RESULTS_MAIN_W_TABNET.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27f40ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import os\n",
    "import sys\n",
    "from visualization import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32583c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Aggregate run results\n",
    "# !{sys.executable} agg_stats_w_tabnet.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b16eb129",
   "metadata": {},
   "outputs": [],
   "source": [
    "nm_d = {\n",
    "    \n",
    "    'otto': 'Otto (9 classes)',\n",
    "    'sf-crime': 'SF-Crime (39 classes)',\n",
    "    \n",
    "    'helena': 'Helena (100 classes)',\n",
    "    'moa': 'MoA (206 labels)',\n",
    "    'dionis': 'Dionis (355 classes)',\n",
    "    \n",
    "    'delicious': 'Delicious (983 labels)',\n",
    "    'mediamill': 'Mediamill (101 labels)',\n",
    "    \n",
    "    'rf1': 'RF1 (8 tasks)',\n",
    "    'scm20d': 'SCM20D (16 tasks)',\n",
    "    \n",
    "}\n",
    "\n",
    "st_d = {\n",
    "    'xgb': 'XGBoost (one-vs-all)',\n",
    "    'cb': 'CatBoost (multioutput)',\n",
    "    'pb': 'SketchBoost Full',\n",
    "    'random': 'Random Sampling',\n",
    "    'best': 'Top Outputs',\n",
    "    'proj': 'Random Projection',   \n",
    "    'tabnet': 'Tabnet'\n",
    "}\n",
    "\n",
    "HYPERPARAMS = 'baselines_and_params.pkl'\n",
    "\n",
    "dataset_order = ['otto', 'dionis', 'helena', 'sf-crime', 'moa', 'scm20d', 'rf1', 'delicious', 'mediamill',]\n",
    "\n",
    "strategy_order =  ['best', 'random', 'proj',]\n",
    "baselines = ['pb', 'cb', 'xgb', 'tabnet', ]\n",
    "k_limit = 20\n",
    "\n",
    "OUTPUT_DIR = 'output/OUTPUT_MAIN'\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "981c9410",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "detailed_plot(dataset_order, \n",
    "              strategy_order, \n",
    "              'test_score', st_d, nm_d, baselines=[x for x in baselines if x != 'tabnet'], \n",
    "              alias='Test Error', output_dir=OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "368b7c14",
   "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>Dataset</th>\n",
       "      <th>SketchBoost Full</th>\n",
       "      <th>CatBoost (multioutput)</th>\n",
       "      <th>XGBoost (one-vs-all)</th>\n",
       "      <th>Tabnet</th>\n",
       "      <th>Top Outputs</th>\n",
       "      <th>Random Sampling</th>\n",
       "      <th>Random Projection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Otto (9 classes)</td>\n",
       "      <td>0.4697+-0.003</td>\n",
       "      <td>0.4658+-0.0033</td>\n",
       "      <td>0.4599+-0.0028</td>\n",
       "      <td>0.5363+-0.0064</td>\n",
       "      <td>0.4715+-0.0035</td>\n",
       "      <td>0.4636+-0.0026</td>\n",
       "      <td>0.4566+-0.0023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dionis (355 classes)</td>\n",
       "      <td>0.3114+-0.0009</td>\n",
       "      <td>0.3085+-0.001</td>\n",
       "      <td>0.3502+-0.002</td>\n",
       "      <td>0.4753+-0.0126</td>\n",
       "      <td>0.3146+-0.0011</td>\n",
       "      <td>0.304+-0.0014</td>\n",
       "      <td>0.2848+-0.0012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helena (100 classes)</td>\n",
       "      <td>2.5865+-0.0025</td>\n",
       "      <td>2.5698+-0.0025</td>\n",
       "      <td>2.5889+-0.0032</td>\n",
       "      <td>2.7197+-0.0235</td>\n",
       "      <td>2.5923+-0.0024</td>\n",
       "      <td>2.5693+-0.0022</td>\n",
       "      <td>2.5673+-0.0026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SF-Crime (39 classes)</td>\n",
       "      <td>2.2067+-0.0003</td>\n",
       "      <td>2.2036+-0.0005</td>\n",
       "      <td>2.2208+-0.0008</td>\n",
       "      <td>2.4819+-0.02</td>\n",
       "      <td>2.207+-0.0005</td>\n",
       "      <td>2.2037+-0.0004</td>\n",
       "      <td>2.2038+-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MoA (206 labels)</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>0.0161+-0.0</td>\n",
       "      <td>0.0166+-0.0</td>\n",
       "      <td>0.0193+-0.0003</td>\n",
       "      <td>0.0163+-0.0</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCM20D (16 tasks)</td>\n",
       "      <td>91.0142+-0.3397</td>\n",
       "      <td>90.9814+-0.3652</td>\n",
       "      <td>89.1045+-0.495</td>\n",
       "      <td>87.3655+-1.3316</td>\n",
       "      <td>88.7442+-0.6346</td>\n",
       "      <td>86.2964+-0.4398</td>\n",
       "      <td>85.8061+-0.5534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RF1 (8 tasks)</td>\n",
       "      <td>1.1687+-0.0835</td>\n",
       "      <td>0.8975+-0.0384</td>\n",
       "      <td>0.925+-0.0308</td>\n",
       "      <td>3.7948+-1.5935</td>\n",
       "      <td>1.186+-0.1366</td>\n",
       "      <td>0.9944+-0.1015</td>\n",
       "      <td>0.9056+-0.0582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delicious (983 labels)</td>\n",
       "      <td>0.0619+-0.0001</td>\n",
       "      <td>0.0614+-0.0001</td>\n",
       "      <td>0.062+-0.0</td>\n",
       "      <td>0.0664+-0.0008</td>\n",
       "      <td>0.0622+-0.0001</td>\n",
       "      <td>0.0619+-0.0001</td>\n",
       "      <td>0.062+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mediamill (101 labels)</td>\n",
       "      <td>0.0747+-0.0001</td>\n",
       "      <td>0.0754+-0.0001</td>\n",
       "      <td>0.0758+-0.0001</td>\n",
       "      <td>0.0859+-0.0033</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "      <td>0.0743+-0.0001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Dataset SketchBoost Full CatBoost (multioutput)  \\\n",
       "0        Otto (9 classes)    0.4697+-0.003         0.4658+-0.0033   \n",
       "1    Dionis (355 classes)   0.3114+-0.0009          0.3085+-0.001   \n",
       "2    Helena (100 classes)   2.5865+-0.0025         2.5698+-0.0025   \n",
       "3   SF-Crime (39 classes)   2.2067+-0.0003         2.2036+-0.0005   \n",
       "4        MoA (206 labels)       0.016+-0.0            0.0161+-0.0   \n",
       "5       SCM20D (16 tasks)  91.0142+-0.3397        90.9814+-0.3652   \n",
       "6           RF1 (8 tasks)   1.1687+-0.0835         0.8975+-0.0384   \n",
       "7  Delicious (983 labels)   0.0619+-0.0001         0.0614+-0.0001   \n",
       "8  Mediamill (101 labels)   0.0747+-0.0001         0.0754+-0.0001   \n",
       "\n",
       "  XGBoost (one-vs-all)           Tabnet      Top Outputs  Random Sampling  \\\n",
       "0       0.4599+-0.0028   0.5363+-0.0064   0.4715+-0.0035   0.4636+-0.0026   \n",
       "1        0.3502+-0.002   0.4753+-0.0126   0.3146+-0.0011    0.304+-0.0014   \n",
       "2       2.5889+-0.0032   2.7197+-0.0235   2.5923+-0.0024   2.5693+-0.0022   \n",
       "3       2.2208+-0.0008     2.4819+-0.02    2.207+-0.0005   2.2037+-0.0004   \n",
       "4          0.0166+-0.0   0.0193+-0.0003      0.0163+-0.0       0.016+-0.0   \n",
       "5       89.1045+-0.495  87.3655+-1.3316  88.7442+-0.6346  86.2964+-0.4398   \n",
       "6        0.925+-0.0308   3.7948+-1.5935    1.186+-0.1366   0.9944+-0.1015   \n",
       "7           0.062+-0.0   0.0664+-0.0008   0.0622+-0.0001   0.0619+-0.0001   \n",
       "8       0.0758+-0.0001   0.0859+-0.0033   0.0745+-0.0001   0.0745+-0.0001   \n",
       "\n",
       "  Random Projection  \n",
       "0    0.4566+-0.0023  \n",
       "1    0.2848+-0.0012  \n",
       "2    2.5673+-0.0026  \n",
       "3    2.2038+-0.0004  \n",
       "4        0.016+-0.0  \n",
       "5   85.8061+-0.5534  \n",
       "6    0.9056+-0.0582  \n",
       "7     0.062+-0.0001  \n",
       "8    0.0743+-0.0001  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = get_summary_table(dataset_order, strategy_order, 'test_score', \n",
    "                           st_d, nm_d, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "stds = get_summary_table(dataset_order, strategy_order, 'std_test_score', \n",
    "                         st_d, nm_d, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "\n",
    "\n",
    "scores = scores.set_index('Dataset').astype(str)\n",
    "\n",
    "\n",
    "total = (scores + '+-' + stds.set_index('Dataset').astype(str)\n",
    "         ).replace('-+--', '-').reset_index()\n",
    "total.to_csv(os.path.join(OUTPUT_DIR, 'summary_' + 'score_w_std' + '.csv'), index=False)\n",
    "\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f085e131",
   "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>Dataset</th>\n",
       "      <th>SketchBoost Full</th>\n",
       "      <th>CatBoost (multioutput)</th>\n",
       "      <th>XGBoost (one-vs-all)</th>\n",
       "      <th>Tabnet</th>\n",
       "      <th>Top Outputs</th>\n",
       "      <th>Random Sampling</th>\n",
       "      <th>Random Projection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Otto (9 classes)</td>\n",
       "      <td>0.8223+-0.0021</td>\n",
       "      <td>0.8213+-0.0012</td>\n",
       "      <td>0.8238+-0.001</td>\n",
       "      <td>0.7972+-0.0031</td>\n",
       "      <td>0.821+-0.0016</td>\n",
       "      <td>0.8236+-0.0025</td>\n",
       "      <td>0.8258+-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dionis (355 classes)</td>\n",
       "      <td>0.9227+-0.0004</td>\n",
       "      <td>0.9234+-0.0003</td>\n",
       "      <td>0.9193+-0.0007</td>\n",
       "      <td>0.8936+-0.0032</td>\n",
       "      <td>0.9227+-0.0007</td>\n",
       "      <td>0.9251+-0.0005</td>\n",
       "      <td>0.9285+-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helena (100 classes)</td>\n",
       "      <td>0.3783+-0.0011</td>\n",
       "      <td>0.3808+-0.0017</td>\n",
       "      <td>0.377+-0.0012</td>\n",
       "      <td>0.3503+-0.0061</td>\n",
       "      <td>0.3773+-0.0014</td>\n",
       "      <td>0.3821+-0.0009</td>\n",
       "      <td>0.3836+-0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SF-Crime (39 classes)</td>\n",
       "      <td>0.3343+-0.0007</td>\n",
       "      <td>0.3352+-0.0008</td>\n",
       "      <td>0.3326+-0.0003</td>\n",
       "      <td>0.255+-0.0038</td>\n",
       "      <td>0.3349+-0.0006</td>\n",
       "      <td>0.3353+-0.0005</td>\n",
       "      <td>0.3357+-0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MoA (206 labels)</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9967+-0.0001</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9972+-0.0</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCM20D (16 tasks)</td>\n",
       "      <td>0.9224+-0.0007</td>\n",
       "      <td>0.9224+-0.0006</td>\n",
       "      <td>0.9257+-0.0008</td>\n",
       "      <td>0.9281+-0.0022</td>\n",
       "      <td>0.9262+-0.001</td>\n",
       "      <td>0.9301+-0.0007</td>\n",
       "      <td>0.9309+-0.0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RF1 (8 tasks)</td>\n",
       "      <td>0.9995+-0.0001</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "      <td>0.9932+-0.0037</td>\n",
       "      <td>0.9994+-0.0001</td>\n",
       "      <td>0.9996+-0.0001</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delicious (983 labels)</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9825+-0.0</td>\n",
       "      <td>0.9826+-0.0</td>\n",
       "      <td>0.9816+-0.0001</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mediamill (101 labels)</td>\n",
       "      <td>0.9747+-0.0001</td>\n",
       "      <td>0.9744+-0.0001</td>\n",
       "      <td>0.9746+-0.0</td>\n",
       "      <td>0.9709+-0.0011</td>\n",
       "      <td>0.9747+-0.0</td>\n",
       "      <td>0.9746+-0.0001</td>\n",
       "      <td>0.9748+-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Dataset SketchBoost Full CatBoost (multioutput)  \\\n",
       "0        Otto (9 classes)   0.8223+-0.0021         0.8213+-0.0012   \n",
       "1    Dionis (355 classes)   0.9227+-0.0004         0.9234+-0.0003   \n",
       "2    Helena (100 classes)   0.3783+-0.0011         0.3808+-0.0017   \n",
       "3   SF-Crime (39 classes)   0.3343+-0.0007         0.3352+-0.0008   \n",
       "4        MoA (206 labels)      0.9971+-0.0            0.9971+-0.0   \n",
       "5       SCM20D (16 tasks)   0.9224+-0.0007         0.9224+-0.0006   \n",
       "6           RF1 (8 tasks)   0.9995+-0.0001            0.9997+-0.0   \n",
       "7  Delicious (983 labels)      0.9824+-0.0            0.9825+-0.0   \n",
       "8  Mediamill (101 labels)   0.9747+-0.0001         0.9744+-0.0001   \n",
       "\n",
       "  XGBoost (one-vs-all)          Tabnet     Top Outputs Random Sampling  \\\n",
       "0        0.8238+-0.001  0.7972+-0.0031   0.821+-0.0016  0.8236+-0.0025   \n",
       "1       0.9193+-0.0007  0.8936+-0.0032  0.9227+-0.0007  0.9251+-0.0005   \n",
       "2        0.377+-0.0012  0.3503+-0.0061  0.3773+-0.0014  0.3821+-0.0009   \n",
       "3       0.3326+-0.0003   0.255+-0.0038  0.3349+-0.0006  0.3353+-0.0005   \n",
       "4          0.9971+-0.0  0.9967+-0.0001     0.9971+-0.0     0.9972+-0.0   \n",
       "5       0.9257+-0.0008  0.9281+-0.0022   0.9262+-0.001  0.9301+-0.0007   \n",
       "6          0.9997+-0.0  0.9932+-0.0037  0.9994+-0.0001  0.9996+-0.0001   \n",
       "7          0.9826+-0.0  0.9816+-0.0001     0.9824+-0.0     0.9824+-0.0   \n",
       "8          0.9746+-0.0  0.9709+-0.0011     0.9747+-0.0  0.9746+-0.0001   \n",
       "\n",
       "  Random Projection  \n",
       "0     0.8258+-0.001  \n",
       "1    0.9285+-0.0004  \n",
       "2    0.3836+-0.0019  \n",
       "3    0.3357+-0.0004  \n",
       "4       0.9971+-0.0  \n",
       "5    0.9309+-0.0008  \n",
       "6       0.9997+-0.0  \n",
       "7       0.9824+-0.0  \n",
       "8       0.9748+-0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = get_summary_table(dataset_order, strategy_order, 'test_acc', \n",
    "                           st_d, nm_d, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "stds = get_summary_table(dataset_order, strategy_order, 'std_test_acc', \n",
    "                         st_d, nm_d, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "\n",
    "\n",
    "scores = scores.set_index('Dataset').astype(str)\n",
    "\n",
    "\n",
    "total = (scores + '+-' + stds.set_index('Dataset').astype(str)\n",
    "         ).replace('-+--', '-').reset_index()\n",
    "total.to_csv(os.path.join(OUTPUT_DIR, 'summary_' + 'acc_w_std' + '.csv'), index=False)\n",
    "\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "20a1c5e6",
   "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>Dataset</th>\n",
       "      <th>SketchBoost Full</th>\n",
       "      <th>CatBoost (multioutput)</th>\n",
       "      <th>XGBoost (one-vs-all)</th>\n",
       "      <th>Tabnet</th>\n",
       "      <th>Top Outputs</th>\n",
       "      <th>Random Sampling</th>\n",
       "      <th>Random Projection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Otto (9 classes)</td>\n",
       "      <td>4424.8</td>\n",
       "      <td>5534.4</td>\n",
       "      <td>2142.0</td>\n",
       "      <td>27.2</td>\n",
       "      <td>4799.2</td>\n",
       "      <td>5424.4</td>\n",
       "      <td>5201.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dionis (355 classes)</td>\n",
       "      <td>18519.0</td>\n",
       "      <td>19858.8</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>39.4</td>\n",
       "      <td>17039.4</td>\n",
       "      <td>17990.4</td>\n",
       "      <td>11509.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helena (100 classes)</td>\n",
       "      <td>13492.2</td>\n",
       "      <td>11238.2</td>\n",
       "      <td>1563.8</td>\n",
       "      <td>49.6</td>\n",
       "      <td>15042.0</td>\n",
       "      <td>16975.6</td>\n",
       "      <td>11670.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SF-Crime (39 classes)</td>\n",
       "      <td>3754.6</td>\n",
       "      <td>3993.6</td>\n",
       "      <td>1212.4</td>\n",
       "      <td>14.8</td>\n",
       "      <td>3790.2</td>\n",
       "      <td>3726.2</td>\n",
       "      <td>3611.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MoA (206 labels)</td>\n",
       "      <td>2240.6</td>\n",
       "      <td>4239.0</td>\n",
       "      <td>471.8</td>\n",
       "      <td>38.2</td>\n",
       "      <td>2606.2</td>\n",
       "      <td>5542.6</td>\n",
       "      <td>2093.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCM20D (16 tasks)</td>\n",
       "      <td>19992.8</td>\n",
       "      <td>19998.0</td>\n",
       "      <td>19998.0</td>\n",
       "      <td>147.2</td>\n",
       "      <td>19992.4</td>\n",
       "      <td>19991.8</td>\n",
       "      <td>19993.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RF1 (8 tasks)</td>\n",
       "      <td>17001.4</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>19994.6</td>\n",
       "      <td>98.4</td>\n",
       "      <td>16102.8</td>\n",
       "      <td>17076.2</td>\n",
       "      <td>16815.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delicious (983 labels)</td>\n",
       "      <td>6911.2</td>\n",
       "      <td>3956.0</td>\n",
       "      <td>1611.6</td>\n",
       "      <td>143.4</td>\n",
       "      <td>7063.2</td>\n",
       "      <td>8015.0</td>\n",
       "      <td>7541.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mediamill (101 labels)</td>\n",
       "      <td>17927.4</td>\n",
       "      <td>8983.2</td>\n",
       "      <td>1878.6</td>\n",
       "      <td>84.6</td>\n",
       "      <td>18623.8</td>\n",
       "      <td>19961.6</td>\n",
       "      <td>17826.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Dataset  SketchBoost Full  CatBoost (multioutput)  \\\n",
       "0        Otto (9 classes)            4424.8                  5534.4   \n",
       "1    Dionis (355 classes)           18519.0                 19858.8   \n",
       "2    Helena (100 classes)           13492.2                 11238.2   \n",
       "3   SF-Crime (39 classes)            3754.6                  3993.6   \n",
       "4        MoA (206 labels)            2240.6                  4239.0   \n",
       "5       SCM20D (16 tasks)           19992.8                 19998.0   \n",
       "6           RF1 (8 tasks)           17001.4                 19999.0   \n",
       "7  Delicious (983 labels)            6911.2                  3956.0   \n",
       "8  Mediamill (101 labels)           17927.4                  8983.2   \n",
       "\n",
       "   XGBoost (one-vs-all)  Tabnet  Top Outputs  Random Sampling  \\\n",
       "0                2142.0    27.2       4799.2           5424.4   \n",
       "1                2681.0    39.4      17039.4          17990.4   \n",
       "2                1563.8    49.6      15042.0          16975.6   \n",
       "3                1212.4    14.8       3790.2           3726.2   \n",
       "4                 471.8    38.2       2606.2           5542.6   \n",
       "5               19998.0   147.2      19992.4          19991.8   \n",
       "6               19994.6    98.4      16102.8          17076.2   \n",
       "7                1611.6   143.4       7063.2           8015.0   \n",
       "8                1878.6    84.6      18623.8          19961.6   \n",
       "\n",
       "   Random Projection  \n",
       "0             5201.4  \n",
       "1            11509.6  \n",
       "2            11670.2  \n",
       "3             3611.4  \n",
       "4             2093.0  \n",
       "5            19993.6  \n",
       "6            16815.2  \n",
       "7             7541.6  \n",
       "8            17826.6  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_summary_table(dataset_order, strategy_order, 'best_iter', \n",
    "                           st_d, nm_d, baselines=baselines, output_dir=OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "258c7303",
   "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>Dataset</th>\n",
       "      <th>SketchBoost Full</th>\n",
       "      <th>CatBoost (multioutput)</th>\n",
       "      <th>XGBoost (one-vs-all)</th>\n",
       "      <th>Tabnet</th>\n",
       "      <th>Top Outputs</th>\n",
       "      <th>Random Sampling</th>\n",
       "      <th>Random Projection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Otto (9 classes)</td>\n",
       "      <td>131.2795</td>\n",
       "      <td>73.7665</td>\n",
       "      <td>1244.9438</td>\n",
       "      <td>903.2607</td>\n",
       "      <td>113.7105</td>\n",
       "      <td>102.3624</td>\n",
       "      <td>89.6285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dionis (355 classes)</td>\n",
       "      <td>23919.0901</td>\n",
       "      <td>18600.3721</td>\n",
       "      <td>18635.1964</td>\n",
       "      <td>1853.4115</td>\n",
       "      <td>1889.2980</td>\n",
       "      <td>2038.0721</td>\n",
       "      <td>419.5824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helena (100 classes)</td>\n",
       "      <td>355.5974</td>\n",
       "      <td>436.6722</td>\n",
       "      <td>1036.0248</td>\n",
       "      <td>1195.6204</td>\n",
       "      <td>154.6307</td>\n",
       "      <td>180.6419</td>\n",
       "      <td>113.4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SF-Crime (39 classes)</td>\n",
       "      <td>1146.1403</td>\n",
       "      <td>659.7310</td>\n",
       "      <td>4016.9352</td>\n",
       "      <td>2682.5084</td>\n",
       "      <td>705.4014</td>\n",
       "      <td>676.2324</td>\n",
       "      <td>612.8040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MoA (206 labels)</td>\n",
       "      <td>696.2490</td>\n",
       "      <td>9398.4015</td>\n",
       "      <td>376.3628</td>\n",
       "      <td>672.1119</td>\n",
       "      <td>103.5214</td>\n",
       "      <td>189.2574</td>\n",
       "      <td>87.0725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCM20D (16 tasks)</td>\n",
       "      <td>597.8406</td>\n",
       "      <td>798.6409</td>\n",
       "      <td>1432.8394</td>\n",
       "      <td>295.7300</td>\n",
       "      <td>499.3633</td>\n",
       "      <td>528.8417</td>\n",
       "      <td>479.0236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RF1 (8 tasks)</td>\n",
       "      <td>413.7743</td>\n",
       "      <td>804.6936</td>\n",
       "      <td>315.1049</td>\n",
       "      <td>207.8435</td>\n",
       "      <td>369.8712</td>\n",
       "      <td>396.4812</td>\n",
       "      <td>340.9701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delicious (983 labels)</td>\n",
       "      <td>19553.5613</td>\n",
       "      <td>20120.1416</td>\n",
       "      <td>15795.5608</td>\n",
       "      <td>2902.3544</td>\n",
       "      <td>575.1124</td>\n",
       "      <td>664.0449</td>\n",
       "      <td>1259.7520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mediamill (101 labels)</td>\n",
       "      <td>1777.6927</td>\n",
       "      <td>10164.0577</td>\n",
       "      <td>2074.3119</td>\n",
       "      <td>1230.7093</td>\n",
       "      <td>251.4459</td>\n",
       "      <td>263.4639</td>\n",
       "      <td>294.3931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Dataset  SketchBoost Full  CatBoost (multioutput)  \\\n",
       "0        Otto (9 classes)          131.2795                 73.7665   \n",
       "1    Dionis (355 classes)        23919.0901              18600.3721   \n",
       "2    Helena (100 classes)          355.5974                436.6722   \n",
       "3   SF-Crime (39 classes)         1146.1403                659.7310   \n",
       "4        MoA (206 labels)          696.2490               9398.4015   \n",
       "5       SCM20D (16 tasks)          597.8406                798.6409   \n",
       "6           RF1 (8 tasks)          413.7743                804.6936   \n",
       "7  Delicious (983 labels)        19553.5613              20120.1416   \n",
       "8  Mediamill (101 labels)         1777.6927              10164.0577   \n",
       "\n",
       "   XGBoost (one-vs-all)     Tabnet  Top Outputs  Random Sampling  \\\n",
       "0             1244.9438   903.2607     113.7105         102.3624   \n",
       "1            18635.1964  1853.4115    1889.2980        2038.0721   \n",
       "2             1036.0248  1195.6204     154.6307         180.6419   \n",
       "3             4016.9352  2682.5084     705.4014         676.2324   \n",
       "4              376.3628   672.1119     103.5214         189.2574   \n",
       "5             1432.8394   295.7300     499.3633         528.8417   \n",
       "6              315.1049   207.8435     369.8712         396.4812   \n",
       "7            15795.5608  2902.3544     575.1124         664.0449   \n",
       "8             2074.3119  1230.7093     251.4459         263.4639   \n",
       "\n",
       "   Random Projection  \n",
       "0            89.6285  \n",
       "1           419.5824  \n",
       "2           113.4900  \n",
       "3           612.8040  \n",
       "4            87.0725  \n",
       "5           479.0236  \n",
       "6           340.9701  \n",
       "7          1259.7520  \n",
       "8           294.3931  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_info = get_summary_table(dataset_order, strategy_order, 'train_time', \n",
    "                           st_d, nm_d, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "time_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "956a0525",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/home/jovyan/.imgenv-test-0/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiIAAAGpCAYAAACwK12sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABcTklEQVR4nO3dd5hU5dnH8e8N2AAbKoKALkaUKqgoGnsQW4w9lpcoKtZoijWaxNgSJZZEjRpjBEVjUExiSxQ7aowoqKCIBUQQEAEBsSKi9/vH/czu7DC77C67ewb297muvWbm1OecPeU+Tzvm7oiIiIhkoVnWCRAREZGmS4GIiIiIZEaBiIiIiGRGgYiIiIhkRoGIiIiIZEaBiIiIiGRGgYiIiIhkRoGISD0xs9vNzM3s9iLjRqdxFzd+yurOzMpSut3MyupjXC3Xv6GZLTSzeWbWuq7LWRk11DFT3XHa0Mxsj9xxUU/L2zEt79n6WJ5kQ4GIlBwzuzjvJuZmdlQN5vlPwTxljZBUaXgXAesBV7r7Z4UjzWxXM/uZmQ03s4lmtjT9/0fXdAVmtrqZnWVmY81skZl9Zmavm9klZrZ2NfO1NLP9zOzXZvYvM5ued/xdXIdtlVpy9zHAo8CuZnZI1umRummRdQJEauB44O6qRprZJsA+jZecOnkfeBv4KOuE1NLXRLpz3xuNmW0JnArMA26sYrIVehI2s/WBJ4Ft0qCvgG+AnulvkJnt7u7Ti8y+A/Dwiqxf6sXFxPk/xMwecvelGadHakk5IlLKPgI+B/Yys47VTHcs0ByY1hiJqgt3P9bdu7r7DVmnpTbcfVZKd1d3n9XIqz+LeFga7u5fVDHNl8BLwM3AScTTcW3cRQQhnwBHAi3dvRWwNzAb2Ax4yMyaVzH/QiKQuQo4GviwluuXFZRyRSYAWwIHZ5saqQsFIlLKPgf+QRynx1Uz3fHp8/YGTo80klQkMjD9/Fs1k67t7v3c/TR3v5VaBAJm1h/YL/08xd1Huvu3AO7+OHBYGteL4sffc+7ext33cvfz3P1uIkdFGl/uGDkl01RInSgQkVJ3W/o8rthIM9uFeBKaSg2z6c3s+2b2TzObZWZfpcqQz5rZaWa2+nLmHWhmz5vZp6k+wYtmdrKZ2XLmq7LioZm1M7OfmNkDZvZmWu6XZjbFzG41sx7VLLdSxUMzO87MXkjLWGhmT5jZbnnTt0jretnMPknTPWxm21ax/HqpdFoHRwGtgUnuPqGqidz9mxVYx6D0ORW4p8iyXwBGp5/H1vO6V8iKHDMFyzEzO9XMXkrHwydm9l8z+78azFtmZtea2RupXs0XZvaWmV1nZpvWcbu6mtktZvZOWt5iM5thZmPM7HIz61rFrH9Pn/3NbPO6rFuyozoiUuqeBd4FvmNmu7l7YbCRnxtSbU18M1sLuAM4PG/wJ8C6wK7p71gz29/dFxbMa8DQvPU58DHQl6grsCd1fxoeQsVNcWlKU0vgO+nvR2Y20N3/uZztuz0tZylRZLEe0B/YPVXkexx4kCh2WELU+WhF5Arsnvbvy3Xchvq2b/p8rgHXMSB9jvKqX0P+CLAHsIuZreXuXzZgemqjXo4ZYARRJPUtsIg4ZnYGdjazvYDBxfaNmQ0kzoc10qCv0jK2Sn/Hm9nh7v5YTTfIzAYAD+Ut82siV7Rj+utHHLcXF87r7h+Y2bvEtu8D/Lmm65XsKUdESlq6CN6efp6QP87MWgFHEBfA21m+W4ggZCqR7b+uu69LXMAPSsN3BIYVmfcnVAQhNwBt3b0N0Ia4MB6ZllEXU4BziSKAtdx9A+Ji3JOow7AGMDxVyq3KQcS+OAVYx93XAboCLxMPHH8CriYCpyOI3Ia10+93iX1wXR3T3xB2TZ8vNcTCzWwDoF36ObGaSXPjmgHdGiItdVQfx8zBxLFwIbB+Op43Jo5viOP9J4UzpYDhDqJe1pVAZ2AtIqjtCtxLHFv31jJn5M8p3Y8Bvdx9dXdfPy27J9GCalo187+YPnevxTqlFLi7/vRXUn/Ejd2Bael3J6Ilw2dA67zpjk/TPZZ+75F+O1BWsMxd0/A5QKcq1tsxrcOBPnnD1wTmp+F3VDHvFXnrvr3I+NFp3MV12B//TvP+usi42/PWO7DI+O/kjXdglyLTfC9vfMeCcWXV7NM6javB9m6eN+92tZw3tz9GL2e6XnnrOLCa6XrnTXdADdY/ra7/54yOmUurmP/ONH4+sGbe8GbAO2ncydWs/4E0zbUFw8vP0YLhbfPS1L6O++ucNP/0Fd33+mvcP+WISMlz9xnAE8QT1xF5o3I5FMVyMAoNTp93peUVW89M4On0M7858N5EzgfApVUsfwiwuAbpqIv/pM9dqpnmfSrKycu5+7vE0zNE5cr/Fpn3GSqKlbauayLrUf5T/LwGWkd+/yBVtcgpHFdlnyIlqCbHzJdELlkxueO8DRVFWAC7AV2IFm23VrPsO9JnTZvVf0rkbAK0r+E8hXJN4+s6v2REdURkZXEbERCcAAwzsy2IXI6FwP01mH/n9Dl4ORXx1k2fm+UN65s+Z7j7FIpw90Vm9nLeemrFzHoTxSq7ELkJrYHCCrDVNWEe5+mxsIg5wBbA2GIj3f0bM/sI6ACsX4tkN5SN8r4vyCwVJa6ejplPio1w98lmNjPN35eouwEVx/e6wAfV1NHOVfrerKoJCtb3pZk9SQQ9o8zsZiKYetXdl9RkGVQcK6uZ2Xru/nEN55OMKRCRlcV9RNCxs5l1oaIVzQh3r0lORO4pe530tzwt8763TZ/L60djZg2WuwwzO4Oon5HLoXSi4mAul2ItIs2tqlnMp9WMW1qLaVarNrGNY8287w3VHDZ/X7SscqrK46rbf42qno6Z5R3Ps4hApG3esNx5tBpRn2R51qrBNDknEpWpexP1Vi4ElpjZWKKoZ6i7VxeY5lckXrPKqaTkqGhGVgru/hVRwx/igpVrTnlb8TmWkeuQ6jR3txr8HVePya+SmXUDriXOxXuJFjhruvv67t7O3dsRHXvBsk+7q6r5ed8bKofmg7zvHaqZLn/cB1VO1YgyPmZy59GLNTyParx+d38f2JZoMXU9UdG6GZELcyUwxcy+V80i2uR9n1/lVFJyFIjIyiQXdPyceFKb6O7jajhvrqOrGmUVF5ibPqu7YdVkfDGHExf3N4Gj3H1skazodsvOtkrLrxfSpsqpVoC7z6fimOhZzaS5cd8S/6NSUF/HTE2P57l5w1bkPFoud//W3R9195+5e1/i/z+QqAO1PvB3q7qvn9yxssjdG/V1BLJiFIjISiMFHa9TUf5ck0qqOc+nzwPqsOpcsNPJzL5TbAIzWwfYrg7L7pQ+J3jq1bOIveqw3JXZZCqKihqyc6rH0+c+1XRIl+vP5L9eOn2I1Ncx09eqeKNxqoOVq1+SH+znzqN2ZtaXBubun7r736mobL4x0eKpmM7ps1QCRqkhBSKysvkFcE36q67r70K3pM+eZnZadROaWauCp67HifopEOXWxZxH7crDcxalz17FboZmth/R5LHJ8HjL7ivp5w4NuKrh6fM7wA8LR5pZP6KjOqhoBVIK6uuYWYto8lrMr9PnAioCNohWZbkK23+sJncil5Ya5WgtbzlUrv9RVfDVL30+U5N1SulQICIrFXd/xN3PSX81btrp7s9QUbRzo5n9Mb8raDNbw8x2NLMrgenkVdBLT8KXpZ+DUrfWG6T51jGzC4FfEj2t1tao9NkjpatNWm4rMzuFeNdOUyzvHp0++1U3kZm1NrMNc39U9Mq5Wv5wi7fsVuLuTxI9pwLcYmY/NLNmabn9gX+lca9TRYd5ZrZ+wfpz19SWBesvmvNQR/V1zCwCLjSzCyze7UNK63VU9Np6WX5lcI83255K5FjtAjxrZv3NrLySs5ltbtFt/FjgxzXcpu+a2WtmdqaZdcv7P5iZfZeKnlJnAq8VzmzxUsJcjqQCkZVN1h2Z6E9/hX8UdGhWi/n2oJpOtIginb/mTeNES4gFRIdp+cM7FMzbjHgqzo3/Js23NP0eQUVHUbcXWfdoquicKs2bv+6FecsdB5xR1f6obp01WXfeNNPSNMcVDC+rap/WdVwN/5d90rxfED3FVjXd7VTed1X9FT2WiHoHr+RN9yXRrXj5fMBmNdhvy/ur8v9Tl/9bfR0zwN3p+9J0PH+bt8zhQLMq0nYw0a18btolRD8eiwvS9auqztFqzt385X2dN2wRsGsV6dk7TTMHWK22+1p/2f4pR0SaDHdf4u4nAd8lLsLvEpX+WhMV8kYTHTlt7QWvvPeoRHcs0VpnDHHDakHcxE4FlvuSsGoMJCrgvkY0v2xOPIVfQLQY+GwFlr1ScvfxRPfuawGHNuB6FhLd+p9DtNLI3fgmUnEsTG+o9a+A+jpmjiZyLV4ljufPgReAY919kFdRB8Xd7yf6prmE+D99Rryn5itgAtHZ2SHAVTVMx1iis8I/E/+Hj4jmx4uB8USrmW7uXtW7h3Jvar7NVVF1pWMpmhQRKSlmdizxVP60u1fXbFOasPTOqdlEny9buvvUjJMktaQcEREpVXcBk4A9zawhK63Kyu0Movv9WxWErJyUIyIiJcvMvk+8wO0Rd98/6/RIaUmVgN8jelLdwt3nZJwkqQN18S4iJcvd/2NmZwLrmllrj6a9IjllwI3EO2kUhKyklCMiIiIimVGOCLDhhht6WVlZ1skQERFZJb388ssfuftGxcYpEAHKysoYN66mrywRERGR2jCzKpvBq9WMiIiIZEaBiIiIiGRGgYiIiIhkRoGIiIiIZEaBiIiIiGRGgYiIiIhkRoGIiIiIZEaBiIiIiGRGgYiISAk44YQTaNu2LT179iwftmDBAgYMGECXLl0YMGAACxcuBOCtt95ip512Yo011uDqq6+utJyPP/6Yww8/nK5du9KtWzdeeOGFSuOvueYazIyPPvqo4TdKpAYUiIiIlIDjjjuOUaNGVRo2ZMgQ+vfvz+TJk+nfvz9DhgwBoE2bNlx//fWcc845yyznZz/7Gfvuuy9vvfUWEyZMoFu3buXjZsyYwWOPPcamm27asBsjUgsKRERESsBuu+1GmzZtKg174IEHGDRoEACDBg3i/vvvB6Bt27Zsv/32rLbaapWmX7RoEc8++yyDBw8GYPXVV2e99dYrH3/mmWdy5ZVXYmYNtyEitaRARESkRM2ZM4f27dsD0K5dO+bMqf5N9++99x4bbbQRxx9/PNtssw0nnngin3/+ORBBTYcOHejdu3eDp1ukNhSIiIisBMxsuTkZS5cu5ZVXXuG0007j1VdfpVWrVgwZMoQvvviCyy+/nEsvvbSRUitScwpERERK1MYbb8zs2bMBmD17Nm3btq12+o4dO9KxY0f69esHwOGHH84rr7zCu+++y3vvvUfv3r0pKytj5syZbLvttnz44YcNvg0iy6NARESkRB144IEMHz4cgOHDh3PQQQdVO327du3o1KkTb7/9NgBPPvkk3bt3p1evXsydO5dp06Yxbdo0OnbsyCuvvEK7du0afBtElqdF1gkQERE4+uijGT16NB999BEdO3bkkksu4fzzz+eII45g6NChbLbZZowcORKADz/8kL59+/LJJ5/QrFkzrr32WiZNmsQ666zDn/70JwYOHMiSJUvYfPPNue222zLeMpHqmbtnnYbM9e3b18eNG5d1MkRERFZJZvayu/ctNk5FMyIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGTXfFRGpZ5sd9UtmLfg462Q0iA5t1mP63ZdnnQxZhTRqIGJmw4ADgLnu3jMNawPcA5QB04Aj3H2hRV/G1wH7A18Ax7n7K2meQcCv02J/6+7D0/DtgNuBtYCHgZ+52ieLSCObteBjru21X9bJaBA/f/2RrJMgq5jGLpq5Hdi3YNj5wJPu3gV4Mv0G2A/okv5OBv4M5YHLRUA/YAfgIjNbP83zZ+CkvPkK1yUiIiIlpFEDEXd/FlhQMPggYHj6Phw4OG/4HR7GAOuZWXtgH+Bxd1/g7guBx4F907h13H1MygW5I29ZIiIiUoJKobLqxu4+O33/ENg4fe8AzMibbmYaVt3wmUWGi4iISIkqhUCkXMrJaJQ6HWZ2spmNM7Nx8+bNa4xVioiISIFSCETmpGIV0ufcNHwW0Clvuo5pWHXDOxYZXpS73+Lufd2970YbbbTCGyEiIiK1VwqByIPAoPR9EPBA3vBjLewILEpFOI8Ce5vZ+qmS6t7Ao2ncJ2a2Y2pxc2zeskRERKQENXbz3RHAHsCGZjaTaP0yBBhpZoOB6cARafKHiaa7U4jmu8cDuPsCM7sMGJumu9TdcxVgf0xF891H0p+IiIiUqEYNRNz96CpG9S8yrQOnV7GcYcCwIsPHAT1XJI0iIiLSeEqhaEZERESaKAUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSGQUiIiIikhkFIiIiIpKZkglEzOxMM3vDzCaa2QgzW9PMOpvZi2Y2xczuMbPV07RrpN9T0viyvOVckIa/bWb7ZLZBIiIislwlEYiYWQfgp0Bfd+8JNAeOAn4P/NHdtwAWAoPTLIOBhWn4H9N0mFn3NF8PYF/gJjNr3pjbIiIiIjVXEoFI0gJYy8xaAC2B2cD3gH+k8cOBg9P3g9Jv0vj+ZmZp+N3u/pW7vwdMAXZonOSLiIhIbZVEIOLus4CrgfeJAGQR8DLwsbsvTZPNBDqk7x2AGWnepWn6DfKHF5lHRERESkxJBCJmtj6Rm9EZ2ARoRRStNOQ6TzazcWY2bt68eQ25KhEREalCSQQiwF7Ae+4+z92/Bv4F7Aysl4pqADoCs9L3WUAngDR+XWB+/vAi81Ti7re4e19377vRRhvV9/aIiIhIDZRKIPI+sKOZtUx1PfoDk4CngcPTNIOAB9L3B9Nv0vin3N3T8KNSq5rOQBfgpUbaBhEREamlFsufpOG5+4tm9g/gFWAp8CpwC/Af4G4z+20aNjTNMhS408ymAAuIljK4+xtmNpIIYpYCp7v7N426MSIiIlJjJRGIALj7RcBFBYOnUqTVi7svBn5YxXJ+B/yu3hMoIiIi9a5UimZERESkCVIgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUSAiIiIimVEgIiIiIpmpUSBiZr8zs79UMe5mM7usfpMlIiIiTUFNc0SOBp6rYtxzwP/VT3JERESkKalpILIJMKuKcR+k8SIiIiK1UtNA5ENg2yrGbQvMq5/kiIiISFNS00BkJPAbM/t+/kAz2x+4ELi7vhMmIiIiq74WNZzuN0Af4CEzmw/MBtoDbYDHiGBEREREpFZqFIi4+2JgbzPbB9gT2ACYDzzp7o83YPpERERkFVbTHBEA3P1R4NEGSouIiIg0MTXu0MzM1jCz08xsqJk9amZd0vAjzaxbwyVRREREVlU1yhExsy2Bx4F1gZeBPYC10+hdge8DxzZA+kRERGQVVtMckeuB94EyYB/A8sY9A+xSv8kSERGRpqCmdUR2BX7o7h+bWfOCcXOIFjQiIiIitVLTHJHFwFpVjOsAfFwvqREREZEmpaaByOPAL81s3bxhbmZrAD8BHq73lImIiMgqr6ZFM+cCzwNTiKDEiU7OegCrA4c2SOpERERklVajHBF3nwH0Bm4mKqy+S9QLuRfYzt0/XNGEmNl6ZvYPM3vLzN40s53MrI2ZPW5mk9Pn+mlaM7PrzWyKmb1mZtvmLWdQmn6ymQ1a0XSJiIhIw6lxPyLuvtDdL3T377r7lu6+o7v/yt3n11NargNGuXtXIuh5Ezif6L21C/Bk+g2wH9Al/Z0M/BnAzNoAFwH9gB2Ai3LBi4iIiJSeGgUiZtYi1QfJH7a3mf08PzeirlLdk92AoQDuvsTdPwYOAoanyYYDB6fvBwF3eBgDrGdm7YmmxY+7+wJ3X0gUI+27oukTERGRhlHTHJF7SLkOAGb2U2AUcAUwxswOWMF0dAbmAbeZ2atmdquZtQI2dvfZaZoPgY3T9w7AjLz5Z6ZhVQ0XERGRElTTQGRHKreMORe4xt3XAm4FfrWC6WgBbAv82d23AT6nohgGAHd3opJsvTCzk81snJmNmzdvXn0tVkRERGqhpoHIBkSOBGbWC9iEqLgKUWG1+wqmYyYw091fTL//QQQmc1KRC+lzbho/C+iUN3/HNKyq4ctw91vcva+7991oo41WMPkiIiJSFzUNROYQrWUg6lxMd/d30++1gG9XJBGp1c0MM9sqDeoPTAIeBHItXwYBD6TvDwLHptYzOwKLUhHOo8DeZrZ+qqS6N3pbsIiISMmqaT8i9wK/N7PewPHADXnjtgEm10NafgLcZWarA1PTepoBI81sMDAdOCJN+zCwP9GvyRdpWtx9gZldBoxN013q7gvqIW0iIiLSAGoaiJwPfAJsT1RavTxv3HZEZdYV4u7jgb5FRvUvMq0Dp1exnGHAsBVNj4iIiDS8GgUi7r4UuLSKcepVVUREROqkxh2aiYiIiNQ3BSIiIiKSGQUiIiIikhkFIiIiIpIZBSIiIiKSmRUORMxsUzNbpz4SIyIiIk1LfeSITCO6Yh9qZj3qYXkiIiLSRNS0Q7PqHA+0BPoB/wS61sMyRUREpAlY4UDE3Yenr39e0WWJiIhI01Lropn0orlNzKw+clNERESkCatxIGJm+5vZi8Bi4H1g6zT8FjP7UQOlT0RERFZhNQpEzOxY4EHgLeDkgvkmA4PrP2kiIiKyqqtpjsivgKvcfRDwt4JxbwDd6zVVIiIi0iTUNBDZDHi8inGLAfUjIiIiIrVW00BkBrBNFeP6AlPqJzkiIiLSlNQ0EBkKXJQqpa6VhpmZ9QfOA/7aEIkTERGRVVtNm+D+HugEDAe+ScP+BzQH/uLu1zdA2kRERGQVV6NAxN0dON3M/gDsBWwALACecvd3GjB9IiIisgqrVadk7v4u8G4DpUVERESamFoFIma2FdABWLNwnLs/XF+JEhERkaahRoGImfUCRgDdACsyiRP1RURERERqrKY5IsOAr4EDiKa6SxosRSIiItJk1DQQ6QYc5u6PNmRiREREpGmpaT8iLwGbNmRCREREpOmpaY7IycAIM/sCeBr4uHACd/+iHtMlIiIiTUBNA5GPgGnAHdVMo8qqIiIiUis1DUT+BuwEXI0qq4qIiEg9qWkgsidwkrv/vSETIyIiIk1LTSurTgNUB0RERETqVU0DkXOBX5lZWQOmRURERJqYmhbNXEI0333HzKZRvNXMDvWXLBEREWkKahqITEx/IiIiIvWmRoGIux/f0AkRERGRpqemdURERERE6l2VOSJmdiVwvbvPTN+r5e7n1WvKREREZJVXXdHMD4G7gJnpe3UcUCAiIiIitVJdIHIc8C6Au3dulNSIiIhIk1JdHZGngO6NlRARERFpeqoLRKzRUiEiIiJNklrNiIiISGaW14/I/mbWtSYLcvc76iE9IiIi0oQsLxD5TQ2X44ACEREREamV5QUiewLjGiMhIiIi0vQsLxD50t0/b5SUiIiISJOjyqoiIiKSGQUiIiIikpkqi2bcXUGKiIiINCgFGyIiIpIZBSIiIiKSGQUiIiIikpmSCkTMrLmZvWpm/06/O5vZi2Y2xczuMbPV0/A10u8paXxZ3jIuSMPfNrN9MtoUERERqYGSCkSAnwFv5v3+PfBHd98CWAgMTsMHAwvT8D+m6TCz7sBRQA9gX+AmM2veSGkXERGRWiqZQMTMOgLfB25Nvw34HvCPNMlw4OD0/aD0mzS+f5r+IOBud//K3d8DpgA7NMoGiIiISK2VTCACXAucB3ybfm8AfOzuS9PvmUCH9L0DMAMgjV+Upi8fXmSeSszsZDMbZ2bj5s2bV4+bISIiIjVVEoGImR0AzHX3lxtrne5+i7v3dfe+G220UWOtVkRERPIs710zjWVn4EAz2x9YE1gHuA5Yz8xapFyPjsCsNP0soBMw08xaAOsC8/OG5+TPIyIiIiWmJHJE3P0Cd+/o7mVEZdOn3H0g8DRweJpsEPBA+v5g+k0a/5S7exp+VGpV0xnoArzUSJshIiIitVQqOSJV+QVwt5n9FngVGJqGDwXuNLMpwAIieMHd3zCzkcAkYClwurt/0/jJFhERkZoouUDE3UcDo9P3qRRp9eLui4EfVjH/74DfNVwKRUREpL6URNGMiIiINE0KRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkRERKRkzZgxgz333JPu3bvTo0cPrrvuOgAWLFjAgAED6NKlCwMGDGDhwoUAXHXVVfTp04c+ffrQs2dPmjdvzoIFC8qX980337DNNttwwAEHZLI9siwFIiIiUrJatGjBNddcw6RJkxgzZgw33ngjkyZNYsiQIfTv35/JkyfTv39/hgwZAsC5557L+PHjGT9+PFdccQW77747bdq0KV/eddddR7du3bLaHClCgYiIiJSs9u3bs+222wKw9tpr061bN2bNmsUDDzzAoEGDABg0aBD333//MvOOGDGCo48+uvz3zJkz+c9//sOJJ57YKGmXmlEgIiIiK4Vp06bx6quv0q9fP+bMmUP79u0BaNeuHXPmzKk07RdffMGoUaM47LDDyof9/Oc/58orr6RZM936Son+GyIiUvI+++wzDjvsMK699lrWWWedSuPMDDOrNOyhhx5i5513Li+W+fe//03btm3ZbrvtGi3NUjMKREREpKR9/fXXHHbYYQwcOJBDDz0UgI033pjZs2cDMHv2bNq2bVtpnrvvvrtSsczzzz/Pgw8+SFlZGUcddRRPPfUUP/rRjxpvI6RKCkRERKRkuTuDBw+mW7dunHXWWeXDDzzwQIYPHw7A8OHDOeigg8rHLVq0iGeeeabSsCuuuIKZM2cybdo07r77br73ve/xt7/9rfE2RKrUIusEiIiIVOX555/nzjvvpFevXvTp0weAyy+/nPPPP58jjjiCoUOHstlmmzFy5Mjyee677z723ntvWrVqlVGqpTbM3bNOQ+b69u3r48aNyzoZIrKKaLH3j7m2135ZJ6NB/Pz1R1j62E1ZJ0NWMmb2srv3LTZORTMikrkTTjiBtm3b0rNnz/JhRx55ZHnHVGVlZeVPw3fddVf58D59+tCsWTPGjx/Pp59+Wmn4hhtuyM9//vNsNkhEakxFMyKSueOOO44zzjiDY489tnzYPffcU/797LPPZt111wVg4MCBDBw4EIDXX3+dgw8+uDxIGT9+fPk82223XXnFRhEpXQpERCRzu+22G9OmTSs6zt0ZOXIkTz311DLjRowYwVFHHbXM8HfeeYe5c+ey66671ndSRaSelUTRjJl1MrOnzWySmb1hZj9Lw9uY2eNmNjl9rp+Gm5ldb2ZTzOw1M9s2b1mD0vSTzWxQVtskIvXjueeeY+ONN6ZLly7LjLvnnnsqNdHMufvuuznyyCOX6VtCREpPSQQiwFLgbHfvDuwInG5m3YHzgSfdvQvwZPoNsB/QJf2dDPwZInABLgL6ATsAF+WCFxFZORV2053z4osv0rJly0r1SnIK+5AQkdJVEkUz7j4bmJ2+f2pmbwIdgIOAPdJkw4HRwC/S8Ds8mvyMMbP1zKx9mvZxd18AYGaPA/sCIxptY0Sk3ixdupR//etfvPzyy8uMqyrYmDBhAkuXLlUPmiVksyN/wayFn2adjHrXYf21mX7P77NOxkqvJAKRfGZWBmwDvAhsnIIUgA+BjdP3DsCMvNlmpmFVDReRldATTzxB165d6dixY6Xh3377LSNHjuS5555bZp6qclAkO7MWfsolbedmnYx6d9Gqt0mZKJWiGQDMrDXwT+Dn7v5J/riU+1FvnZ6Y2clmNs7Mxs2bN6++FisidXD00Uez00478fbbb9OxY0eGDh0KVJ3r8eyzz9KpUyc233zzZcaNHDlSgYjISqRkckTMbDUiCLnL3f+VBs8xs/buPjsVveTiz1lAp7zZO6Zhs6goyskNH11sfe5+C3ALRIdm9bQZIlIHI0YULz29/fbbiw7fY489GDNmTNFxU6dOra9kiUgjKIkcEYuq7UOBN939D3mjHgRyLV8GAQ/kDT82tZ7ZEViUinAeBfY2s/VTJdW90zAREREpQaWSI7IzcAzwupmNT8N+CQwBRprZYGA6cEQa9zCwPzAF+AI4HsDdF5jZZcDYNN2luYqrIiIiUnpKIhBx9/8CVTX4719kegdOr2JZw4Bh9Zc6ERERaSglUTQjIiIiTVNJ5IiIyMqv85HnMWPhZ1kno951Wr81791zZdbJEFllKRARkXoxY+FnPLn4jayTUe/6L+yRdRJEVmkqmhEREZHMKBARERFZSZxwwgm0bdu20juWzj33XLp27crWW2/NIYccwscffwzASy+9RJ8+fejTpw+9e/fmvvvuK59n1KhRbLXVVmyxxRYMGTKksTejEgUiIiIiK4njjjuOUaNGVRo2YMAAJk6cyGuvvcaWW27JFVdcAUDPnj0ZN24c48ePZ9SoUZxyyiksXbqUb775htNPP51HHnmESZMmMWLECCZNmpTF5gAKRERERFYau+22G23atKk0bO+996ZFi6jyueOOOzJz5kwAWrZsWT588eLFRN+hkVOyxRZbsPnmm7P66qtz1FFH8cADD5AVBSIiIiKriGHDhrHffvuV/37xxRfp0aMHvXr14uabb6ZFixbMmjWLTp0q3pLSsWNHZs2alUVyAQUiIiIiq4Tf/e53tGjRgoEDB5YP69evH2+88QZjx47liiuuYPHixRmmsDg13xUREVnJ3X777fz73//mySefLC+CydetWzdat27NxIkT6dChAzNmzCgfN3PmTDp06NCYya1EOSIiIiIrsVGjRnHllVfy4IMP0rJly/Lh7733HkuXLgVg+vTpvPXWW5SVlbH99tszefJk3nvvPZYsWcLdd9/NgQcemFXylSMiIiKysjj66KMZPXo0H330ER07duSSSy7hiiuu4KuvvmLAgAFAVFi9+eab+e9//8uQIUNYbbXVaNasGTfddBMbbrghADfccAP77LMP33zzDSeccAI9emTXcZ8CERERkZXEiBEjlhk2ePDgotMec8wxHHPMMUXH7b///uy///71mra6UtGMiIiIZEaBiIiIiGRGgYiIiIhkRoGIiIiIZEaBiIiIiGRGrWZEREQa0eZH/5L353+cdTLq3aYbrMfUEZfXej4FIiIiIo3o/fkf8/ruh2edjHrX65l/1Gk+Fc2IiIhIZhSIiIiISGYUiIiIiEhmFIiIiIhIZhSIiIiISGYUiIiIiEhmFIiIiIhIZhSIiIiISGYUiIiIiEhmFIiIiIhIZhSIiIiISGYUiIiIiEhmFIhIo5sxYwZ77rkn3bt3p0ePHlx33XXl4/70pz/RtWtXevTowXnnnQfASy+9RJ8+fejTpw+9e/fmvvvuyyrpDaKq/TFhwgR22mknevXqxQ9+8AM++eSTSvO9//77tG7dmquvvjqLZIuI1AsFIo2kqpvNxRdfTIcOHcpvtA8//DAA8+fPZ88996R169acccYZWSa93rVo0YJrrrmGSZMmMWbMGG688UYmTZrE008/zQMPPMCECRN44403OOeccwDo2bMn48aNY/z48YwaNYpTTjmFpUuXZrwV9aeq/XHiiScyZMgQXn/9dQ455BCuuuqqSvOdddZZ7LfffhmlWkSkfrTIOgFNRe5ms+222/Lpp5+y3XbbMWDAAADOPPPM8ptuzpprrslll13GxIkTmThxYhZJbjDt27enffv2AKy99tp069aNWbNm8de//pXzzz+fNdZYA4C2bdsC0LJly/J5Fy9ejJk1fqIbUFX745133mG33XYDYMCAAeyzzz5cdtllANx///107tyZVq1aZZZuEZH6oByRRtK+fXu23XZboPLNpiqtWrVil112Yc0112ysJGZi2rRpvPrqq/Tr14933nmH5557jn79+rH77rszduzY8ulefPFFevToQa9evbj55ptp0WLVjKHz90ePHj144IEHALj33nuZMWMGAJ999hm///3vueiii7JMqohIvVAgkoH8mw3ADTfcwNZbb80JJ5zAwoULM05d4/nss8847LDDuPbaa1lnnXVYunQpCxYsYMyYMVx11VUcccQRuDsA/fr144033mDs2LFcccUVLF68OOPU17/C/TFs2DBuuukmtttuOz799FNWX311IIrzzjzzTFq3bp1xikVEVpwCkUZWeLM57bTTePfddxk/fjzt27fn7LPPzjqJjeLrr7/msMMOY+DAgRx66KEAdOzYkUMPPRQzY4cddqBZs2Z89NFHlebr1q0brVu3XuWKq4rtj65du/LYY4/x8ssvc/TRR/Od73wHiNyh8847j7KyMq699louv/xybrjhhiyTLyJSZ6tm/naJKnaz2XjjjcvHn3TSSRxwwAFZJa/RuDuDBw+mW7dunHXWWeXDDz74YJ5++mn23HNP3nnnHZYsWcKGG27Ie++9R6dOnWjRogXTp0/nrbfeoqysLLsNqGdV7Y+5c+fStm1bvv32W377299y6qmnAvDcc8+VT3PxxRevkhWaRaTpUCDSSKq62cyePbu8ouJ9991Hz549s0pio3n++ee588476dWrF3369AHg8ssv54QTTuCEE06gZ8+erL766gwfPhwz47///S9DhgxhtdVWo1mzZtx0001suOGG2W5EPapqf0yePJkbb7wRgEMPPZTjjz8+w1SKiDQMBSKNpKqbzYgRIxg/fjxmRllZGX/5y1/K5ykrK+OTTz5hyZIl3H///Tz22GN07949oy2oP7vsskt53Y9Cf/vb35YZdswxx3DMMcc0dLIyU93++NnPflbtvBdffHEDpEhEpPEoEGkkVd1s9t9//yrnmTZtWgOmSEREJHuqrCoiIiKZUSAiIiIimVEgIiIiIplRICIiIiKZUWVVqbHNjz6P9+d/lnUy6t2mG7Rm6ograz1fp6PPY/YquD/ab9CaGXXYHyIidaFApBodj7qADxcsyjoZDaJdm3WZefcVtZrn/fmfMXWX55Y/4Upm8//uWqf5Zs//jH4/mFDPqcneiw/1zjoJItKEKBCpxocLFtG7945ZJ6NBTJgwJuskiIiIqI6IiIiIZGeVDETMbF8ze9vMppjZ+VmnR0RERIpb5QIRM2sO3AjsB3QHjjazlb9fdBERkVXQKheIADsAU9x9qrsvAe4GDso4TSIiIlLEqhiIdABm5P2emYaJiIhIibGq3vq5sjKzw4F93f3E9PsYoJ+7n1Ew3cnAyennVsDbjZrQZW0IfJRxGkqJ9kdl2h+VaX9U0L6oTPujslLZH5u5+0bFRqyKzXdnAZ3yfndMwypx91uAWxorUctjZuPcvW/W6SgV2h+VaX9Upv1RQfuiMu2PylaG/bEqFs2MBbqYWWczWx04Cngw4zSJiIhIEatcjoi7LzWzM4BHgebAMHd/I+NkiYiISBGrXCAC4O4PAw9nnY5aKpliohKh/VGZ9kdl2h8VtC8q0/6orOT3xypXWVVERERWHqtiHRERERFZSSgQaSLMrJmZNTczyzotqxIz0znUBOXOo9z/38y+b2YHZ5qoVUhTPa+yvj5baNnYaWmS/+xVnZmtbWZb5Q9z92/d/RtXWdwKy79Iuvu3WaalVKQLWJMIdM2smbu7mR0N/DUN7g6cksav8vugvpnZWma2Wvp+LnCqma2TcbIajZmtZ2Y/yuL6nB5Sc8fs9sDxAI2ZFgUiq5C8g2ld4DcF43Yxsz+b2eNmdlrjp27lkjs507uLKskFH2a2sZkdaGadll3CqqvgwgXERWtVC3TNrL2ZdU7fyyv25wWf04D26QnyUaBdGr/K7IOGlJebtBNwoLt/nUbdDbQCzsqfblVhZlukY6u1md1nZlsAXwEHpA45G1V6SM0ds0uAC8zsVDO7uLHSsEr9g5uC6k7K9JTWgjiJDzWzUWb2TLpQng5MAH4KjGyc1Ja+FGxsnL4PTlnszXMnp7t/k8Y1z5vnx2Y2BfgdcdEcmEniG4GZ9TKzLfOHFVy4MLOOZtbHzC4zszFmtl+xAK7UpRydfc3sUTP7H3A/sBdEtwBpGjOz7cysJ/Aqca59x91fA9Y1s/YZJb/kFQlecwHdasDPzewXZnahu88AngAOSrlPK3WuYwrc86/bVwDfd/fPgLWJ4+dLIvA6ycy61uO6rbp7hpltbmZHW/Q0DrAL8SC7A/BGY+XuKRApURadseUOpPKDofCkTNF1btoLgDOALYGpwHPAAGANoC3R1W9zIuptsgqe6E8GLjSz9YmO755w92/M7DspKPmNmb0HnG9m65pZW2Bb4NT0GoG/Azul5a70WfJm1iEFFrkcgN7kXSfSPjjIzE40s03S4BuB64HJRFPBo4DvN2a6ayt3zhRoBmwDGPADd+/n7n81s83M7HIzWz0FYKcQN5LFwHwgd+P4kDg2mrTczS//fDAzKwheV0tB37HAZkAPYt+/B+DurxI5Tns3auIbQArcv83bH28C66fvk4Bu6dj6AJgHHFDbdaRjtGP6nl907Hk5uBuYWVnePNsCfyD2exczO9PdbwBeJPrfurexcvcUiJSQ3FOkmXUjvTE4HUiehnc2s5PNbJ/09NYJuBnYOS3iU6CDuz9E9KPyrbsvcfeFxA1ifSJQudeaWPFMwcmZ/0T/PpEt2h5YDzgv5SDtD9wKvAP8ENiYKDtdj7hw/jfNfz3QLS13pcuST8dR/nVgd2CXXA4AEZytblHvaC3gOuBEYE/gN2a2AfAvoiuAO9x9GPHept0abyuWzyqK2szM9iAFj/lS0cDbwMvuPt/MWlpkm29ABBjd06RvAK3T90lAz/T91WLLXdWZ2aZmVv7/zt38Ug7tema2cfq+iZm9kHI5viaC3N7ufifwGPAnd/9b3qLfBrqm87HkpGOp8PwpnKaDmR1jZr8g3mkGEWx1TMHwBCKQXSONGwVsk861qpbZObfuvMFXE+dkpYfVlOPxYzO7BxgN3GlmufvFj4F7iWvY+sDgdE95mXT/sUbK2VQgkqHCf3KuGABoAxxuZj9NQcdhZnYtEXTsRGTh/RSYA7xOxQH+NrBOOpjGknczcPd73P1sdz8VuAPYt+G2LBtWIG9487yngjZmdqSZ7Z9GTwfWJHKLmgF9iHcVPQV8QuSQjEu/dyYCl/ZA7kIxA+hkZu0afANXQFUXzVSvIz+XbTNgiJm9ZWY/A/Yh6hutTQRnZe7+AyInaWH6fIF0Y045KW8AXRp6m6qS/7/PyStqc+Lp+//M7HQz293y6n8AHwD9zGwS8BLwf8DnwLtArzTNVGA9iyK9l4Et0vBnieNnlcgdK1TNNrUCTjSzFinY6Jr27YPAGOAWM+tOvHitBRGAQOzH1VKg8S6wu5m1yrsuzgbaufsXDbZRtZR/zc6rF1W06CjlOAwBtiOuMSeZ2YZEwNqGeA/ay8CmVAS2Y9PwXMXdDS2KOi+zKDIcRxQHW979AuAtYFMz+62ZTTSzi82sDXGdOhGY6O69gOeBoyxygJsB16S/mcBJqVhsPCm4LlhHg1Eg0sDSDeAgM7vVzMZalIN2gGX/yWZ2cHqy6AocBhyTRs0lss/+5+7HE0+lOxAHy1tUPKlNIS4KmxE3h7K03DUssu4uMrPbiSDmmoba5saWu0B6AYvKYB1SUcs26SS+EDgOON7MziaySRcTN5OpxE1nc2K/rg7k/keTiWDla+AV4MfpRrRrmiZ3MyoJKdjaL323YhfNvGPzvxZPqqcS+2AecLG7X0ds62dEcLaIyHUD+JZ4eutNHHdrm9kGKSflXWADa+RWD/nHQcHwDczsEDP7tUX5ey/gSCKw+hrIn/5DIv1/dfee7n4psX3vEwEMRJHB1kTl1AnE0+1GxE1k5xT4rnS5Y/mKBa5F9mtZCuKOIJ6g3wJ+ROynnxJBfFdifw0mchPHA99Ni/ggfXYGxgGbu/vnedfFWaRrW1aBXf72p/Pom7zfm5vZ+WZ2m5kdl4a1yAtsJxK5Djem398H+hPXkmbEdr8FtAQ2AnD3t9PwnJuIosBtievUQHfvB+xvZhemYIe0nN2JAOb7RDBzOfGgNY54qAJ4kgh6Nk3pu93dj3T3i939hRS8PAWsYdG44S913HW1okCk4e0EDCIOhuOJg+xKi1rT25nZlWa2dpp2a+Bn7j4UuAz4i7s/StwsJ1DxFuGXgY+JqPpdIkjB3d9L6+ueItsnzOxloq5IS+JA/jdwuLs/27CbXX9yJ3bhhbHwxmNRYfLoFHRdQ+zzYSn6n0LcTCe7+37AL4icpebEzadDyi6eD3ROF5xZQO6tlfOIgGMH4uLSgSie6Uuc0Nvmp6kx5d00LG//vEtc8HKVmLump6phVtHfRRlwKrEv9nH3m4H7iCe2uWmaacSNulMatq6ZtfaoXNcb+CAFN0uIfQOxD6cT+6ghtrew8h9Qvp1rpfMqV16+FXAbcDhxvfuMKHL7C/Ard/9fwQPBJ0TWea6fkOZp/KtU1HvZNP1t5+5T0zwbpu/7E0HaSsXMtrKorL0GFH/aN7MtzWy3vADzP1S8VPR54AJ3v5K4Pk0krlEAdxHHR3ciWMsVX82lIgfyRaCVmf3NzHI37jlE8NugxZ4F500l+dufjq8+ZjY8DfotkdPxMPC/NM3SXLGmuy8hAt7rievMY0BPd/8cWABs6lHPaAGwVV4aJhDHF+5+hLsfDPyKCOK2MbM/AIcSx+iRZtabyHlaDXjV3acTzco3Ih5K36KiTsprxH3DUrqPMLOjUkB1P9Df3ecAvyYeSkbUcnfWiQKRFZA7gK36crQjgHfc/WZ3n0hkc78NnE2ciDsCuRP7USqi4QVA93RjW5R+bwGQDpQ9iazOp4DmZnaHmY1Iy85VxrsQONLdd3D3N9399+7+D3efWV/7oCFYZO9ebGYPmNkTwPegcjGCmbVNF4bVzOwMM7uNuDAcA9xOZEV2JU6mE9z9UyL78c10c5lK7NfepBPVzFql77nmuFOB/dL3z4CngXU8aruf6e5dgOHExfX1lMYGfRKu6kk17RvPu3BOAY5N86xLZM/OIPbNMRY5b2VAc3d/3t0/SU98S4hcgi3MbK10E55DBGqvETeUc8xsIPAD4PG0vnvSONx9trsPdPc36xqYWWRJH2BmV5vZjRZZ+7nt/TbvOGiVN88xxEX8j0T9la2IYGiDlJ5L07H/PnHR3i63T/NW/TERmHZK6/omfT4GPGdmb6b9eglRFIO7/yC3re7+QinnhphZpxSsrWvRpD/3EPQl8Iy7f5U33dkpMDg0TbMrkZu4cfp9P/A9j4qlY6jImV1K3PBy080kcnnnpul2s2iye1Aav4O7v09UnHyTCIYhgvzb623jq1Bw3lRiZj3NbGzeoPWJYLwFUWx5P/CCu7+Tpu+arke/S/eF84Cz3f03REC/pUV/KbOJB0+Ih4YOHhVadyFyIeek5eWOzcVE8Whz4BziQfXzlIYBRO5SrrIrxP7vRORgzgXamNlq6d7RknhYfYsIrvcicnvvInI5SQH6Ve4+unZ7s25WyZfeNQQza+fuH+Y9IeVuOlVedNJT2WZEQJDL2ptrZk8Bf3D3c8xsKdDOzD4gLqLrmVlr4km0V1rH12Y2HfiFmU0mWsBMAualg/fHREDzOjA23Uwgbgwf56XHiLLFkn1iSyfpaUQTsluJfZJ7Ou0JXEwEa5+Y2RXu/li6KfcB9nD3RWY2l4oT8kWgV7pwTCQqYj6dxr1J/H/eJZ4SOhPBx67pqe/WNByPcurfpXQYkQX/F+Km9XDeMutrPzQj/qd7EhfkD4DfedSsz88eXoPIhj2bKFK6z93/4u4fWxRFXEZc8LYkjo99iRvK02k5z6Wgbm7eDXQGcYy1Im5Qs4GeFhVTDyNyUfYD/kxUgMPdLym2DTU91nKBVTqe+xLZ2QbcSVxQzzezc9L5sw1x01oHeN7MriByIY4jjoEPzOxM4mI9EPjCzO4lbojNgZ8R58U6aZ3l53Ba/yfAJmbW3t1np/PWgXO8mvoKpRqA5F+ziJvNn4jcjP0AN7OJRPDQy8w2dfeniKKU7wDPEP1blBHB5q7EuQmRzf/n9H0mFZUlZ5rZIqJOxB+I43MjYFY6Py8hzqUXgZ8T5x/uPpYI6nPeJnJXVnT7m5HijSrGfRfoByz0qGydO8ebEfWd5pnZ4e7+D+KcfMzjLe/Xp/R/YWY9gJOIm3qvNM03KXAdnD63Ih5+OhMBfLsUrFzgFZXDvwZmuvu8vOMOInheTARC+xC5uROIiuJbu/uXZvYRkUP1ZPrdmrgHzCOKETsTle+HEddC3P1N4iElW+6uv4I/4h92GHAt0Z79HeC3eeObpc/uRAXGe4gsrE2LLOsV4kDJH1ZG1OFoRVxo/y8NX5cIMPoRF9+/ExVUjyCKd64nstyuBvpmvZ9WYP82y+3DKsZ/UsXwbkQte4gs8IeISpF7ERfEXmnc3cAl6Xt/4sLbmSi/npz+t5ek6dZK/4+fEjfr1YHW1aU9fa5J5CY01D5qSwRFNxM5D78mAh6AXwI/ScfWnmmai4ncnTHA8Wm6SUQQc0gafgtxY94ije+d9sGx6femxA1jU6Js+hPgfOLCOgBYazlprtH+gPKXbR5OBOSF47cg6jDtkH5vSGR9r5P+Xzel82E9IsgfQTzlzcylgygHf53IwYIIKLckcha3I5pI3pHOqYOB1fPW3464CVvW50odjx0r9j39HpKOpa2JJ+oP0vGzG3Fd+QkROLwAtEzz9COKNCECmX3S93WAL9P37YngdnPihns4kcP7OFGsdUxN9idxbWhe1fbUZf6C8T3Iu04TRSd3Eef/9UQwu2bBPHulaTqmfXVikeWOAK4sMrw7UafvFiJnd93lnBPNgNWqmOZcIvCZBvRLw35K3KPapO+vEveNacD5aZqNiOtf7kE082O08E85IgXMbHsiO/AN4gY12t33MrP1Lbp0/tbd70mT/4+ISO8jsiGHmdlAd5+T9xQyC9guPXW4x5GxFRHcLCaeAHYxs/eJG0BLopfBX5nZP4issznERWM74B53f6JIuquM+rNULF1edTZo7gngRYty2NnEfnoGmOaR/X2Qmd1A3Gi+JS6Sz6fv7Yibz5PEkwHEk1pLoh7NGCLQ2JRo+XKrR12HacRFKKe8n5WCp8nytHuU7a6Q/FyAwnEeT/5vEZUmXzaz/xLlwa2JHInniNyNxUT58U3u/lp6Sts+Zbm/SNxgniKClr+4+4S07s2IQGUEcKyZnQN8kZZzR3pq/Y27f5SS9HpB2ptHMiuVoVdbwz6XQ5J3LHxGlHWflc67jdz9YeL/voRo2bJ7mmYq8LnHU+ZBwEUeuT5/JIpINgBmmVkfdx9vUS9oOtGSYCYRTK1NOlbc/d8px7IH8L5X5CLi7h9Wtx2lIrc/85+ci323KNY6zd1/QmTZHwlcRdywPnL3C9L0PYhzYw6wlbt/YdG/xYsWRTlrEE/mOxBBRleiUmNfdx9rZtOInJa/ErmNbwIj3f1/RdKey3Go1DleFedCdbnOmwFnufvPqprforn29kTQ3gH40Mx+7pEbcAjRhPV94oHvO0QumxPB0zR3v82ild0ZREuxXL9NHYmHhHWoOLZyddrco7h0EpEDVyzt5f+rvM9vKahjlDfdB8TD6qfAwSmH+LtErnxXos7abKKO01m549jd51GRQ1ySVEdkWePcvYe7H0GU/39gZtsBDxBZmcdatLaAuADOd/e7PVoYrE48OUI8lQH8gzgBDso7ofYHZqcL9x1EvYRhxBP/SUTlOtz9X+4+2N2fIZ7kvyRli9qyzTC/re6EbQxW5MV6+emyqKC7gZl918weMbPR6SJRvoj0+Qsi67wVcaLfD/zEzNYknrQudPdt0vBtiWBjCRWVI58nZRMTN6PHiWzeqURxxK3ufnJ+QGdJ4TYt7+ZaU1a8W/Tyug6F06avs0jNQYkcsunEcfU08LG7zyVyNV6hovnfZGK/rUUEcPu6+/g0/PdmdpOZvUDciFq4+wPAmcDB7r6ju9+R0jYnLwgpdrxV12yxuUVZeb/C7U3jyyx6IH2KKIq8jOht8iIzO9ajMt8s4kl0E+DKtIhfp2PgHSpasCxN065HNLc9waL5+inAax71sg5MyzicqKSaq89zs7v/xN1fKbYdpcKiP5Ne6fsyXc2nYMPMrHP63iYFaP3TpB8TPStDHCudPeqCPEw098/10zGHCAa/BmaY2f7uvsTMNiUemDYgHtK6W/QsfCTxELZmmv80d+/m7n8grmlrk+q8WXqPTF7aczfq5V6zzGzNwuMvzxGkujpp2r3MbKSZTTCzU9L+Wo/IDR3m7r2J4pHD0zG4PpGbeHzathOIXLPDiYeVaWnRw4hcpIFEgA9xvd+DOCevIHJo8aiwml98alWc/7W9Xs8m7hHXEMHIXildR3nU6XjFoxOycStLMF3OSyBbptT+qMh+P4nIBn8VGJyG9SOy6foQ2fuX5803BLgxfV8tb/ixxJPC/US53oNA27zxVWaXEQd5lcUYGe+nnsClRLO7wnG5rMatiSeCYcQN4+9EFvDWxMVxJNHiYJn9QORkrEOUiU4mbrbvETfZ9YFHiIpiuX1/Rt68u1aR5leB3XL7tqGOn/R/K/p/JS5wGxBPM48Q9Sz2yP+fp88fE5XHbiGyys9Ow88AnkrfNyXqQlydfvcGxqTvWwEf5vYtceE6ncgdKnpM1eVYS9tb+L/bh7g4bwB0ykv3y8TN8Og0bBrRVBiiF807iKDrB2n+NmncLlTcDK4ggkmI7O5haT0bE2Xn44mbyg5ZnyP1dDx9j8hZyB/Wgcj124V4QHqFyO04II0fTvSB0iL9nk0EIFDR98lOwFDS+Zt+35L2/1FpH/41HXs/zlt3Z6BbseOGivO+JVFMU21xXnXnT/rehVR0XTBNbvy9wBHp+3rADcT1phNxbflV2ld3Avul6Qal35sBvydd2/PPzyLrs3RMXwu0yug4aEVUas38mKzvPxXNFJeLVKcR2f3vU9GB1aT0twPxxHmVmd3o7rOIp/JZUN5LI+n7HRY1r7cGXnT3aZVWljvSI7v72/xh3kgdylQnPwuxwGJi/3QBplq0UjibeFp90swuJ1on/Ai43907mNmvgB95VNT9hNiP3YFn89dhZq08nowxsyXAVHf/zMxuJYpYmhPltc3T09YlHsUsuSzr5wq2oYVHhbAdcv+b+tq3KYv0CKJN/lQv6KvD3d3MtiZyaXoTF7RniKzWXxAVAH9sZhM9LxeCuAGcSGSDX01qjks0A/xF+j6DuOBeY1GU15moQ4G7v52exNq4+wKiLHmZYr18Xk2xGREcjPK8Yoz8eSxaaq3l7otSugcQxSpXmNl4oqLfqR6VEnPeIHI1IM6zRcTxMDnNX0Y8oS4igs9PiZvjyWaWa8XzV3efn9LwR48n8pWGRTFa14L9khvXjChWGJ5+9yQqei4lrhWbEoHFTunvJynHaxJxM16PaF03hQhohhLBfF/iWFhKdCT2FdF44RsiKL7T4tUG2xOVtsfl0uTRTUAufUYEIfkV+PGo1Du1Btu+zLWl4Bhcm3hvVnciV/Nxj6f93DzvpH0AcS3pQDTLXmRmdxD1Jm5M+6BNmu4FonhzIXFuXZe2Y2six+HXwGzLK35MaXw0/VVKP43UACBdDz9v6PVkQYFIEXknxjQi6628Pwl3/zRlVb5LZPFuQrywqQ/xFFD0HRse5ZFv5n5XcQJmHnTkWF6Lh8J0WpS/702Up35JRZPjQ4gbxY/T9zvc/Qgzm0pk1UJkXx6Wvk8nLnwb5++PFFicmAKbMuLi8tM0z1XACI/mt/nyA79vC/evV7Tt/5o6aqyAjIoy4inETfhud5+dty3vWHQdvUG6Ab9uZmek9b/m0YcMZnYS8aRc/t4i4snOq9iO3HauQdy0vksUff3R3Z8ws78SgUWuLDzXqmQfohJdGfC4RUuJ24mb3A3u/nxKy5cedQmaE0/qXxE3w1xx5nyiFVJ3orx7TeCnFq2gdiVyv95L676YqM/yfn7aq9uuUmHL1q/5hnhyPziNzwWvlo7lAVQUPywmcmN/7u73mdkw4Ct3/8qiZcanRH2Y14lztBVxE/6A2IdDiRv6ge5+t5k9TeQwbU/kJgwhNR119xepKIYoKu3vOl+38s753Da3Io69nYmcwuZE0domRMD1eZrv2zTtAiJYgQjKO1DR6d5LRAXlT4hANtdp2DvpGr6Fuz9lZoOJSqpTiBzbl9J0y2yXFbQES+kv+WOu1KmOSPVyuRzzgJ3M7AgzO5eoPf2YRyWgBcTTwq+J2uQfV7Uws6p7KcxKKr8s1vV37il3HYv+Brqk3y2JbR1I3NR2Bdqmp+FjiZrjE4g+PfZKF935QMv0fSqw1My6pH3wEZFNnOsiPPceigVEIHg5sKNHPRk83p0zNS/tVXVEVC/7t7r/mUUF5iOJ3JCqArJviYDs5bTt+QHZl+n7MgFZ+vyUuBDvmUuHVfRZcxjxjpxc2t5x9/+4+4wUcEBUPv2tV1Ray3+6q86xRGDxJRFQzEjDnyCKJrGo3PrbFHR9B7jU3bsRAddVRK7VFCq6RZ9H3EzwqBuQS/vjVLyr5VOiPkPf9PSX64FyKlH/4Ky87V1cGISUmrxza5lXOeSdX2sSQcAuFvUa/kbaT1TUM/ucij4nPiYqY2+Qfo8lKoyuTtxspxNFb+OIIORHFhUtW1JRb2oUUUQJUfm9s7v/2N0nuft0z6uIbUXqNtRhHxQ9Ry36ANrezHbMC0JyPR8vJgKIZ4jitn+5+33pnMj5kugjox2UP+x9SsXbsDsQOUNG6h3YogdcgAuIoBd3fylt/x/cfUx150dj5Hw0RcoRqYZHrfFmxH46lqjI9CVxk811mfsx0dyr2ieHtLzMDmKLfgD6kl7v7O7DU5rKn2isohZ+C6I+wcHEU+5awDdmdgBxcfuhu2+a5jHi/SNLLFp05HpmXGpmHxJP6W8S2cRre7R0+IJ46plMPJ2tSaqompcLc+fytqk+n0bSduRa+OQ/8ZQHZMTNYI67T84LyLoQT427AvPzArI9PVpPvQHMXU5ANtmiD4BcQJYLQJYS9ZHezdsv36T/033FtiEFG7ns8bvqsB9WI/43f3L3f6dhuRvJq8B3UlA6gLjgtyEq+h2ebiTNgL+l9C8ignaI4qTjUiB/LXHz/cajRUZHi95aPzOzR4hKrLj7VbVNf1ZyuU1FnpaLPVUfTdxsuxE32U+IIqoX3P38vPlzRVYvkXpPJoLPN9O8EAHH/kSx1VyieOtgd/+jmV1HtAYrIyrrLkzLfY5odbVMsXDhTbgm1yyLoqVdiaK37sCDXlHpufwcNbMNPRU9WhQxDU/bvsDMHiaakh9AdHQ2x6PjQCxyDLub2dr5gUi6Vo0l3p3SLQUilxMd9p1M5ICcmc6Zu4lWVwvT+XNHwTaUfB9LqzIFIlXIXdSJJ8LPUu7Hi3njcxfn26gISkpSOulfI/ra+Ijo1nctd7/Z4lXuvyDqLrQwswvd/WmLdw58n+gAbLpF89G9iRYqT5jZFu4+hchC75Ju1KOJjpAusmi5MIF4msv13bA+Ebj9kfSeCY/u7KtKd9GL4wruizJWjoAsV5R0a+E25AVHhcVP9bGfmhE5UT+06JH1M+Kp+wOitc7pxHXjT+7+UQq81iZ6kHyh4Gn6fWBri1ZnnxPFD2cSN9ZPiZsGxIvNPkvbs9x6BVmzeCPvPI+6MEDxoNiiCPNIomjhWaJS7RIiCLsReMRTUaFFUcFO6ftqXrkI8b9UNC//kshp2jH9foPIieuYAt8xRAdbzTxaDH2vim0obJZeo+IVi2K73kSOzGgi4LyKCB6uJHJbfmBmC939ITNbj6iz1J1oNnuNuz9O5BZe51F/rmua7y4iGLmeKOLchGgsMJ0IdIq1anvLzOYR5+mb7v6kRTPilkTvyrncxZl581TVRLgkcqmbIhXNVCHvqfK9FIQA5U0T8+tP3Frs6bTEvAEscvfT3f0i4J9EXxMtiZvKaOKCeQrRRHIrooz5RSoqeD1NxYvdvqTiHSzrElnr3YhKdOuZ2TNE08BcfYXHiZYR7wG4+wOeVzGvqqxfr2HzvppKAdlUIov6SyIgOzWN28TMrjOz0cCzZrZnCgZyAdkp7r47Ud9ib6LG/RPppgRxs1itICCjSEC2JRXvffgjUYcDdx/q7je6+zJBbVVZ2/W5b/KW+RVRGXQTotXFEUTvqwNzaSVa4xxkUUflA6Lsfmcih2dDi/dWtHf3kcTNYyhRvPYG8YS6jbvv5u657qTnNtT21BczO9XM/mdREfQBoqfXbmlcMzPrYmY/MbOBVtEcdh8it+KPROB6KxFsbkDUF9rfogvxZkQw2h2WrcfkUSy5lpl1SsfkB8RDw/oelUJPJM5x3P0Ndx/hlStM595DlN+svsrAo9j5mM6PfxMPNJcS58T+KdfiNeBad/9jCpybUVFseChR52RnonXXH9I5sh0V70J6i3hA2svdf0sENb9IyzmSiiLyYRbdzueC/9yD9F+JYs0BaXnvuvvreUFIyR5XEpQjUks1fXIoJemEnG9m/TyKkFoRlW2/dvf3LcpN/0DcYDoQT1sPEk3+Niey5HNPV7cSQcpJFl1+f5d4Ytne3W8ws0uJSpqvu3suK3guBayayrANqDwgS2k4geiq/Q4qArLLiZ48r7fodj8/IJtORUA2k4qAbArLBmQ/TgFZG+DvHnU3Hgee99RqyqMPj3JVXTQbO7s45UoMSDkibYkn+OOIzvsmE31xfAe43aKb+6uIejEvEHVEJlLRr8kgr1xckV/GX7IsupE3r+hjpA/RBfj30430UqKviVwX+tcROV5LiX1xOHG+/MPdHzezCcTT/vpEk9HdiePlaqKo9z9EXau1iGPxy5SbljtP/kxU4B6WzuGDUjqbu/uYgrQXVqisaW7Hhu7+UbpetCXOhx1TcHwp0bPvTWnaNUhv9ybqWvQ3s98QOY1bUFGv6AjgTo86P89bVDz+LqkZPXHMQNTl2Cx9/x4R8C8hmp9PMbO7iKDmdSrqduRyDZ9PuW/l7yPK23YFISsBBSJNx1iiBcJTwMnANe7+dQomjgBecfeBFmX4e7n7cDNbQAQiEDeXnxJ9YPyVqHw4gGg6OjqXTe3RiuNZlqOxb65pnQrIaiilaxGwKG1frhhiHrCtR8+/bxFP/X8gArjrPJoJl8vi/1xbVqSXWKK4Y1+imXAHIocvVwmyjAgwb01P5d8HnnP3X6cb9Asp960d8ZLFNT16yv2G6DPnOeLGi0Wz2XYexVyjiFyniUSrq/epKC64nugfY1viOAOKBxl12ecp5+4FMzvS3Z8nmmo/QhTzrE1UOi6vs5Nyzt5OP98n+nDpTrxOoQ+Rs/prIjewN1HsQtq+bYkefc8zs1fTfjLgHjPbMm3nvLT++9L6/kd6w20xKed1RlXjpbQpEGk6HiI68ZlAtIU/yczmUPFa7h+l6fpQUezyKRVvIX3DzH5LdIf9DVG8889iKyrx7NAmH5AtTypa2N6iN88yIlv9bo8Xad1E6knT3R8kArWcrwqXVSpSwLAfEVyUEbljV6YcgPK6QcSNf3Uix+GHaR/cRVS23c/MXiJye74m+qSZaFF5916LeldfWvQ62pbo4+JA4pyDqLfQ3Cqad/dM6zo3jf8N0a3+/Fy6c+eRR4XL24ngud6lHLtTgEstmpsfDVyfcmW+RxS/5fbTesS+7EVcV94hzqvH3f1Ri3oq5xM5Q/cRfdxsRrTiWpcoqn2WCD5+Q5xD//CoyPoRVXSBkPv/lPC1RepIgUjTMZboVv5KADM7hKgwtysRmNxj0WLiXsAtKiEOJbXbT8HFI/kLrOJJsqSe7ItQQLZ8XxJPuDsS++V04NW0Pc9nmrK624UIPJ8iWvU48R6aRUTRyA5ELsdQd7/eonO4HYgu8hel4GwacK67P5OKZ8ZYdKr2JlHn4Zm0rjWJwPVqojnq3y0qXr7s7qNTDsu/iCD4bSjPgSoPQIodOym3qVKOU31J6/tPKpI5iwi0ckU+i4j6Gu2IYsitiECkFVHx+icpWO+S0rko7Z/JxPFzE5EDshaRq/F42rbR6a9YepYJOkoxaJf6YSvndVLqwqKH0jKPCoZYdFA1hnia34bovXRmNfOvrDfWcunp9Wl375h+H0K8u2FXoh7EAUQ9h3uJJ74TiAvu5x4tYpbZB1UFZFI6UhHAeR4tNgrHHUoECdPN7B3iVQGPmdljRGduj5iZEW/ZfshT03IzG0kUNfyNqCsyjwhaewK/dPd3zawdURdiBvBSsaKUUjqvUgBwK5ErcaBH8+pOpKIiz+u11uIleX92991SbspAIuekF9F/zuGpfkl553fVrFOBRhOmHJGm5T/EU02u6exJeePKixGqygItlYvlivDos6OtmW3i7h949E65P5Flfz3Rv0JhQJb/ZtZlauIXu7lI6bB4udlHRBECVtHdf86rRJ2GnkSR265EUcwkIlfokfR/H0/klN1pZnsRuQZj3X1mqhN0GpEb8gd3fxfAozO5kUXStMybV0vEekSF6wuI7dw5FdvcBYy0eIv4K8R++AHw7zTfJKKzvveIoqxXvXLz4NmwbEXaNE4BSBOnQKQJcfdDoPJFsIos4FX9wtDkA7ImZi2iUnLuDcaFgeMPiArLO5nZ4URfJxcST/cnpGKGTYkb8MNmtjNR3+FRohUVHk3Tzyu28pSbUum4KeFjqA/RUmWYRV9Ct5rZJR5d859B1B35E1FX6gVSJVTP6yStOk3g2iJ1oECkibFlOzIq1Qtig1FA1uR8RNR76Ul6h42ZLU65HGVEs9HcCwU3A/qm4OMBogLzo0THhX8Herv768VWkgKOYr3zrkzn2FlEfQ7c/epUybS/mb2Z6pCM9vQyykLFAi6RmlAdEWmSCgMyWXWlG+RAouhkv7x6C/2IVitrEnU81iCe8psTrVfm2LK9nOaW2Yy4fq4yx5CZbUx0JPY3r+g/pappVa9D6o0CERFpEiw6rltCNKPtTDQ3/qm7v2bRDf2H7j6ryHxN7j0kBbmFy9TrEKlPCkREpMkws/2I+kEveXQ5v0yxXFPvr6KUWvFI06BARESaLN10RbKnl96JSJNi8ZI6VawUKRHKEREREZHMKEdEREREMqNARERERDKjQEREREQyo0BEROrEzI4zs5fN7FMzW2hmr5pZ/kvR2prZxan30vpc78lmdnB9LlNEsqNARERqzcwuIN7S+ihwKHAs0SX6gXmTtQUuAsrqefUnAwfX8zJFJCN614yI1MUZwF/c/Zd5wx4ys0vqsjAzW8vdv6yfpInIykQ5IiJSF+sBHxYOzOsWvAzIvRzuaTNzM8uN2yP93sfMHjSzz4Ab0rizzWysmS0yszlm9pCZbZFbvpmNBrYDBuWWaWbH5Y0/0czeMLOvzGy6mS3zRlwzO8PMZpjZ52Z2v5n1T8vZI40fmdZTON/FKU2r1WF/iUgVFIiISF28AvzEzAaZ2QZFxs8mXjQHcDqwU/rLNxSYQBTnDE3DOhJByUHAScQL6P5nZuum8T8G3gIezlvmfwDM7Fzgz8D9wAHp+2Xp9fWkaQ4hXmP/IHAI8FreuvPTtZuZdc6bz4BBxAvhlnkJnojUnTo0E5FaM7OtiRt+Z8CBN4F/Alfnvd22J5Ersqe7j86bdw/gaeBadz+zmnU0J96OOxc43d3vSMPHARPd/bi8adcBPgCucvdL8oZfStQp6eDu35jZWGCuu38/b5qbiDfz7unuo9O7ZqYBt7n7RWma7wFPAr3cfWItd5eIVEM5IiJSa+7+GtCNyM24CTDgQmCcmbWu4WL+UzjAzHY0s8fNbD6wFPgCaA1suZxl7QS0Au41sxa5P+ApYGOgY/q9DZEbkq/S7/Sm2duAY3NdwQPHAeMUhIjUPwUiIlIn7v6Vuz/k7me4e3fgRKALMLiGi5iT/8PMNgUeI4KaU4Cdge2JHJE1l7OsDdPnG8DXeX9Pp+Gd0jTNgXkF8xb+hghENgP2NLO1gcOAYcvdIhGpNbWaEZF64e5DzexKoGtNZyn4vS/QEjjI3T8HSLkYbWqwrAXp8wAKApzkbeBL4Btgo4Jxhb9x92lm9gSRE9KZeGgbUYN0iEgtKRARkVozs7buPrdg2EbAulQEAkvS5/JyM3LWAr4limRyjmDZ69SSIst8gQg0NnH3ZYp88tL4KlER9i95gw+sYvKhRC5ID+B+d/94OekXkTpQICIidfG6mT1AFKXMJYoxziHqdAxP07xPBAeDzGwR8LW7j6tmmU8RRSe3mdlQIgA4B/i4YLq3gH3MbB9gPvCeu883s4uB68xsM+BZIhdjS6IS6iFp3iuAf5rZDUTdkJ2BXMXVbwvWcz9R/2Vb4ILl7RARqRvVERGRuriU6DH1eiIYuYyon7GDu78H4O6LiSa42wHPAGOrW6C7v04UhfQD/g38H/BDYFHBpL8lWumMTMv8QZr/SqKFzH5EL68jiCbEz+Wt41/AT4meWe8n6qCck0Z/UpCer4BHgBnAE9WlXUTqTs13RaRJM7NfA78C2uT37prqp0wHhrn7hVmlT2RVp6IZEWkyUj2WC4jWNF8AuwK/AIbmghAzWx3oTeTIbEDl+iQiUs8UiIhIU7KEaNVzLFGxdjZwHdEHSs4mwEtE3ZdT3H1mYydSpClR0YyIiIhkRpVVRUREJDMKRERERCQzCkREREQkMwpEREREJDMKRERERCQzCkREREQkM/8PmlHoGn0gaBUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 612x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary_plot(dataset_order, strategy_order, 'train_time', 'Time, sec', st_d, nm_d, r=0, baselines=baselines, \n",
    "             sorter='test_score', k_limit=20, output_dir=OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3e8bdead",
   "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>Dataset</th>\n",
       "      <th>K</th>\n",
       "      <th>Otto (9 classes)</th>\n",
       "      <th>Dionis (355 classes)</th>\n",
       "      <th>Helena (100 classes)</th>\n",
       "      <th>SF-Crime (39 classes)</th>\n",
       "      <th>MoA (206 labels)</th>\n",
       "      <th>SCM20D (16 tasks)</th>\n",
       "      <th>RF1 (8 tasks)</th>\n",
       "      <th>Delicious (983 labels)</th>\n",
       "      <th>Mediamill (101 labels)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tabnet</td>\n",
       "      <td>-</td>\n",
       "      <td>0.5363+-0.0064</td>\n",
       "      <td>0.4753+-0.0126</td>\n",
       "      <td>2.7197+-0.0235</td>\n",
       "      <td>2.4819+-0.02</td>\n",
       "      <td>0.0193+-0.0003</td>\n",
       "      <td>87.3655+-1.3316</td>\n",
       "      <td>3.7948+-1.5935</td>\n",
       "      <td>0.0664+-0.0008</td>\n",
       "      <td>0.0859+-0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SketchBoost Full</td>\n",
       "      <td>-</td>\n",
       "      <td>0.4697+-0.003</td>\n",
       "      <td>0.3114+-0.0009</td>\n",
       "      <td>2.5865+-0.0025</td>\n",
       "      <td>2.2067+-0.0003</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>91.0142+-0.3397</td>\n",
       "      <td>1.1687+-0.0835</td>\n",
       "      <td>0.0619+-0.0001</td>\n",
       "      <td>0.0747+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost (one-vs-all)</td>\n",
       "      <td>-</td>\n",
       "      <td>0.4599+-0.0028</td>\n",
       "      <td>0.3502+-0.002</td>\n",
       "      <td>2.5889+-0.0032</td>\n",
       "      <td>2.2208+-0.0008</td>\n",
       "      <td>0.0166+-0.0</td>\n",
       "      <td>89.1045+-0.495</td>\n",
       "      <td>0.925+-0.0308</td>\n",
       "      <td>0.062+-0.0</td>\n",
       "      <td>0.0758+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost (multioutput)</td>\n",
       "      <td>-</td>\n",
       "      <td>0.4658+-0.0033</td>\n",
       "      <td>0.3085+-0.001</td>\n",
       "      <td>2.5698+-0.0025</td>\n",
       "      <td>2.2036+-0.0005</td>\n",
       "      <td>0.0161+-0.0</td>\n",
       "      <td>90.9814+-0.3652</td>\n",
       "      <td>0.8975+-0.0384</td>\n",
       "      <td>0.0614+-0.0001</td>\n",
       "      <td>0.0754+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4975+-0.0031</td>\n",
       "      <td>0.3339+-0.0017</td>\n",
       "      <td>2.5923+-0.0024</td>\n",
       "      <td>2.2282+-0.0004</td>\n",
       "      <td>0.0169+-0.0</td>\n",
       "      <td>90.7613+-0.3989</td>\n",
       "      <td>1.3151+-0.0721</td>\n",
       "      <td>0.0624+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4964+-0.0042</td>\n",
       "      <td>0.3217+-0.0019</td>\n",
       "      <td>2.5938+-0.0026</td>\n",
       "      <td>2.2284+-0.0002</td>\n",
       "      <td>0.0168+-0.0</td>\n",
       "      <td>89.5284+-0.8352</td>\n",
       "      <td>1.2823+-0.1364</td>\n",
       "      <td>0.0624+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4715+-0.0035</td>\n",
       "      <td>0.3155+-0.0014</td>\n",
       "      <td>2.5974+-0.0019</td>\n",
       "      <td>2.2183+-0.0005</td>\n",
       "      <td>0.0168+-0.0</td>\n",
       "      <td>88.7442+-0.6346</td>\n",
       "      <td>1.186+-0.1366</td>\n",
       "      <td>0.0623+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>0.3151+-0.0014</td>\n",
       "      <td>2.5987+-0.002</td>\n",
       "      <td>2.2116+-0.0003</td>\n",
       "      <td>0.0166+-0.0</td>\n",
       "      <td>89.8727+-0.3126</td>\n",
       "      <td>-</td>\n",
       "      <td>0.0622+-0.0001</td>\n",
       "      <td>0.0746+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>0.3146+-0.0011</td>\n",
       "      <td>2.5945+-0.002</td>\n",
       "      <td>2.207+-0.0005</td>\n",
       "      <td>0.0163+-0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.0622+-0.0001</td>\n",
       "      <td>0.0747+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4677+-0.002</td>\n",
       "      <td>0.3175+-0.0009</td>\n",
       "      <td>2.5693+-0.0022</td>\n",
       "      <td>2.214+-0.0004</td>\n",
       "      <td>0.0163+-0.0</td>\n",
       "      <td>87.9358+-0.4112</td>\n",
       "      <td>1.1495+-0.0674</td>\n",
       "      <td>0.0622+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4636+-0.0026</td>\n",
       "      <td>0.3089+-0.0013</td>\n",
       "      <td>2.571+-0.0032</td>\n",
       "      <td>2.2083+-0.0003</td>\n",
       "      <td>0.0162+-0.0</td>\n",
       "      <td>86.7842+-0.5547</td>\n",
       "      <td>0.9965+-0.1012</td>\n",
       "      <td>0.0621+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4642+-0.002</td>\n",
       "      <td>0.3055+-0.0012</td>\n",
       "      <td>2.573+-0.0025</td>\n",
       "      <td>2.2052+-0.0005</td>\n",
       "      <td>0.0161+-0.0</td>\n",
       "      <td>86.2964+-0.4398</td>\n",
       "      <td>0.9944+-0.1015</td>\n",
       "      <td>0.062+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>0.3051+-0.001</td>\n",
       "      <td>2.5757+-0.0019</td>\n",
       "      <td>2.2041+-0.0005</td>\n",
       "      <td>0.0161+-0.0</td>\n",
       "      <td>86.6865+-0.283</td>\n",
       "      <td>-</td>\n",
       "      <td>0.0619+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>0.304+-0.0014</td>\n",
       "      <td>2.5759+-0.0024</td>\n",
       "      <td>2.2037+-0.0004</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.0619+-0.0001</td>\n",
       "      <td>0.0745+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4566+-0.0023</td>\n",
       "      <td>0.2848+-0.0012</td>\n",
       "      <td>2.5674+-0.004</td>\n",
       "      <td>2.2096+-0.0005</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>86.1442+-0.4824</td>\n",
       "      <td>0.9058+-0.0442</td>\n",
       "      <td>0.0621+-0.0001</td>\n",
       "      <td>0.0743+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>2</td>\n",
       "      <td>0.4583+-0.0029</td>\n",
       "      <td>0.285+-0.0011</td>\n",
       "      <td>2.5673+-0.0026</td>\n",
       "      <td>2.2076+-0.0006</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>85.8061+-0.5534</td>\n",
       "      <td>0.9056+-0.0582</td>\n",
       "      <td>0.0621+-0.0001</td>\n",
       "      <td>0.0743+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4607+-0.0032</td>\n",
       "      <td>0.2866+-0.0014</td>\n",
       "      <td>2.5681+-0.0019</td>\n",
       "      <td>2.2052+-0.0006</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>85.8565+-0.3116</td>\n",
       "      <td>0.9193+-0.0781</td>\n",
       "      <td>0.062+-0.0001</td>\n",
       "      <td>0.0743+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>0.2881+-0.001</td>\n",
       "      <td>2.5713+-0.0024</td>\n",
       "      <td>2.2043+-0.0003</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>86.3126+-0.371</td>\n",
       "      <td>-</td>\n",
       "      <td>0.062+-0.0001</td>\n",
       "      <td>0.0743+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>0.2907+-0.001</td>\n",
       "      <td>2.573+-0.0031</td>\n",
       "      <td>2.2038+-0.0004</td>\n",
       "      <td>0.016+-0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.062+-0.0001</td>\n",
       "      <td>0.0743+-0.0001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dataset   K Otto (9 classes) Dionis (355 classes)  \\\n",
       "0                   Tabnet   -   0.5363+-0.0064       0.4753+-0.0126   \n",
       "1         SketchBoost Full   -    0.4697+-0.003       0.3114+-0.0009   \n",
       "2     XGBoost (one-vs-all)   -   0.4599+-0.0028        0.3502+-0.002   \n",
       "3   CatBoost (multioutput)   -   0.4658+-0.0033        0.3085+-0.001   \n",
       "4              Top Outputs   1   0.4975+-0.0031       0.3339+-0.0017   \n",
       "5              Top Outputs   2   0.4964+-0.0042       0.3217+-0.0019   \n",
       "6              Top Outputs   5   0.4715+-0.0035       0.3155+-0.0014   \n",
       "7              Top Outputs  10                -       0.3151+-0.0014   \n",
       "8              Top Outputs  20                -       0.3146+-0.0011   \n",
       "9          Random Sampling   1    0.4677+-0.002       0.3175+-0.0009   \n",
       "10         Random Sampling   2   0.4636+-0.0026       0.3089+-0.0013   \n",
       "11         Random Sampling   5    0.4642+-0.002       0.3055+-0.0012   \n",
       "12         Random Sampling  10                -        0.3051+-0.001   \n",
       "13         Random Sampling  20                -        0.304+-0.0014   \n",
       "14       Random Projection   1   0.4566+-0.0023       0.2848+-0.0012   \n",
       "15       Random Projection   2   0.4583+-0.0029        0.285+-0.0011   \n",
       "16       Random Projection   5   0.4607+-0.0032       0.2866+-0.0014   \n",
       "17       Random Projection  10                -        0.2881+-0.001   \n",
       "18       Random Projection  20                -        0.2907+-0.001   \n",
       "\n",
       "   Helena (100 classes) SF-Crime (39 classes) MoA (206 labels)  \\\n",
       "0        2.7197+-0.0235          2.4819+-0.02   0.0193+-0.0003   \n",
       "1        2.5865+-0.0025        2.2067+-0.0003       0.016+-0.0   \n",
       "2        2.5889+-0.0032        2.2208+-0.0008      0.0166+-0.0   \n",
       "3        2.5698+-0.0025        2.2036+-0.0005      0.0161+-0.0   \n",
       "4        2.5923+-0.0024        2.2282+-0.0004      0.0169+-0.0   \n",
       "5        2.5938+-0.0026        2.2284+-0.0002      0.0168+-0.0   \n",
       "6        2.5974+-0.0019        2.2183+-0.0005      0.0168+-0.0   \n",
       "7         2.5987+-0.002        2.2116+-0.0003      0.0166+-0.0   \n",
       "8         2.5945+-0.002         2.207+-0.0005      0.0163+-0.0   \n",
       "9        2.5693+-0.0022         2.214+-0.0004      0.0163+-0.0   \n",
       "10        2.571+-0.0032        2.2083+-0.0003      0.0162+-0.0   \n",
       "11        2.573+-0.0025        2.2052+-0.0005      0.0161+-0.0   \n",
       "12       2.5757+-0.0019        2.2041+-0.0005      0.0161+-0.0   \n",
       "13       2.5759+-0.0024        2.2037+-0.0004       0.016+-0.0   \n",
       "14        2.5674+-0.004        2.2096+-0.0005       0.016+-0.0   \n",
       "15       2.5673+-0.0026        2.2076+-0.0006       0.016+-0.0   \n",
       "16       2.5681+-0.0019        2.2052+-0.0006       0.016+-0.0   \n",
       "17       2.5713+-0.0024        2.2043+-0.0003       0.016+-0.0   \n",
       "18        2.573+-0.0031        2.2038+-0.0004       0.016+-0.0   \n",
       "\n",
       "   SCM20D (16 tasks)   RF1 (8 tasks) Delicious (983 labels)  \\\n",
       "0    87.3655+-1.3316  3.7948+-1.5935         0.0664+-0.0008   \n",
       "1    91.0142+-0.3397  1.1687+-0.0835         0.0619+-0.0001   \n",
       "2     89.1045+-0.495   0.925+-0.0308             0.062+-0.0   \n",
       "3    90.9814+-0.3652  0.8975+-0.0384         0.0614+-0.0001   \n",
       "4    90.7613+-0.3989  1.3151+-0.0721         0.0624+-0.0001   \n",
       "5    89.5284+-0.8352  1.2823+-0.1364         0.0624+-0.0001   \n",
       "6    88.7442+-0.6346   1.186+-0.1366         0.0623+-0.0001   \n",
       "7    89.8727+-0.3126               -         0.0622+-0.0001   \n",
       "8                  -               -         0.0622+-0.0001   \n",
       "9    87.9358+-0.4112  1.1495+-0.0674         0.0622+-0.0001   \n",
       "10   86.7842+-0.5547  0.9965+-0.1012         0.0621+-0.0001   \n",
       "11   86.2964+-0.4398  0.9944+-0.1015          0.062+-0.0001   \n",
       "12    86.6865+-0.283               -         0.0619+-0.0001   \n",
       "13                 -               -         0.0619+-0.0001   \n",
       "14   86.1442+-0.4824  0.9058+-0.0442         0.0621+-0.0001   \n",
       "15   85.8061+-0.5534  0.9056+-0.0582         0.0621+-0.0001   \n",
       "16   85.8565+-0.3116  0.9193+-0.0781          0.062+-0.0001   \n",
       "17    86.3126+-0.371               -          0.062+-0.0001   \n",
       "18                 -               -          0.062+-0.0001   \n",
       "\n",
       "   Mediamill (101 labels)  \n",
       "0          0.0859+-0.0033  \n",
       "1          0.0747+-0.0001  \n",
       "2          0.0758+-0.0001  \n",
       "3          0.0754+-0.0001  \n",
       "4          0.0745+-0.0001  \n",
       "5          0.0745+-0.0001  \n",
       "6          0.0745+-0.0001  \n",
       "7          0.0746+-0.0001  \n",
       "8          0.0747+-0.0001  \n",
       "9          0.0745+-0.0001  \n",
       "10         0.0745+-0.0001  \n",
       "11         0.0745+-0.0001  \n",
       "12         0.0745+-0.0001  \n",
       "13         0.0745+-0.0001  \n",
       "14         0.0743+-0.0001  \n",
       "15         0.0743+-0.0001  \n",
       "16         0.0743+-0.0001  \n",
       "17         0.0743+-0.0001  \n",
       "18         0.0743+-0.0001  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = get_total_table(dataset_order, strategy_order, 'test_score', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "stds = get_total_table(dataset_order, strategy_order, 'std_test_score', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "\n",
    "\n",
    "scores = scores.set_index(['Dataset', 'K']).astype(str)\n",
    "stds = stds.set_index(['Dataset', 'K']).astype(str)\n",
    "\n",
    "total = (scores + '+-' + stds).replace('-+--', '-').reset_index()\n",
    "total.to_csv(os.path.join(OUTPUT_DIR, 'detailed_' + 'test_score_w_std' + '.csv'), index=False)\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "40d54788",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = get_total_table(dataset_order, strategy_order, 'test_score', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "stds = get_total_table(dataset_order, strategy_order, 'std_test_score', \n",
    "                         st_d, nm_d, r=6, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "\n",
    "\n",
    "scores = scores.set_index(['Dataset', 'K']).astype(str)\n",
    "stds = stds.set_index(['Dataset', 'K']).astype(str)\n",
    "\n",
    "total = (scores + '+-' + stds).replace('-+--', '-').reset_index()\n",
    "total.to_csv(os.path.join(OUTPUT_DIR, 'detailed_' + 'test_score_w_std_6d' + '.csv'), index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ad033c18",
   "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>Dataset</th>\n",
       "      <th>K</th>\n",
       "      <th>Otto (9 classes)</th>\n",
       "      <th>Dionis (355 classes)</th>\n",
       "      <th>Helena (100 classes)</th>\n",
       "      <th>SF-Crime (39 classes)</th>\n",
       "      <th>MoA (206 labels)</th>\n",
       "      <th>SCM20D (16 tasks)</th>\n",
       "      <th>RF1 (8 tasks)</th>\n",
       "      <th>Delicious (983 labels)</th>\n",
       "      <th>Mediamill (101 labels)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tabnet</td>\n",
       "      <td>-</td>\n",
       "      <td>0.7972+-0.0031</td>\n",
       "      <td>0.8936+-0.0032</td>\n",
       "      <td>0.3503+-0.0061</td>\n",
       "      <td>0.255+-0.0038</td>\n",
       "      <td>0.9967+-0.0001</td>\n",
       "      <td>0.9281+-0.0022</td>\n",
       "      <td>0.9932+-0.0037</td>\n",
       "      <td>0.9816+-0.0001</td>\n",
       "      <td>0.9709+-0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SketchBoost Full</td>\n",
       "      <td>-</td>\n",
       "      <td>0.8223+-0.0021</td>\n",
       "      <td>0.9227+-0.0004</td>\n",
       "      <td>0.3783+-0.0011</td>\n",
       "      <td>0.3343+-0.0007</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9224+-0.0007</td>\n",
       "      <td>0.9995+-0.0001</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9747+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost (one-vs-all)</td>\n",
       "      <td>-</td>\n",
       "      <td>0.8238+-0.001</td>\n",
       "      <td>0.9193+-0.0007</td>\n",
       "      <td>0.377+-0.0012</td>\n",
       "      <td>0.3326+-0.0003</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9257+-0.0008</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "      <td>0.9826+-0.0</td>\n",
       "      <td>0.9746+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost (multioutput)</td>\n",
       "      <td>-</td>\n",
       "      <td>0.8213+-0.0012</td>\n",
       "      <td>0.9234+-0.0003</td>\n",
       "      <td>0.3808+-0.0017</td>\n",
       "      <td>0.3352+-0.0008</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9224+-0.0006</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "      <td>0.9825+-0.0</td>\n",
       "      <td>0.9744+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8172+-0.0022</td>\n",
       "      <td>0.9192+-0.0006</td>\n",
       "      <td>0.3773+-0.0014</td>\n",
       "      <td>0.3275+-0.0006</td>\n",
       "      <td>0.997+-0.0</td>\n",
       "      <td>0.9228+-0.0006</td>\n",
       "      <td>0.9992+-0.0001</td>\n",
       "      <td>0.9823+-0.0</td>\n",
       "      <td>0.9747+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8171+-0.0016</td>\n",
       "      <td>0.9214+-0.0004</td>\n",
       "      <td>0.3772+-0.0021</td>\n",
       "      <td>0.3275+-0.0008</td>\n",
       "      <td>0.997+-0.0</td>\n",
       "      <td>0.9249+-0.0013</td>\n",
       "      <td>0.9993+-0.0001</td>\n",
       "      <td>0.9823+-0.0</td>\n",
       "      <td>0.9747+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>5</td>\n",
       "      <td>0.821+-0.0016</td>\n",
       "      <td>0.9229+-0.0004</td>\n",
       "      <td>0.376+-0.0019</td>\n",
       "      <td>0.3315+-0.0003</td>\n",
       "      <td>0.997+-0.0</td>\n",
       "      <td>0.9262+-0.001</td>\n",
       "      <td>0.9994+-0.0001</td>\n",
       "      <td>0.9823+-0.0</td>\n",
       "      <td>0.9747+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9229+-0.0003</td>\n",
       "      <td>0.3757+-0.0013</td>\n",
       "      <td>0.3333+-0.0003</td>\n",
       "      <td>0.997+-0.0</td>\n",
       "      <td>0.9243+-0.0005</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9747+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9227+-0.0007</td>\n",
       "      <td>0.3766+-0.0008</td>\n",
       "      <td>0.3349+-0.0006</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9747+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8228+-0.0011</td>\n",
       "      <td>0.9224+-0.0005</td>\n",
       "      <td>0.3821+-0.0009</td>\n",
       "      <td>0.332+-0.0002</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9276+-0.0007</td>\n",
       "      <td>0.9995+-0.0001</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9746+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8236+-0.0025</td>\n",
       "      <td>0.9243+-0.0003</td>\n",
       "      <td>0.3827+-0.0015</td>\n",
       "      <td>0.3338+-0.0003</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9293+-0.0009</td>\n",
       "      <td>0.9996+-0.0001</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9746+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8231+-0.0018</td>\n",
       "      <td>0.925+-0.0002</td>\n",
       "      <td>0.3795+-0.0022</td>\n",
       "      <td>0.3348+-0.0002</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9301+-0.0007</td>\n",
       "      <td>0.9996+-0.0001</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9746+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>0.925+-0.0002</td>\n",
       "      <td>0.3793+-0.0018</td>\n",
       "      <td>0.3351+-0.0004</td>\n",
       "      <td>0.9972+-0.0</td>\n",
       "      <td>0.9295+-0.0004</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9746+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9251+-0.0005</td>\n",
       "      <td>0.3795+-0.0017</td>\n",
       "      <td>0.3353+-0.0005</td>\n",
       "      <td>0.9972+-0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9746+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8258+-0.001</td>\n",
       "      <td>0.9285+-0.0004</td>\n",
       "      <td>0.3834+-0.0033</td>\n",
       "      <td>0.3338+-0.0003</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9304+-0.0008</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9747+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>2</td>\n",
       "      <td>0.8255+-0.0023</td>\n",
       "      <td>0.9287+-0.0002</td>\n",
       "      <td>0.3836+-0.0019</td>\n",
       "      <td>0.3344+-0.0005</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9309+-0.0008</td>\n",
       "      <td>0.9997+-0.0</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9748+-0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>5</td>\n",
       "      <td>0.8251+-0.0023</td>\n",
       "      <td>0.9281+-0.0002</td>\n",
       "      <td>0.3835+-0.0018</td>\n",
       "      <td>0.3351+-0.0005</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9308+-0.0005</td>\n",
       "      <td>0.9997+-0.0001</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9748+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9279+-0.0002</td>\n",
       "      <td>0.3824+-0.0022</td>\n",
       "      <td>0.3355+-0.0003</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>0.9301+-0.0006</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9748+-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9275+-0.0002</td>\n",
       "      <td>0.3805+-0.0025</td>\n",
       "      <td>0.3357+-0.0004</td>\n",
       "      <td>0.9971+-0.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>0.9824+-0.0</td>\n",
       "      <td>0.9747+-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dataset   K Otto (9 classes) Dionis (355 classes)  \\\n",
       "0                   Tabnet   -   0.7972+-0.0031       0.8936+-0.0032   \n",
       "1         SketchBoost Full   -   0.8223+-0.0021       0.9227+-0.0004   \n",
       "2     XGBoost (one-vs-all)   -    0.8238+-0.001       0.9193+-0.0007   \n",
       "3   CatBoost (multioutput)   -   0.8213+-0.0012       0.9234+-0.0003   \n",
       "4              Top Outputs   1   0.8172+-0.0022       0.9192+-0.0006   \n",
       "5              Top Outputs   2   0.8171+-0.0016       0.9214+-0.0004   \n",
       "6              Top Outputs   5    0.821+-0.0016       0.9229+-0.0004   \n",
       "7              Top Outputs  10                -       0.9229+-0.0003   \n",
       "8              Top Outputs  20                -       0.9227+-0.0007   \n",
       "9          Random Sampling   1   0.8228+-0.0011       0.9224+-0.0005   \n",
       "10         Random Sampling   2   0.8236+-0.0025       0.9243+-0.0003   \n",
       "11         Random Sampling   5   0.8231+-0.0018        0.925+-0.0002   \n",
       "12         Random Sampling  10                -        0.925+-0.0002   \n",
       "13         Random Sampling  20                -       0.9251+-0.0005   \n",
       "14       Random Projection   1    0.8258+-0.001       0.9285+-0.0004   \n",
       "15       Random Projection   2   0.8255+-0.0023       0.9287+-0.0002   \n",
       "16       Random Projection   5   0.8251+-0.0023       0.9281+-0.0002   \n",
       "17       Random Projection  10                -       0.9279+-0.0002   \n",
       "18       Random Projection  20                -       0.9275+-0.0002   \n",
       "\n",
       "   Helena (100 classes) SF-Crime (39 classes) MoA (206 labels)  \\\n",
       "0        0.3503+-0.0061         0.255+-0.0038   0.9967+-0.0001   \n",
       "1        0.3783+-0.0011        0.3343+-0.0007      0.9971+-0.0   \n",
       "2         0.377+-0.0012        0.3326+-0.0003      0.9971+-0.0   \n",
       "3        0.3808+-0.0017        0.3352+-0.0008      0.9971+-0.0   \n",
       "4        0.3773+-0.0014        0.3275+-0.0006       0.997+-0.0   \n",
       "5        0.3772+-0.0021        0.3275+-0.0008       0.997+-0.0   \n",
       "6         0.376+-0.0019        0.3315+-0.0003       0.997+-0.0   \n",
       "7        0.3757+-0.0013        0.3333+-0.0003       0.997+-0.0   \n",
       "8        0.3766+-0.0008        0.3349+-0.0006      0.9971+-0.0   \n",
       "9        0.3821+-0.0009         0.332+-0.0002      0.9971+-0.0   \n",
       "10       0.3827+-0.0015        0.3338+-0.0003      0.9971+-0.0   \n",
       "11       0.3795+-0.0022        0.3348+-0.0002      0.9971+-0.0   \n",
       "12       0.3793+-0.0018        0.3351+-0.0004      0.9972+-0.0   \n",
       "13       0.3795+-0.0017        0.3353+-0.0005      0.9972+-0.0   \n",
       "14       0.3834+-0.0033        0.3338+-0.0003      0.9971+-0.0   \n",
       "15       0.3836+-0.0019        0.3344+-0.0005      0.9971+-0.0   \n",
       "16       0.3835+-0.0018        0.3351+-0.0005      0.9971+-0.0   \n",
       "17       0.3824+-0.0022        0.3355+-0.0003      0.9971+-0.0   \n",
       "18       0.3805+-0.0025        0.3357+-0.0004      0.9971+-0.0   \n",
       "\n",
       "   SCM20D (16 tasks)   RF1 (8 tasks) Delicious (983 labels)  \\\n",
       "0     0.9281+-0.0022  0.9932+-0.0037         0.9816+-0.0001   \n",
       "1     0.9224+-0.0007  0.9995+-0.0001            0.9824+-0.0   \n",
       "2     0.9257+-0.0008     0.9997+-0.0            0.9826+-0.0   \n",
       "3     0.9224+-0.0006     0.9997+-0.0            0.9825+-0.0   \n",
       "4     0.9228+-0.0006  0.9992+-0.0001            0.9823+-0.0   \n",
       "5     0.9249+-0.0013  0.9993+-0.0001            0.9823+-0.0   \n",
       "6      0.9262+-0.001  0.9994+-0.0001            0.9823+-0.0   \n",
       "7     0.9243+-0.0005               -            0.9824+-0.0   \n",
       "8                  -               -            0.9824+-0.0   \n",
       "9     0.9276+-0.0007  0.9995+-0.0001            0.9824+-0.0   \n",
       "10    0.9293+-0.0009  0.9996+-0.0001            0.9824+-0.0   \n",
       "11    0.9301+-0.0007  0.9996+-0.0001            0.9824+-0.0   \n",
       "12    0.9295+-0.0004               -            0.9824+-0.0   \n",
       "13                 -               -            0.9824+-0.0   \n",
       "14    0.9304+-0.0008     0.9997+-0.0            0.9824+-0.0   \n",
       "15    0.9309+-0.0008     0.9997+-0.0            0.9824+-0.0   \n",
       "16    0.9308+-0.0005  0.9997+-0.0001            0.9824+-0.0   \n",
       "17    0.9301+-0.0006               -            0.9824+-0.0   \n",
       "18                 -               -            0.9824+-0.0   \n",
       "\n",
       "   Mediamill (101 labels)  \n",
       "0          0.9709+-0.0011  \n",
       "1          0.9747+-0.0001  \n",
       "2             0.9746+-0.0  \n",
       "3          0.9744+-0.0001  \n",
       "4             0.9747+-0.0  \n",
       "5             0.9747+-0.0  \n",
       "6          0.9747+-0.0001  \n",
       "7             0.9747+-0.0  \n",
       "8          0.9747+-0.0001  \n",
       "9          0.9746+-0.0001  \n",
       "10            0.9746+-0.0  \n",
       "11            0.9746+-0.0  \n",
       "12            0.9746+-0.0  \n",
       "13         0.9746+-0.0001  \n",
       "14            0.9747+-0.0  \n",
       "15         0.9748+-0.0001  \n",
       "16            0.9748+-0.0  \n",
       "17            0.9748+-0.0  \n",
       "18            0.9747+-0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = get_total_table(dataset_order, strategy_order, 'test_acc', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "stds = get_total_table(dataset_order, strategy_order, 'std_test_acc', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "\n",
    "\n",
    "scores = scores.set_index(['Dataset', 'K']).astype(str)\n",
    "stds = stds.set_index(['Dataset', 'K']).astype(str)\n",
    "\n",
    "total = (scores + '+-' + stds).replace('-+--', '-').reset_index()\n",
    "total.to_csv(os.path.join(OUTPUT_DIR, 'detailed_' + 'test_acc_w_std' + '.csv'), index=False)\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d4d76d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = get_total_table(dataset_order, strategy_order, 'test_acc', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "stds = get_total_table(dataset_order, strategy_order, 'std_test_acc', \n",
    "                         st_d, nm_d, r=6, baselines=baselines, output_dir=OUTPUT_DIR)\n",
    "\n",
    "\n",
    "scores = scores.set_index(['Dataset', 'K']).astype(str)\n",
    "stds = stds.set_index(['Dataset', 'K']).astype(str)\n",
    "\n",
    "total = (scores + '+-' + stds).replace('-+--', '-').reset_index()\n",
    "total.to_csv(os.path.join(OUTPUT_DIR, 'detailed_' + 'test_acc_w_std_6d' + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe7ccf10",
   "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>Dataset</th>\n",
       "      <th>K</th>\n",
       "      <th>Otto (9 classes)</th>\n",
       "      <th>Dionis (355 classes)</th>\n",
       "      <th>Helena (100 classes)</th>\n",
       "      <th>SF-Crime (39 classes)</th>\n",
       "      <th>MoA (206 labels)</th>\n",
       "      <th>SCM20D (16 tasks)</th>\n",
       "      <th>RF1 (8 tasks)</th>\n",
       "      <th>Delicious (983 labels)</th>\n",
       "      <th>Mediamill (101 labels)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tabnet</td>\n",
       "      <td>-</td>\n",
       "      <td>903.2607</td>\n",
       "      <td>1853.4115</td>\n",
       "      <td>1195.6204</td>\n",
       "      <td>2682.5084</td>\n",
       "      <td>672.1119</td>\n",
       "      <td>295.73</td>\n",
       "      <td>207.8435</td>\n",
       "      <td>2902.3544</td>\n",
       "      <td>1230.7093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SketchBoost Full</td>\n",
       "      <td>-</td>\n",
       "      <td>131.2795</td>\n",
       "      <td>23919.0901</td>\n",
       "      <td>355.5974</td>\n",
       "      <td>1146.1403</td>\n",
       "      <td>696.2490</td>\n",
       "      <td>597.8406</td>\n",
       "      <td>413.7743</td>\n",
       "      <td>19553.5613</td>\n",
       "      <td>1777.6927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost (one-vs-all)</td>\n",
       "      <td>-</td>\n",
       "      <td>1244.9438</td>\n",
       "      <td>18635.1964</td>\n",
       "      <td>1036.0248</td>\n",
       "      <td>4016.9352</td>\n",
       "      <td>376.3628</td>\n",
       "      <td>1432.8394</td>\n",
       "      <td>315.1049</td>\n",
       "      <td>15795.5608</td>\n",
       "      <td>2074.3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost (multioutput)</td>\n",
       "      <td>-</td>\n",
       "      <td>73.7665</td>\n",
       "      <td>18600.3721</td>\n",
       "      <td>436.6722</td>\n",
       "      <td>659.7310</td>\n",
       "      <td>9398.4015</td>\n",
       "      <td>798.6409</td>\n",
       "      <td>804.6936</td>\n",
       "      <td>20120.1416</td>\n",
       "      <td>10164.0577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>1</td>\n",
       "      <td>129.5913</td>\n",
       "      <td>783.7018</td>\n",
       "      <td>154.6307</td>\n",
       "      <td>174.2330</td>\n",
       "      <td>40.0038</td>\n",
       "      <td>458.9562</td>\n",
       "      <td>351.1528</td>\n",
       "      <td>213.8397</td>\n",
       "      <td>251.4459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>2</td>\n",
       "      <td>126.5449</td>\n",
       "      <td>810.4931</td>\n",
       "      <td>151.6850</td>\n",
       "      <td>207.5298</td>\n",
       "      <td>45.1278</td>\n",
       "      <td>476.2507</td>\n",
       "      <td>364.9863</td>\n",
       "      <td>229.3393</td>\n",
       "      <td>276.4852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>5</td>\n",
       "      <td>113.7105</td>\n",
       "      <td>1003.3881</td>\n",
       "      <td>146.3063</td>\n",
       "      <td>270.8648</td>\n",
       "      <td>59.4057</td>\n",
       "      <td>499.3633</td>\n",
       "      <td>369.8712</td>\n",
       "      <td>274.7669</td>\n",
       "      <td>313.6988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>1293.5372</td>\n",
       "      <td>138.1224</td>\n",
       "      <td>425.9713</td>\n",
       "      <td>69.9126</td>\n",
       "      <td>551.4483</td>\n",
       "      <td>-</td>\n",
       "      <td>375.7564</td>\n",
       "      <td>386.5528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>1889.2980</td>\n",
       "      <td>156.4791</td>\n",
       "      <td>705.4014</td>\n",
       "      <td>103.5214</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>575.1124</td>\n",
       "      <td>529.5114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>1</td>\n",
       "      <td>104.4247</td>\n",
       "      <td>835.5595</td>\n",
       "      <td>180.6419</td>\n",
       "      <td>198.0625</td>\n",
       "      <td>61.5073</td>\n",
       "      <td>485.6434</td>\n",
       "      <td>347.9057</td>\n",
       "      <td>230.6440</td>\n",
       "      <td>263.4639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>2</td>\n",
       "      <td>102.3624</td>\n",
       "      <td>880.4566</td>\n",
       "      <td>180.9462</td>\n",
       "      <td>219.6978</td>\n",
       "      <td>75.5351</td>\n",
       "      <td>491.4076</td>\n",
       "      <td>354.304</td>\n",
       "      <td>243.0265</td>\n",
       "      <td>273.3999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>5</td>\n",
       "      <td>116.3004</td>\n",
       "      <td>1087.0867</td>\n",
       "      <td>185.7413</td>\n",
       "      <td>299.7088</td>\n",
       "      <td>104.9205</td>\n",
       "      <td>528.8417</td>\n",
       "      <td>396.4812</td>\n",
       "      <td>314.3676</td>\n",
       "      <td>319.1161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>1404.1419</td>\n",
       "      <td>198.9258</td>\n",
       "      <td>422.4236</td>\n",
       "      <td>135.0571</td>\n",
       "      <td>590.4221</td>\n",
       "      <td>-</td>\n",
       "      <td>432.8620</td>\n",
       "      <td>399.5259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>2038.0721</td>\n",
       "      <td>213.2844</td>\n",
       "      <td>676.2324</td>\n",
       "      <td>189.2574</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>664.0449</td>\n",
       "      <td>559.8831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>1</td>\n",
       "      <td>89.6285</td>\n",
       "      <td>419.5824</td>\n",
       "      <td>109.9810</td>\n",
       "      <td>136.5652</td>\n",
       "      <td>26.3955</td>\n",
       "      <td>466.0371</td>\n",
       "      <td>331.2367</td>\n",
       "      <td>212.1800</td>\n",
       "      <td>235.9618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>2</td>\n",
       "      <td>87.1467</td>\n",
       "      <td>464.4464</td>\n",
       "      <td>113.4900</td>\n",
       "      <td>159.5843</td>\n",
       "      <td>29.3747</td>\n",
       "      <td>479.0236</td>\n",
       "      <td>340.9701</td>\n",
       "      <td>235.0210</td>\n",
       "      <td>243.3114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>5</td>\n",
       "      <td>107.955</td>\n",
       "      <td>629.9292</td>\n",
       "      <td>116.9194</td>\n",
       "      <td>233.3936</td>\n",
       "      <td>39.3908</td>\n",
       "      <td>528.448</td>\n",
       "      <td>393.1228</td>\n",
       "      <td>295.0743</td>\n",
       "      <td>294.3931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>895.8915</td>\n",
       "      <td>128.2305</td>\n",
       "      <td>365.4783</td>\n",
       "      <td>55.9317</td>\n",
       "      <td>594.7274</td>\n",
       "      <td>-</td>\n",
       "      <td>436.5177</td>\n",
       "      <td>369.2635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>1417.1877</td>\n",
       "      <td>149.0080</td>\n",
       "      <td>612.8040</td>\n",
       "      <td>87.0725</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>1259.7520</td>\n",
       "      <td>527.1915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dataset   K Otto (9 classes)  Dionis (355 classes)  \\\n",
       "0                   Tabnet   -         903.2607             1853.4115   \n",
       "1         SketchBoost Full   -         131.2795            23919.0901   \n",
       "2     XGBoost (one-vs-all)   -        1244.9438            18635.1964   \n",
       "3   CatBoost (multioutput)   -          73.7665            18600.3721   \n",
       "4              Top Outputs   1         129.5913              783.7018   \n",
       "5              Top Outputs   2         126.5449              810.4931   \n",
       "6              Top Outputs   5         113.7105             1003.3881   \n",
       "7              Top Outputs  10                -             1293.5372   \n",
       "8              Top Outputs  20                -             1889.2980   \n",
       "9          Random Sampling   1         104.4247              835.5595   \n",
       "10         Random Sampling   2         102.3624              880.4566   \n",
       "11         Random Sampling   5         116.3004             1087.0867   \n",
       "12         Random Sampling  10                -             1404.1419   \n",
       "13         Random Sampling  20                -             2038.0721   \n",
       "14       Random Projection   1          89.6285              419.5824   \n",
       "15       Random Projection   2          87.1467              464.4464   \n",
       "16       Random Projection   5          107.955              629.9292   \n",
       "17       Random Projection  10                -              895.8915   \n",
       "18       Random Projection  20                -             1417.1877   \n",
       "\n",
       "    Helena (100 classes)  SF-Crime (39 classes)  MoA (206 labels)  \\\n",
       "0              1195.6204              2682.5084          672.1119   \n",
       "1               355.5974              1146.1403          696.2490   \n",
       "2              1036.0248              4016.9352          376.3628   \n",
       "3               436.6722               659.7310         9398.4015   \n",
       "4               154.6307               174.2330           40.0038   \n",
       "5               151.6850               207.5298           45.1278   \n",
       "6               146.3063               270.8648           59.4057   \n",
       "7               138.1224               425.9713           69.9126   \n",
       "8               156.4791               705.4014          103.5214   \n",
       "9               180.6419               198.0625           61.5073   \n",
       "10              180.9462               219.6978           75.5351   \n",
       "11              185.7413               299.7088          104.9205   \n",
       "12              198.9258               422.4236          135.0571   \n",
       "13              213.2844               676.2324          189.2574   \n",
       "14              109.9810               136.5652           26.3955   \n",
       "15              113.4900               159.5843           29.3747   \n",
       "16              116.9194               233.3936           39.3908   \n",
       "17              128.2305               365.4783           55.9317   \n",
       "18              149.0080               612.8040           87.0725   \n",
       "\n",
       "   SCM20D (16 tasks) RF1 (8 tasks)  Delicious (983 labels)  \\\n",
       "0             295.73      207.8435               2902.3544   \n",
       "1           597.8406      413.7743              19553.5613   \n",
       "2          1432.8394      315.1049              15795.5608   \n",
       "3           798.6409      804.6936              20120.1416   \n",
       "4           458.9562      351.1528                213.8397   \n",
       "5           476.2507      364.9863                229.3393   \n",
       "6           499.3633      369.8712                274.7669   \n",
       "7           551.4483             -                375.7564   \n",
       "8                  -             -                575.1124   \n",
       "9           485.6434      347.9057                230.6440   \n",
       "10          491.4076       354.304                243.0265   \n",
       "11          528.8417      396.4812                314.3676   \n",
       "12          590.4221             -                432.8620   \n",
       "13                 -             -                664.0449   \n",
       "14          466.0371      331.2367                212.1800   \n",
       "15          479.0236      340.9701                235.0210   \n",
       "16           528.448      393.1228                295.0743   \n",
       "17          594.7274             -                436.5177   \n",
       "18                 -             -               1259.7520   \n",
       "\n",
       "    Mediamill (101 labels)  \n",
       "0                1230.7093  \n",
       "1                1777.6927  \n",
       "2                2074.3119  \n",
       "3               10164.0577  \n",
       "4                 251.4459  \n",
       "5                 276.4852  \n",
       "6                 313.6988  \n",
       "7                 386.5528  \n",
       "8                 529.5114  \n",
       "9                 263.4639  \n",
       "10                273.3999  \n",
       "11                319.1161  \n",
       "12                399.5259  \n",
       "13                559.8831  \n",
       "14                235.9618  \n",
       "15                243.3114  \n",
       "16                294.3931  \n",
       "17                369.2635  \n",
       "18                527.1915  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_total_table(dataset_order, strategy_order, 'train_time', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "07456bc8",
   "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>Dataset</th>\n",
       "      <th>K</th>\n",
       "      <th>Otto (9 classes)</th>\n",
       "      <th>Dionis (355 classes)</th>\n",
       "      <th>Helena (100 classes)</th>\n",
       "      <th>SF-Crime (39 classes)</th>\n",
       "      <th>MoA (206 labels)</th>\n",
       "      <th>SCM20D (16 tasks)</th>\n",
       "      <th>RF1 (8 tasks)</th>\n",
       "      <th>Delicious (983 labels)</th>\n",
       "      <th>Mediamill (101 labels)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tabnet</td>\n",
       "      <td>-</td>\n",
       "      <td>27.2</td>\n",
       "      <td>39.4</td>\n",
       "      <td>49.6</td>\n",
       "      <td>14.8</td>\n",
       "      <td>38.2</td>\n",
       "      <td>147.2</td>\n",
       "      <td>98.4</td>\n",
       "      <td>143.4</td>\n",
       "      <td>84.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SketchBoost Full</td>\n",
       "      <td>-</td>\n",
       "      <td>4424.8</td>\n",
       "      <td>18519.0</td>\n",
       "      <td>13492.2</td>\n",
       "      <td>3754.6</td>\n",
       "      <td>2240.6</td>\n",
       "      <td>19992.8</td>\n",
       "      <td>17001.4</td>\n",
       "      <td>6911.2</td>\n",
       "      <td>17927.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost (one-vs-all)</td>\n",
       "      <td>-</td>\n",
       "      <td>2142.0</td>\n",
       "      <td>2681.0</td>\n",
       "      <td>1563.8</td>\n",
       "      <td>1212.4</td>\n",
       "      <td>471.8</td>\n",
       "      <td>19998.0</td>\n",
       "      <td>19994.6</td>\n",
       "      <td>1611.6</td>\n",
       "      <td>1878.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CatBoost (multioutput)</td>\n",
       "      <td>-</td>\n",
       "      <td>5534.4</td>\n",
       "      <td>19858.8</td>\n",
       "      <td>11238.2</td>\n",
       "      <td>3993.6</td>\n",
       "      <td>4239.0</td>\n",
       "      <td>19998.0</td>\n",
       "      <td>19999.0</td>\n",
       "      <td>3956.0</td>\n",
       "      <td>8983.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>1</td>\n",
       "      <td>7654.8</td>\n",
       "      <td>17338.8</td>\n",
       "      <td>15042.0</td>\n",
       "      <td>3467.6</td>\n",
       "      <td>3813.6</td>\n",
       "      <td>19998.2</td>\n",
       "      <td>17787.6</td>\n",
       "      <td>7693.6</td>\n",
       "      <td>18623.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>2</td>\n",
       "      <td>7022.8</td>\n",
       "      <td>16747.4</td>\n",
       "      <td>14344.4</td>\n",
       "      <td>3660.0</td>\n",
       "      <td>3743.6</td>\n",
       "      <td>19983.6</td>\n",
       "      <td>17431.2</td>\n",
       "      <td>7439.0</td>\n",
       "      <td>19113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>5</td>\n",
       "      <td>4799.2</td>\n",
       "      <td>16847.0</td>\n",
       "      <td>13611.8</td>\n",
       "      <td>3457.6</td>\n",
       "      <td>3613.2</td>\n",
       "      <td>19992.4</td>\n",
       "      <td>16102.8</td>\n",
       "      <td>7068.4</td>\n",
       "      <td>18447.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>16779.4</td>\n",
       "      <td>11975.2</td>\n",
       "      <td>3699.6</td>\n",
       "      <td>2831.6</td>\n",
       "      <td>19996.6</td>\n",
       "      <td>-</td>\n",
       "      <td>7085.6</td>\n",
       "      <td>17845.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Top Outputs</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>17039.4</td>\n",
       "      <td>11985.6</td>\n",
       "      <td>3790.2</td>\n",
       "      <td>2606.2</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>7063.2</td>\n",
       "      <td>17288.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>1</td>\n",
       "      <td>5937.2</td>\n",
       "      <td>17983.8</td>\n",
       "      <td>16975.6</td>\n",
       "      <td>3915.8</td>\n",
       "      <td>6161.8</td>\n",
       "      <td>19994.6</td>\n",
       "      <td>17230.8</td>\n",
       "      <td>8134.2</td>\n",
       "      <td>19961.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>2</td>\n",
       "      <td>5424.4</td>\n",
       "      <td>17491.6</td>\n",
       "      <td>16823.4</td>\n",
       "      <td>3809.6</td>\n",
       "      <td>6764.6</td>\n",
       "      <td>19991.6</td>\n",
       "      <td>17515.0</td>\n",
       "      <td>7838.6</td>\n",
       "      <td>19971.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>5</td>\n",
       "      <td>4866.0</td>\n",
       "      <td>17799.4</td>\n",
       "      <td>16812.2</td>\n",
       "      <td>3798.4</td>\n",
       "      <td>6984.8</td>\n",
       "      <td>19991.8</td>\n",
       "      <td>17076.2</td>\n",
       "      <td>8007.2</td>\n",
       "      <td>19978.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>17860.0</td>\n",
       "      <td>17030.4</td>\n",
       "      <td>3740.8</td>\n",
       "      <td>6288.2</td>\n",
       "      <td>19992.8</td>\n",
       "      <td>-</td>\n",
       "      <td>7994.0</td>\n",
       "      <td>19976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Random Sampling</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>17990.4</td>\n",
       "      <td>16232.4</td>\n",
       "      <td>3726.2</td>\n",
       "      <td>5542.6</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>8015.0</td>\n",
       "      <td>19955.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>1</td>\n",
       "      <td>5201.4</td>\n",
       "      <td>11509.6</td>\n",
       "      <td>11549.0</td>\n",
       "      <td>3697.8</td>\n",
       "      <td>2169.6</td>\n",
       "      <td>19994.4</td>\n",
       "      <td>16902.6</td>\n",
       "      <td>7611.4</td>\n",
       "      <td>18074.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>2</td>\n",
       "      <td>4734.0</td>\n",
       "      <td>11486.0</td>\n",
       "      <td>11670.2</td>\n",
       "      <td>3679.2</td>\n",
       "      <td>2121.8</td>\n",
       "      <td>19993.6</td>\n",
       "      <td>16815.2</td>\n",
       "      <td>7787.6</td>\n",
       "      <td>18057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>5</td>\n",
       "      <td>4606.6</td>\n",
       "      <td>12311.0</td>\n",
       "      <td>11701.2</td>\n",
       "      <td>3620.4</td>\n",
       "      <td>2109.6</td>\n",
       "      <td>19993.4</td>\n",
       "      <td>16766.8</td>\n",
       "      <td>7613.0</td>\n",
       "      <td>17826.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>10</td>\n",
       "      <td>-</td>\n",
       "      <td>13060.2</td>\n",
       "      <td>12087.2</td>\n",
       "      <td>3664.6</td>\n",
       "      <td>2094.4</td>\n",
       "      <td>19987.6</td>\n",
       "      <td>-</td>\n",
       "      <td>7432.2</td>\n",
       "      <td>17654.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Random Projection</td>\n",
       "      <td>20</td>\n",
       "      <td>-</td>\n",
       "      <td>13672.6</td>\n",
       "      <td>12422.6</td>\n",
       "      <td>3611.4</td>\n",
       "      <td>2093.0</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>7541.6</td>\n",
       "      <td>17500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dataset   K Otto (9 classes)  Dionis (355 classes)  \\\n",
       "0                   Tabnet   -             27.2                  39.4   \n",
       "1         SketchBoost Full   -           4424.8               18519.0   \n",
       "2     XGBoost (one-vs-all)   -           2142.0                2681.0   \n",
       "3   CatBoost (multioutput)   -           5534.4               19858.8   \n",
       "4              Top Outputs   1           7654.8               17338.8   \n",
       "5              Top Outputs   2           7022.8               16747.4   \n",
       "6              Top Outputs   5           4799.2               16847.0   \n",
       "7              Top Outputs  10                -               16779.4   \n",
       "8              Top Outputs  20                -               17039.4   \n",
       "9          Random Sampling   1           5937.2               17983.8   \n",
       "10         Random Sampling   2           5424.4               17491.6   \n",
       "11         Random Sampling   5           4866.0               17799.4   \n",
       "12         Random Sampling  10                -               17860.0   \n",
       "13         Random Sampling  20                -               17990.4   \n",
       "14       Random Projection   1           5201.4               11509.6   \n",
       "15       Random Projection   2           4734.0               11486.0   \n",
       "16       Random Projection   5           4606.6               12311.0   \n",
       "17       Random Projection  10                -               13060.2   \n",
       "18       Random Projection  20                -               13672.6   \n",
       "\n",
       "    Helena (100 classes)  SF-Crime (39 classes)  MoA (206 labels)  \\\n",
       "0                   49.6                   14.8              38.2   \n",
       "1                13492.2                 3754.6            2240.6   \n",
       "2                 1563.8                 1212.4             471.8   \n",
       "3                11238.2                 3993.6            4239.0   \n",
       "4                15042.0                 3467.6            3813.6   \n",
       "5                14344.4                 3660.0            3743.6   \n",
       "6                13611.8                 3457.6            3613.2   \n",
       "7                11975.2                 3699.6            2831.6   \n",
       "8                11985.6                 3790.2            2606.2   \n",
       "9                16975.6                 3915.8            6161.8   \n",
       "10               16823.4                 3809.6            6764.6   \n",
       "11               16812.2                 3798.4            6984.8   \n",
       "12               17030.4                 3740.8            6288.2   \n",
       "13               16232.4                 3726.2            5542.6   \n",
       "14               11549.0                 3697.8            2169.6   \n",
       "15               11670.2                 3679.2            2121.8   \n",
       "16               11701.2                 3620.4            2109.6   \n",
       "17               12087.2                 3664.6            2094.4   \n",
       "18               12422.6                 3611.4            2093.0   \n",
       "\n",
       "   SCM20D (16 tasks) RF1 (8 tasks)  Delicious (983 labels)  \\\n",
       "0              147.2          98.4                   143.4   \n",
       "1            19992.8       17001.4                  6911.2   \n",
       "2            19998.0       19994.6                  1611.6   \n",
       "3            19998.0       19999.0                  3956.0   \n",
       "4            19998.2       17787.6                  7693.6   \n",
       "5            19983.6       17431.2                  7439.0   \n",
       "6            19992.4       16102.8                  7068.4   \n",
       "7            19996.6             -                  7085.6   \n",
       "8                  -             -                  7063.2   \n",
       "9            19994.6       17230.8                  8134.2   \n",
       "10           19991.6       17515.0                  7838.6   \n",
       "11           19991.8       17076.2                  8007.2   \n",
       "12           19992.8             -                  7994.0   \n",
       "13                 -             -                  8015.0   \n",
       "14           19994.4       16902.6                  7611.4   \n",
       "15           19993.6       16815.2                  7787.6   \n",
       "16           19993.4       16766.8                  7613.0   \n",
       "17           19987.6             -                  7432.2   \n",
       "18                 -             -                  7541.6   \n",
       "\n",
       "    Mediamill (101 labels)  \n",
       "0                     84.6  \n",
       "1                  17927.4  \n",
       "2                   1878.6  \n",
       "3                   8983.2  \n",
       "4                  18623.8  \n",
       "5                  19113.0  \n",
       "6                  18447.0  \n",
       "7                  17845.4  \n",
       "8                  17288.2  \n",
       "9                  19961.6  \n",
       "10                 19971.6  \n",
       "11                 19978.6  \n",
       "12                 19976.0  \n",
       "13                 19955.2  \n",
       "14                 18074.4  \n",
       "15                 18057.0  \n",
       "16                 17826.6  \n",
       "17                 17654.6  \n",
       "18                 17500.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_total_table(dataset_order, strategy_order, 'best_iter', \n",
    "                         st_d, nm_d, r=4, baselines=baselines, output_dir=OUTPUT_DIR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6f9e0e30",
   "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>Dataset</th>\n",
       "      <th>Framework</th>\n",
       "      <th>min_data_in_leaf</th>\n",
       "      <th>subsample</th>\n",
       "      <th>max_depth</th>\n",
       "      <th>lambda_l2</th>\n",
       "      <th>Completed Trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Otto</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.889871</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.830034</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Otto</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.580116</td>\n",
       "      <td>12.0</td>\n",
       "      <td>23.569951</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dionis</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dionis</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Helena</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.546926</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.773937</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Helena</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.337340</td>\n",
       "      <td>0.504766</td>\n",
       "      <td>6.0</td>\n",
       "      <td>23.325277</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SF-Crime</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.941623</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.646515</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SF-Crime</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1.256824</td>\n",
       "      <td>0.918289</td>\n",
       "      <td>12.0</td>\n",
       "      <td>8.374243</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MoA</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>0.880233</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.031856</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MoA</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.003430</td>\n",
       "      <td>0.935334</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.370817</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SCM20D</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.992114</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.572463</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SCM20D</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>9.937704</td>\n",
       "      <td>0.805208</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.448211</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RF1</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.852105</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.960692</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF1</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.004313</td>\n",
       "      <td>0.687735</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.226070</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Delicious</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>0.623613</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.057198</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Delicious</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.062360</td>\n",
       "      <td>0.721285</td>\n",
       "      <td>11.0</td>\n",
       "      <td>33.228067</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Mediamill</td>\n",
       "      <td>CatBoost</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.761407</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.162539</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mediamill</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>1.904695</td>\n",
       "      <td>0.688189</td>\n",
       "      <td>12.0</td>\n",
       "      <td>39.308135</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Dataset Framework  min_data_in_leaf  subsample  max_depth  lambda_l2  \\\n",
       "0        Otto  CatBoost         47.000000   0.889871       10.0   3.830034   \n",
       "1        Otto   XGBoost          0.000010   0.580116       12.0  23.569951   \n",
       "2      Dionis  CatBoost          1.000000   1.000000        6.0   1.000000   \n",
       "3      Dionis   XGBoost          0.000010   1.000000        6.0   1.000000   \n",
       "4      Helena  CatBoost          2.000000   0.546926        6.0  12.773937   \n",
       "5      Helena   XGBoost          0.337340   0.504766        6.0  23.325277   \n",
       "6    SF-Crime  CatBoost          2.000000   0.941623       11.0   2.646515   \n",
       "7    SF-Crime   XGBoost          1.256824   0.918289       12.0   8.374243   \n",
       "8         MoA  CatBoost         30.000000   0.880233        4.0   1.031856   \n",
       "9         MoA   XGBoost          0.003430   0.935334        3.0   0.370817   \n",
       "10     SCM20D  CatBoost          5.000000   0.992114       12.0   6.572463   \n",
       "11     SCM20D   XGBoost          9.937704   0.805208        6.0   1.448211   \n",
       "12        RF1  CatBoost          1.000000   0.852105       10.0   9.960692   \n",
       "13        RF1   XGBoost          0.004313   0.687735        5.0  12.226070   \n",
       "14  Delicious  CatBoost         76.000000   0.623613       12.0  13.057198   \n",
       "15  Delicious   XGBoost          0.062360   0.721285       11.0  33.228067   \n",
       "16  Mediamill  CatBoost         10.000000   0.761407        8.0  11.162539   \n",
       "17  Mediamill   XGBoost          1.904695   0.688189       12.0  39.308135   \n",
       "\n",
       "    Completed Trials  \n",
       "0                 31  \n",
       "1                 31  \n",
       "2                  6  \n",
       "3                  3  \n",
       "4                 31  \n",
       "5                 31  \n",
       "6                 31  \n",
       "7                 24  \n",
       "8                 10  \n",
       "9                 31  \n",
       "10                31  \n",
       "11                31  \n",
       "12                31  \n",
       "13                31  \n",
       "14                 5  \n",
       "15                10  \n",
       "16                 8  \n",
       "17                31  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperparams = joblib.load(os.path.join('runs', HYPERPARAMS))\n",
    "hyperparams = {x: hyperparams[x] for x in hyperparams if x[0] in dataset_order and x[1] != 'tabnet'}\n",
    "\n",
    "hp = DataFrame({x: hyperparams[x]['params'] for x in hyperparams}).T\n",
    "hp['Completed Trials'] = Series({x: hyperparams[x]['num_trials'] for x in hyperparams})\n",
    "hp.index.names = ('Dataset', 'Framework')\n",
    "hp = hp.reset_index()\n",
    "hp['Dataset'] = hp['Dataset'].map(nm_d).map(lambda x: x.split(' ')[0])\n",
    "hp['Framework'] = hp['Framework'].map(st_d).map(lambda x: x.split(' ')[0])\n",
    "hp.to_csv(os.path.join(OUTPUT_DIR, 'hyper.csv'), index=False)\n",
    "hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12392716",
   "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>Dataset</th>\n",
       "      <th>lr</th>\n",
       "      <th>Completed Trials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Otto</td>\n",
       "      <td>0.039473</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dionis</td>\n",
       "      <td>0.010951</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Helena</td>\n",
       "      <td>0.008036</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SF-Crime</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MoA</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SCM20D</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RF1</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delicious</td>\n",
       "      <td>0.023071</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mediamill</td>\n",
       "      <td>0.031523</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dataset        lr  Completed Trials\n",
       "0       Otto  0.039473                11\n",
       "1     Dionis  0.010951                 7\n",
       "2     Helena  0.008036                11\n",
       "3   SF-Crime  0.020000                 3\n",
       "4        MoA  0.020000                10\n",
       "5     SCM20D  0.020000                11\n",
       "6        RF1  0.020000                11\n",
       "7  Delicious  0.023071                11\n",
       "8  Mediamill  0.031523                11"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hyperparams = joblib.load(os.path.join('runs', HYPERPARAMS))\n",
    "hyperparams = {x: hyperparams[x] for x in hyperparams if x[0] in dataset_order and x[1] == 'tabnet'}\n",
    "\n",
    "hp = DataFrame({x: hyperparams[x]['params'] for x in hyperparams}).T\n",
    "hp['Completed Trials'] = Series({x: hyperparams[x]['num_trials'] for x in hyperparams})\n",
    "hp.index.names = ('Dataset', 'Framework')\n",
    "hp = hp.reset_index()\n",
    "hp['Dataset'] = hp['Dataset'].map(nm_d).map(lambda x: x.split(' ')[0])\n",
    "hp['Framework'] = hp['Framework'].map(st_d).map(lambda x: x.split(' ')[0])\n",
    "hp = hp.drop('Framework', axis=1)\n",
    "hp.to_csv(os.path.join(OUTPUT_DIR, 'hyper_tab.csv'), index=False)\n",
    "hp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "41305f43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output/OUTPUT_MAIN/\n",
      "output/OUTPUT_MAIN/detailed_test_score_w_std_6d.csv\n",
      "output/OUTPUT_MAIN/summary_train_time/\n",
      "output/OUTPUT_MAIN/summary_train_time/sf-crime.png\n",
      "output/OUTPUT_MAIN/summary_train_time/moa.png\n",
      "output/OUTPUT_MAIN/summary_train_time/mediamill.png\n",
      "output/OUTPUT_MAIN/summary_train_time/helena.png\n",
      "output/OUTPUT_MAIN/summary_train_time/otto.png\n",
      "output/OUTPUT_MAIN/summary_train_time/rf1.png\n",
      "output/OUTPUT_MAIN/summary_train_time/dionis.png\n",
      "output/OUTPUT_MAIN/summary_train_time/scm20d.png\n",
      "output/OUTPUT_MAIN/summary_train_time/delicious.png\n",
      "output/OUTPUT_MAIN/summary_std_test_score.csv\n",
      "output/OUTPUT_MAIN/detailed_std_test_score.csv\n",
      "output/OUTPUT_MAIN/detailed_test_score_w_std.csv\n",
      "output/OUTPUT_MAIN/summary_train_time.csv\n",
      "output/OUTPUT_MAIN/summary_test_score.csv\n",
      "output/OUTPUT_MAIN/detailed_train_time.csv\n",
      "output/OUTPUT_MAIN/detailed_std_test_acc.csv\n",
      "output/OUTPUT_MAIN/detailed_test_acc_w_std.csv\n",
      "output/OUTPUT_MAIN/summary_score_w_std.csv\n",
      "output/OUTPUT_MAIN/summary_best_iter.csv\n",
      "output/OUTPUT_MAIN/detailed_best_iter.csv\n",
      "output/OUTPUT_MAIN/detailed_test_acc_w_std_6d.csv\n",
      "output/OUTPUT_MAIN/summary_test_acc.csv\n",
      "output/OUTPUT_MAIN/summary_std_test_acc.csv\n",
      "output/OUTPUT_MAIN/hyper.csv\n",
      "output/OUTPUT_MAIN/detailed_test_score/\n",
      "output/OUTPUT_MAIN/detailed_test_score/rf1.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/mediamill.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/dionis.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/delicious.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/otto.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/scm20d.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/sf-crime.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/helena.png\n",
      "output/OUTPUT_MAIN/detailed_test_score/moa.png\n",
      "output/OUTPUT_MAIN/summary_acc_w_std.csv\n",
      "output/OUTPUT_MAIN/detailed_test_score.csv\n",
      "output/OUTPUT_MAIN/detailed_test_acc.csv\n",
      "output/OUTPUT_MAIN/hyper_tab.csv\n",
      "output/OUTPUT_MAIN/summary_test_pred_time.csv\n",
      "tar: output/OUTPUT_MAIN/experiments_main.tar: file is the archive; not dumped\n",
      "Total bytes written: 7567360 (7.3MiB, 62MiB/s)\n"
     ]
    }
   ],
   "source": [
    "!rm {os.path.join(OUTPUT_DIR, 'experiments_main.tar')}\n",
    "!tar --totals -cvf {os.path.join(OUTPUT_DIR, 'experiments_main.tar')} {OUTPUT_DIR}"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "sb_env",
   "language": "python",
   "name": "sb_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}