571  \n",
       "477  0.967452  \n",
       "393  0.750000  \n",
       "273  0.658344  \n",
       "135  0.800278  \n",
       "243  0.939726  \n",
       "489  0.709859  \n",
       "9    1.000000  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_idx = ablation_df[ablation_df['algorithm'] == 'XGBoost'].groupby(by=['data_label', 'approach'])['accuracy'].idxmax()\n",
    "max_acc = ablation_df.loc[a_idx]\n",
    "max_acc = max_acc[['data_label', 'approach', 'total_time', 'feature_selection_time', 'train_time', 'accuracy']]\n",
    "max_acc\n",
    "\n",
    "\n",
    "af_idx = autofeat_df[(autofeat_df['algorithm'] == 'XGBoost')].groupby(by=['data_label', 'approach'])['accuracy'].idxmax()\n",
    "max_acc_af = autofeat_df.loc[af_idx]\n",
    "max_acc_af = max_acc_af[['data_label', 'approach', 'total_time','feature_selection_time', 'train_time', 'accuracy']]\n",
    "max_acc_af"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "2cb00e7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = pd.concat([max_acc_af, max_acc])\n",
    "plot_data.loc[plot_data['approach']=='AutoFeat', 'approach'] = 'Spearman-MRMR \\n (default)'\n",
    "plot_data.loc[plot_data['approach']=='AutoFeat-Pearson-JMI', 'approach'] = 'Pearson-JMI'\n",
    "plot_data.loc[plot_data['approach']=='AutoFeat-Pearson-MRMR', 'approach'] = 'Pearson-MRMR'\n",
    "plot_data.loc[plot_data['approach']=='AutoFeat-Spearman-JMI', 'approach'] = 'Spearman-JMI'\n",
    "plot_data.loc[plot_data['approach']=='AutoFeat_Red', 'approach'] = 'MRMR'\n",
    "plot_data.loc[plot_data['approach']=='AutoFeat_Rel', 'approach'] = 'Spearman'\n",
    "\n",
    "sns.set(font_scale=1.5, style=\"whitegrid\")\n",
    "pal = sns.color_palette(\"pastel\")\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "f, axs = plt.subplots(3, 3, figsize=(10, 6))\n",
    "plt.subplots_adjust(hspace=0.6, wspace=0.4)\n",
    "\n",
    "size=170\n",
    "\n",
    "a00 = plot_data[plot_data['data_label'] == 'jannis']\n",
    "sns.scatterplot(data=a00, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[0][0])\n",
    "axs[0][0].get_legend().remove()\n",
    "axs[0][0].set_xlabel('')\n",
    "axs[0][0].set_ylabel('')\n",
    "axs[0][0].set_title('jannis')\n",
    "\n",
    "a01 = plot_data[plot_data['data_label'] == 'covertype']\n",
    "sns.scatterplot(data=a01, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[0][1])\n",
    "axs[0][1].get_legend().remove()\n",
    "axs[0][1].set_xlabel('')\n",
    "axs[0][1].set_ylabel('')\n",
    "axs[0][1].set_title('covertype')\n",
    "\n",
    "a02 = plot_data[plot_data['data_label'] == 'credit']\n",
    "sns.scatterplot(data=a02, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[0][2])\n",
    "axs[0][2].get_legend().remove()\n",
    "axs[0][2].set_xlabel('')\n",
    "axs[0][2].set_ylabel('')\n",
    "axs[0][2].set_title('credit')\n",
    "\n",
    "\n",
    "\n",
    "a10 = plot_data[plot_data['data_label'] == 'eyemove']\n",
    "sns.scatterplot(data=a10, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[1][0])\n",
    "axs[1][0].get_legend().remove()\n",
    "axs[1][0].set_xlabel('')\n",
    "axs[1][0].set_ylabel('Accuracy')\n",
    "axs[1][0].set_title('eyemove')\n",
    "\n",
    "\n",
    "a12 = plot_data[plot_data['data_label'] == 'bioresponse']\n",
    "sns.scatterplot(data=a12, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[1][1])\n",
    "axs[1][1].get_legend().remove()\n",
    "axs[1][1].set_xlabel('')\n",
    "axs[1][1].set_ylabel('')\n",
    "axs[1][1].set_title('bioresponse')\n",
    "\n",
    "\n",
    "a20 = plot_data[plot_data['data_label'] == 'miniboone']\n",
    "sns.scatterplot(data=a20, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[2][0])\n",
    "axs[2][0].get_legend().remove()\n",
    "axs[2][0].set_xlabel('')\n",
    "axs[2][0].set_ylabel('')\n",
    "axs[2][0].set_title('miniboone')\n",
    "\n",
    "\n",
    "a21 = plot_data[plot_data['data_label'] == 'school']\n",
    "sns.scatterplot(data=a21, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[2][1])\n",
    "axs[2][1].get_legend().remove()\n",
    "axs[2][1].set_xlabel('Total time (seconds)')\n",
    "axs[2][1].set_ylabel('')\n",
    "axs[2][1].set_title('school')\n",
    "\n",
    "\n",
    "a22 = plot_data[plot_data['data_label'] == 'steel']\n",
    "sns.scatterplot(data=a22, x='total_time', y='accuracy', hue='approach', \n",
    "                style='approach', s=size, ax=axs[2][2])\n",
    "axs[2][2].set_xlabel('')\n",
    "axs[2][2].set_ylabel('')\n",
    "axs[2][2].set_title('steel')\n",
    "\n",
    "axs[1][2].remove()\n",
    "\n",
    "hh, ll = axs[0][2].get_legend_handles_labels()\n",
    "plt.legend(hh, ll, bbox_to_anchor=(1, 2.9), ncol=1,  facecolor='white',\n",
    "           framealpha=1, fontsize=\"x-small\", title_fontsize='small')\n",
    "\n",
    "plt.savefig('plots/revision/ablation_rel_red.pdf', dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab22d349",
   "metadata": {},
   "source": [
    "# Lake - Acc + feature selection time - Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "e3d89f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['LightGBM', 'RandomForest', 'ExtraTrees', 'XGBoost', 'KNeighbors', 'LinearModel']\n",
      "['credit', 'steel', 'jannis', 'miniboone', 'covertype', 'eyemove', 'bioresponse', 'school', 'air', 'yprop', 'superconduct']\n",
      "['AutoFeat', 'ARDA', 'MAB', 'BASE']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "folder_name = os.path.abspath(os.path.abspath(''))\n",
    "\n",
    "dataset_path = \"results/revision/final/lake-final\"\n",
    "tfd_filename = \"results_autofeat_path.csv\"\n",
    "arda_filename = \"results_arda.csv\"\n",
    "mab_filename = \"results_mab.csv\"\n",
    "base_filename = \"results_base.csv\"\n",
    "\n",
    "\n",
    "# Read data\n",
    "df_tfd = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{tfd_filename}\"), header=0)\n",
    "df_arda = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{arda_filename}\"), header=0)\n",
    "df_mab = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{mab_filename}\"), header=0)\n",
    "df_base = pd.read_csv(os.path.join(folder_name, f\"{dataset_path}/{base_filename}\"), header=0)\n",
    "\n",
    "\n",
    "# Create a big dataframe\n",
    "dataframe = pd.concat([df_tfd, df_arda, df_mab, df_base])\n",
    "\n",
    "\n",
    "# Remove unused columns\n",
    "dataframe.drop(columns=['join_time', 'depth', 'redundancy_threshold'], inplace=True)\n",
    "\n",
    "\n",
    "# Process columns\n",
    "# number_of_features, features = parse_feature_importance(dataframe)\n",
    "# dataframe['number_features_importance'] = number_of_features\n",
    "\n",
    "# number_of_features, jp_features = parse_join_path_features(dataframe)\n",
    "# dataframe['number_join_path_features'] = number_of_features\n",
    "\n",
    "# nr_common_features, difference = determine_common_features(dataframe)\n",
    "# dataframe['nr_common_features'] = nr_common_features\n",
    "# dataframe['different_features'] = difference\n",
    "\n",
    "dataframe.loc[dataframe['approach']=='TFD_BFS', 'approach'] = 'AutoFeat'\n",
    "\n",
    "\n",
    "# Get globnal variables \n",
    "algs = list(dataframe['algorithm'].unique())\n",
    "print(algs)\n",
    "\n",
    "tables = list(dataframe['data_label'].unique())\n",
    "print(tables)\n",
    "\n",
    "approach = list(dataframe['approach'].unique())\n",
    "print(approach)\n",
    "\n",
    "\n",
    "dataframe.reset_index(inplace=True)\n",
    "dataframe.drop(columns=['index'], inplace=True)\n",
    "dataframe['feature_selection_time'] = dataframe['feature_selection_time'].fillna(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "e2c98190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style=\"whitegrid\")\n",
    "pal = sns.color_palette(\"pastel\")\n",
    "pal2 = sns.color_palette('tab10')\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n",
    "\n",
    "# df_basic.sort_values('data_label')\n",
    "\n",
    "time_variable = 'feature_selection_time'\n",
    "df = dataframe[dataframe['algorithm'].isin(['RandomForest', 'LightGBM', 'ExtraTrees', 'XGBoost'])]\n",
    "\n",
    "# total_time = dataframe.groupby(['data_label', 'approach', 'algorithm', time_variable])[['accuracy']].idxmax()\n",
    "total_time = df.loc[df.groupby(by=['data_label', 'approach', 'algorithm'])['accuracy'].idxmax()]\n",
    "taux = total_time[~total_time['data_label'].isin(['air', 'superconduct', 'yprop'])]\n",
    "\n",
    "accuracy_cls = df.loc[df.groupby(by=['data_label', 'approach', 'algorithm'])['accuracy'].idxmax()]\n",
    "\n",
    "# accuracy_cls = dataframe.groupby(['data_label', 'approach', 'algorithm'])[['accuracy']].max().reset_index()\n",
    "aux = accuracy_cls[~accuracy_cls['data_label'].isin(['air', 'superconduct', 'yprop'])]\n",
    "\n",
    "\n",
    "ax1 = sns.barplot(x=\"data_label\", y='total_time', hue=\"approach\", \n",
    "                data=taux, \n",
    "                palette=[pal[4], pal[0], pal[1], pal[2]], hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'], ax=ax1)\n",
    "#                 palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "#                   hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAllF', 'AutoFeat'], ax=ax1)\n",
    "\n",
    "ax1 = sns.barplot(x=\"data_label\", y=time_variable, hue=\"approach\", \n",
    "                data=taux, \n",
    "                palette=[pal2[4], pal2[0], pal2[1], pal2[2]], hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'], ax=ax1)\n",
    "#                 palette=[pal2[4], pal2[0], pal2[1], pal2[5], pal2[6], pal2[2]], \n",
    "#                   hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAllF', 'AutoFeat'], ax=ax1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax1.set_title(\"Average total time and feature selection time per dataset\")\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Seconds')\n",
    "ax1.get_legend().remove()\n",
    "ax1.set_yscale('symlog')\n",
    "# ax1.set_ylim()\n",
    "locmin = matplotlib.ticker.LogLocator(base=10, subs=np.arange(0.1,1,0.1),numticks=10)  \n",
    "ax1.yaxis.set_minor_locator(locmin)\n",
    "ax1.yaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())\n",
    "ax1.grid(True,which=\"both\",ls=\"--\",c='lightgray')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ax2 = sns.barplot(x=\"data_label\", y=\"accuracy\", hue=\"approach\", \n",
    "                data=aux, \n",
    "                palette=[pal[4], pal[0], pal[1], pal[2]], hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'], ax=ax2)\n",
    "#                   palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "#                   hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAllF', 'AutoFeat'], ax=ax2)\n",
    "\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.set_title(\"Average accuracy per dataset\")  \n",
    "ax2.set_yticks(np.arange(0, 1.1, 0.25))\n",
    "\n",
    "\n",
    "autofeat_tables = [1, 8, 2, 4, 1, 2, 1, 14]\n",
    "mab_tables = [3, 3, 2, 3, 3, 3, 2, 3]\n",
    "arda_tables = [3, 3, 2, 3, 3, 3, 16, 3]\n",
    "total_tables = [39, 11, 4, 5, 11, 14, 'X', 14]\n",
    "\n",
    "ax2.bar_label(ax2.containers[1], labels=arda_tables, label_type='center', padding=15)\n",
    "ax2.bar_label(ax2.containers[2], labels=mab_tables, label_type='center')\n",
    "# ax2.bar_label(ax2.containers[3], labels=total_tables, label_type='edge')\n",
    "# ax2.bar_label(ax2.containers[4], labels=total_tables, label_type='center')\n",
    "ax2.bar_label(ax2.containers[3], labels=autofeat_tables, label_type='center', padding=10)\n",
    "\n",
    "ax2.annotate('',xy=(0.5, 0.885),xytext=(-0.5, 0.885), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(1.5, 0.99),xytext=(0.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(2.5, 0.99),xytext=(1.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(3.5, 0.894),xytext=(2.5, 0.894), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(4.5, 0.875),xytext=(3.5, 0.875), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(5.5, 0.9465),xytext=(4.5, 0.9465), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(6.5, 0.831),xytext=(5.5, 0.831), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(7.5, 1.0),xytext=(6.5, 1.0), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "\n",
    "\n",
    "\n",
    "loc, labels = plt.xticks()\n",
    "hh, ll = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "ax2.set_xticklabels(labels, rotation=30)\n",
    "plt.legend(hh, ll, bbox_to_anchor=(0.85, -0.45), ncol=6, title=\"Approach\", fontsize='x-small', title_fontsize='small')\n",
    "\n",
    "plt.savefig('plots/revision/lake-all.pdf', dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5030b5",
   "metadata": {},
   "source": [
    "# Lake - KNN + Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "e298a72e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style=\"whitegrid\")\n",
    "pal = sns.color_palette(\"pastel\")\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 6), sharex=True)\n",
    "\n",
    "\n",
    "timevar = 'total_time'\n",
    "df_time = dataframe[dataframe['algorithm']=='KNeighbors']\n",
    "# df_time = df[df['algorithm']=='LinearModel']\n",
    "\n",
    "accuracy_cls = df_time.groupby(['data_label', 'approach'])['accuracy'].max().reset_index()\n",
    "\n",
    "ax1 = sns.barplot(x=\"data_label\", y=\"accuracy\", hue=\"approach\", \n",
    "                data=accuracy_cls, \n",
    "                  palette=[pal[4], pal[0], pal[1], pal[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'],\n",
    "                 ax=ax1)\n",
    "#                   palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "#                   hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAll-Filter', 'AutoFeat'], ax=ax2)\n",
    "\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Accuracy')\n",
    "ax1.set_title(\"KNN - Accuracy per dataset\")  \n",
    "ax1.get_legend().remove()\n",
    "ax1.set_yticks(np.arange(0, 1.1, 0.25))\n",
    "\n",
    "autofeat_tables = [1, 8, 2, 4, 1, 2, 1, 14]\n",
    "mab_tables = [3, 3, 2, 3, 3, 3, 2, 3]\n",
    "arda_tables = [3, 3, 2, 3, 3, 3, 16, 3]\n",
    "total_tables = [39, 11, 4, 5, 11, 14, 'X', 14]\n",
    "\n",
    "ax1.bar_label(ax1.containers[1], labels=arda_tables, label_type='center', padding=15)\n",
    "ax1.bar_label(ax1.containers[2], labels=mab_tables, label_type='center')\n",
    "# ax2.bar_label(ax2.containers[3], labels=total_tables, label_type='edge')\n",
    "# ax2.bar_label(ax2.containers[4], labels=total_tables, label_type='center')\n",
    "ax1.bar_label(ax1.containers[3], labels=autofeat_tables, label_type='center', padding=10)\n",
    "\n",
    "ax1.annotate('',xy=(0.5, 0.885),xytext=(-0.5, 0.885), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(1.5, 0.99),xytext=(0.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(2.5, 0.99),xytext=(1.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(3.5, 0.894),xytext=(2.5, 0.894), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(4.5, 0.875),xytext=(3.5, 0.875), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(5.5, 0.9465),xytext=(4.5, 0.9465), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(6.5, 0.831),xytext=(5.5, 0.831), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax1.annotate('',xy=(7.5, 1.0),xytext=(6.5, 1.0), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "\n",
    "# loc, labels = plt.xticks()\n",
    "# hh, ll = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "# ax2.set_xticklabels(labels, rotation=30)\n",
    "# plt.legend(hh, ll, bbox_to_anchor=(0.9, -0.4), ncol=6, title=\"Approach\", fontsize='small')\n",
    "\n",
    "\n",
    "df_time = dataframe[dataframe['algorithm']=='LinearModel']\n",
    "\n",
    "accuracy_cls = df_time.groupby(['data_label', 'approach'])['accuracy'].max().reset_index()\n",
    "\n",
    "ax2 = sns.barplot(x=\"data_label\", y=\"accuracy\", hue=\"approach\", \n",
    "                data=accuracy_cls,\n",
    "                  palette=[pal[4], pal[0], pal[1], pal[2]], \n",
    "                  hue_order=['BASE', 'ARDA', 'MAB', 'AutoFeat'],\n",
    "#                  palette=[pal[4], pal[0], pal[1], pal[5], pal[6], pal[2]], \n",
    "#                   hue_order=['BASE', 'ARDA', 'MAB', 'JoinAll', 'JoinAllF', 'AutoFeat'], ax=ax2)\n",
    "                 ax=ax2)\n",
    "\n",
    "ax2.set_xlabel('')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.set_title(\"Linear - Accuracy per dataset\") \n",
    "ax2.set_yticks(np.arange(0, 1.1, 0.25))\n",
    "\n",
    "\n",
    "ax2.bar_label(ax2.containers[1], labels=arda_tables, label_type='center', padding=15)\n",
    "ax2.bar_label(ax2.containers[2], labels=mab_tables, label_type='center')\n",
    "# ax2.bar_label(ax2.containers[3], labels=total_tables, label_type='edge')\n",
    "# ax2.bar_label(ax2.containers[4], labels=total_tables, label_type='center')\n",
    "ax2.bar_label(ax2.containers[3], labels=autofeat_tables, label_type='center', padding=10)\n",
    "\n",
    "ax2.annotate('',xy=(0.5, 0.885),xytext=(-0.5, 0.885), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(1.5, 0.99),xytext=(0.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(2.5, 0.99),xytext=(1.5, 0.99), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(3.5, 0.894),xytext=(2.5, 0.894), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(4.5, 0.875),xytext=(3.5, 0.875), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(5.5, 0.9465),xytext=(4.5, 0.9465), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(6.5, 0.831),xytext=(5.5, 0.831), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "ax2.annotate('',xy=(7.5, 1.0),xytext=(6.5, 1.0), arrowprops=dict(arrowstyle=\"-\", color='black', lw=2))\n",
    "\n",
    "loc, labels = plt.xticks()\n",
    "hh, ll = plt.gca().get_legend_handles_labels()\n",
    "\n",
    "ax2.set_xticklabels(labels, rotation=30)\n",
    "plt.legend(hh, ll, bbox_to_anchor=(0.8, -0.45), ncol=6, title=\"Approach\", fontsize='x-small', title_fontsize='small')\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('plots/revision/lake_linear_knn_accuracy.pdf', dpi=300, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "a6e4ff6e",
   "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>data_label</th>\n",
       "      <th>approach</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>all</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.506164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bioresponse</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.498333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bioresponse</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.497817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bioresponse</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.509461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bioresponse</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.503639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>covertype</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.496667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>covertype</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.500389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>covertype</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.502289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>covertype</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.497923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>credit</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>credit</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>credit</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>credit</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.685000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>eyemove</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>eyemove</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.496058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>eyemove</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.504599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>eyemove</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.493430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>jannis</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.521667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>jannis</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.502431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>jannis</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.506339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>jannis</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.505297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>miniboone</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.483333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>miniboone</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.505753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>miniboone</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.507808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>miniboone</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.506164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>school</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.664789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>school</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.645070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>school</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.645070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>school</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.630380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>steel</td>\n",
       "      <td>ARDA</td>\n",
       "      <td>0.619537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>steel</td>\n",
       "      <td>AutoFeat</td>\n",
       "      <td>0.632391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>steel</td>\n",
       "      <td>BASE</td>\n",
       "      <td>0.606684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>steel</td>\n",
       "      <td>MAB</td>\n",
       "      <td>0.462725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data_label  approach  accuracy\n",
       "0           all       MAB  0.506164\n",
       "1   bioresponse      ARDA  0.498333\n",
       "2   bioresponse  AutoFeat  0.497817\n",
       "3   bioresponse      BASE  0.509461\n",
       "4   bioresponse       MAB  0.503639\n",
       "5     covertype      ARDA  0.496667\n",
       "6     covertype  AutoFeat  0.500389\n",
       "7     covertype      BASE  0.502289\n",
       "8     covertype       MAB  0.497923\n",
       "9        credit      ARDA  0.630000\n",
       "10       credit  AutoFeat  0.645000\n",
       "11       credit      BASE  0.590000\n",
       "12       credit       MAB  0.685000\n",
       "13      eyemove      ARDA  0.505000\n",
       "14      eyemove  AutoFeat  0.496058\n",
       "15      eyemove      BASE  0.504599\n",
       "16      eyemove       MAB  0.493430\n",
       "17       jannis      ARDA  0.521667\n",
       "18       jannis  AutoFeat  0.502431\n",
       "19       jannis      BASE  0.506339\n",
       "20       jannis       MAB  0.505297\n",
       "21    miniboone      ARDA  0.483333\n",
       "22    miniboone  AutoFeat  0.505753\n",
       "23    miniboone      BASE  0.507808\n",
       "24    miniboone       MAB  0.506164\n",
       "25       school      ARDA  0.664789\n",
       "26       school  AutoFeat  0.645070\n",
       "27       school      BASE  0.645070\n",
       "28       school       MAB  0.630380\n",
       "29        steel      ARDA  0.619537\n",
       "30        steel  AutoFeat  0.632391\n",
       "31        steel      BASE  0.606684\n",
       "32        steel       MAB  0.462725"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[dataframe['algorithm']=='KNeighbors'].groupby(['data_label', 'approach'])['accuracy'].max().reset_index()"
   ]
  }
 ],
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