evaluation/visualization_single_evaluation-LOD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplementary Material:\n",
    "## SchemaTree recommender evaluation results for the LOD-a-Lot dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading and preparing the evaluation results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# use LaTeX fonts in the plot\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('font', family='serif')\n",
    "\n",
    "# load data sets\n",
    "counts  = pd.read_csv(\"results/LOD_counts.csv\",encoding = \"ISO-8859-1\", skipinitialspace=True, sep=\" \", header=None,names=[\"count\",\"numNonTypes\",\"trash\"])\n",
    "prefix = \"results/LOD_a_lot_v1-1in10000000-test-r1-\"\n",
    "df_recommender   = pd.read_csv(prefix+\"standard-takeMoreButCommon-numNonTypes-stats.csv\",encoding = \"ISO-8859-1\", skipinitialspace=True)\n",
    "df_typed         = pd.read_csv(prefix+\"typed-takeMoreButCommon-numNonTypes-stats.csv\",encoding = \"ISO-8859-1\", skipinitialspace=True)\n",
    "df_typed_backoff = pd.read_csv(prefix+\"typed-tooFewRecs-takeMoreButCommon-numNonTypes-stats.csv\",encoding = \"ISO-8859-1\", skipinitialspace=True)\n",
    "\n",
    "# extend information\n",
    "df_recommender['Recommender']='Standard'\n",
    "df_typed['Recommender']='Typed'\n",
    "df_typed_backoff['Recommender']='Typed \\& Backoff'\n",
    "\n",
    "# combine results\n",
    "df_combined = pd.concat([\n",
    "    df_recommender,\n",
    "    df_typed,\n",
    "    df_typed_backoff,\n",
    "])\n",
    "\n",
    "# compute F1 & reciprocal rank\n",
    "df_combined[\"f1\"]=2*df_combined[\"precisionAtL\"]*df_combined[\"recall\"]/(df_combined[\"precisionAtL\"]+df_combined[\"recall\"])\n",
    "df_combined[\"RR\"]=1/df_combined[\"mean\"]\n",
    "\n",
    "# filter out the aggregated columns\n",
    "group = df_combined.columns[0]\n",
    "aggregated = df_combined[df_combined[group]==-1]\n",
    "df_combined = df_combined[df_combined[group] >=0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary results of the different recommenders over all evaluated recommendation requests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Recommender</th>\n",
       "      <th>subjects</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>stddev</th>\n",
       "      <th>top1</th>\n",
       "      <th>top5</th>\n",
       "      <th>top10</th>\n",
       "      <th>topL</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>precisionAtL</th>\n",
       "      <th>f1</th>\n",
       "      <th>RR</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Standard</td>\n",
       "      <td>123731</td>\n",
       "      <td>9.4433</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.0536</td>\n",
       "      <td>75.8735</td>\n",
       "      <td>85.6988</td>\n",
       "      <td>90.224</td>\n",
       "      <td>81.3975</td>\n",
       "      <td>97.9065</td>\n",
       "      <td>18.0645</td>\n",
       "      <td>59.5663</td>\n",
       "      <td>74.069020</td>\n",
       "      <td>0.105895</td>\n",
       "      <td>2670.6998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Typed</td>\n",
       "      <td>123731</td>\n",
       "      <td>2.7836</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.9431</td>\n",
       "      <td>84.7265</td>\n",
       "      <td>93.9045</td>\n",
       "      <td>97.417</td>\n",
       "      <td>89.7293</td>\n",
       "      <td>99.4509</td>\n",
       "      <td>23.2183</td>\n",
       "      <td>68.9024</td>\n",
       "      <td>81.405065</td>\n",
       "      <td>0.359247</td>\n",
       "      <td>410.9850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>123731</td>\n",
       "      <td>2.7838</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.9421</td>\n",
       "      <td>84.7249</td>\n",
       "      <td>93.9021</td>\n",
       "      <td>97.417</td>\n",
       "      <td>89.7269</td>\n",
       "      <td>99.4509</td>\n",
       "      <td>23.2183</td>\n",
       "      <td>68.9007</td>\n",
       "      <td>81.403879</td>\n",
       "      <td>0.359221</td>\n",
       "      <td>410.9490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Recommender  subjects    mean  median   stddev     top1     top5  \\\n",
       "0          Standard    123731  9.4433     1.0  46.0536  75.8735  85.6988   \n",
       "0             Typed    123731  2.7836     1.0  16.9431  84.7265  93.9045   \n",
       "0  Typed \\& Backoff    123731  2.7838     1.0  16.9421  84.7249  93.9021   \n",
       "\n",
       "    top10     topL   recall  precision  precisionAtL         f1        RR  \\\n",
       "0  90.224  81.3975  97.9065    18.0645       59.5663  74.069020  0.105895   \n",
       "0  97.417  89.7293  99.4509    23.2183       68.9024  81.405065  0.359247   \n",
       "0  97.417  89.7269  99.4509    23.2183       68.9007  81.403879  0.359221   \n",
       "\n",
       "    duration  \n",
       "0  2670.6998  \n",
       "0   410.9850  \n",
       "0   410.9490  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregated.iloc[:, [14,1,3,5,6,7,8,9,10,11,12,13,15,16,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create detailed plot of interesting metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the matplotlib figure\n",
    "f, axes = plt.subplots(2, 2, figsize=(11, 5), sharex=True)\n",
    "\n",
    "ax = axes.flat[0]\n",
    "ax.set_xlim(2,55)\n",
    "ax.set_ylim(0,3000)\n",
    "cline = sns.lineplot(\n",
    "    x=counts[\"numNonTypes\"], \n",
    "    y=\"count\", \n",
    "    data=counts,\n",
    "    color=\"black\",\n",
    "    ax=ax,\n",
    ")\n",
    "   \n",
    "for axx, column in zip(axes.flat[1:], df_combined[[\"top5\",\"mean\",\"f1\"]]):\n",
    "    sns.lineplot(\n",
    "        x=group, \n",
    "        y=column, \n",
    "        hue=\"Recommender\",\n",
    "        data=df_combined, \n",
    "        style=\"Recommender\",\n",
    "        ax=axx,\n",
    "    )\n",
    "    axx.set_xlim(2,55)\n",
    "    axx.get_legend().remove()\n",
    "\n",
    "f.tight_layout()\n",
    "\n",
    "handles, labels = axx.get_legend_handles_labels()\n",
    "handles = [ handles[0], cline.get_lines()[0], handles[0]] + handles\n",
    "labels  = ['Input Set', 'Frequency',''] + labels\n",
    "f.legend(handles, labels, loc='center right', bbox_to_anchor=(0.485, 0.78), fontsize='small')\n",
    "\n",
    "labels = [\n",
    "    r'(a) Distribution of non-type input property set sizes.',\n",
    "    r'(b) $\\mathbf{Top5}$ score per recommender system. Higher is better.',\n",
    "    r'(c) $\\mathbf{ØRank}$ of first correct recommendation. Lower is better.',\n",
    "    r'(d) Modified $\\mathbf{F1}$ score per recommender system. Higher is better.',\n",
    "]\n",
    "for ax, label in zip(axes.flat, labels):\n",
    "    ax.set_title(label, fontsize=11)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.2)\n",
    "\n",
    "# f.savefig('combined.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create and save additional detailed plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_combined' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-4e5f3abd9c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf_combined\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_combined\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mdf_combined\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Recommender'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     g = sns.lineplot(\n\u001b[1;32m      5\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df_combined' is not defined"
     ]
    }
   ],
   "source": [
    "for column in df_combined[df_combined.columns[~df_combined.columns.isin(['Recommender'])]].iloc[:,2:]:\n",
    "\n",
    "    plt.figure(num=None, figsize=(8, 3))\n",
    "    g = sns.lineplot(\n",
    "        x=group, \n",
    "        y=column, \n",
    "        hue=\"Recommender\",\n",
    "        data=df_combined, \n",
    "        style =\"Recommender\",\n",
    "    )\n",
    "    ax = g.axes\n",
    "    ax.set_xlim(2,55)\n",
    "    ax.legend(fancybox=True, framealpha=0.5, loc='upper right')\n",
    "    \n",
    "    plt.subplots_adjust(bottom=0.2)\n",
    "#     plt.savefig(column+'.pdf', bbox_inches='tight')\n",
    "    \n",
    "plt.figure(num=None, figsize=(8, 3))\n",
    "g = sns.lineplot(\n",
    "    x=counts[\"numNonTypes\"], \n",
    "    y=counts[\"count\"], \n",
    "    data=counts,\n",
    "    color=\"black\",\n",
    ")\n",
    "ax = g.axes\n",
    "ax.set_xlim(2,55)\n",
    "# plt.savefig('LOD_counts.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Detailed evaluation results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>numNonTypes</th>\n",
       "      <th>subjects</th>\n",
       "      <th>duration</th>\n",
       "      <th>mean</th>\n",
       "      <th>meanOfHits</th>\n",
       "      <th>median</th>\n",
       "      <th>stddev</th>\n",
       "      <th>top1</th>\n",
       "      <th>top5</th>\n",
       "      <th>top10</th>\n",
       "      <th>topL</th>\n",
       "      <th>recall</th>\n",
       "      <th>precision</th>\n",
       "      <th>precisionAtL</th>\n",
       "      <th>Recommender</th>\n",
       "      <th>f1</th>\n",
       "      <th>RR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2414</td>\n",
       "      <td>10511.0329</td>\n",
       "      <td>86.7498</td>\n",
       "      <td>40.9176</td>\n",
       "      <td>11.0</td>\n",
       "      <td>159.6074</td>\n",
       "      <td>19.8012</td>\n",
       "      <td>31.1516</td>\n",
       "      <td>31.1516</td>\n",
       "      <td>19.8012</td>\n",
       "      <td>90.0166</td>\n",
       "      <td>5.2850</td>\n",
       "      <td>19.8012</td>\n",
       "      <td>Standard</td>\n",
       "      <td>32.461708</td>\n",
       "      <td>0.011527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4298</td>\n",
       "      <td>8789.6953</td>\n",
       "      <td>49.0021</td>\n",
       "      <td>31.6770</td>\n",
       "      <td>21.0</td>\n",
       "      <td>110.5881</td>\n",
       "      <td>40.4374</td>\n",
       "      <td>44.3229</td>\n",
       "      <td>45.1605</td>\n",
       "      <td>40.5305</td>\n",
       "      <td>94.1019</td>\n",
       "      <td>9.2746</td>\n",
       "      <td>40.4723</td>\n",
       "      <td>Standard</td>\n",
       "      <td>56.601047</td>\n",
       "      <td>0.020407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2496</td>\n",
       "      <td>6587.6079</td>\n",
       "      <td>38.3385</td>\n",
       "      <td>25.4090</td>\n",
       "      <td>3.0</td>\n",
       "      <td>91.8028</td>\n",
       "      <td>42.3478</td>\n",
       "      <td>57.6122</td>\n",
       "      <td>66.3462</td>\n",
       "      <td>46.5946</td>\n",
       "      <td>95.1522</td>\n",
       "      <td>12.2951</td>\n",
       "      <td>42.1207</td>\n",
       "      <td>Standard</td>\n",
       "      <td>58.392840</td>\n",
       "      <td>0.026083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>7080</td>\n",
       "      <td>4020.3970</td>\n",
       "      <td>16.2027</td>\n",
       "      <td>12.7617</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.0671</td>\n",
       "      <td>71.2288</td>\n",
       "      <td>72.5989</td>\n",
       "      <td>73.7994</td>\n",
       "      <td>71.4689</td>\n",
       "      <td>98.5064</td>\n",
       "      <td>45.6096</td>\n",
       "      <td>69.7434</td>\n",
       "      <td>Standard</td>\n",
       "      <td>81.666323</td>\n",
       "      <td>0.061718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>7040</td>\n",
       "      <td>3387.6348</td>\n",
       "      <td>10.2548</td>\n",
       "      <td>9.3488</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42.6847</td>\n",
       "      <td>71.8608</td>\n",
       "      <td>75.5966</td>\n",
       "      <td>91.8892</td>\n",
       "      <td>72.4148</td>\n",
       "      <td>98.7637</td>\n",
       "      <td>34.9995</td>\n",
       "      <td>67.6870</td>\n",
       "      <td>Standard</td>\n",
       "      <td>80.324307</td>\n",
       "      <td>0.097515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3348</td>\n",
       "      <td>4002.7306</td>\n",
       "      <td>13.9388</td>\n",
       "      <td>12.1903</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.7106</td>\n",
       "      <td>69.6237</td>\n",
       "      <td>78.1661</td>\n",
       "      <td>83.7216</td>\n",
       "      <td>74.3728</td>\n",
       "      <td>98.0785</td>\n",
       "      <td>9.6694</td>\n",
       "      <td>61.8130</td>\n",
       "      <td>Standard</td>\n",
       "      <td>75.833003</td>\n",
       "      <td>0.071742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>7133</td>\n",
       "      <td>3049.6917</td>\n",
       "      <td>10.0077</td>\n",
       "      <td>9.3198</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.9053</td>\n",
       "      <td>72.2137</td>\n",
       "      <td>86.0928</td>\n",
       "      <td>90.4108</td>\n",
       "      <td>76.8821</td>\n",
       "      <td>98.2235</td>\n",
       "      <td>10.5990</td>\n",
       "      <td>52.6248</td>\n",
       "      <td>Standard</td>\n",
       "      <td>68.532321</td>\n",
       "      <td>0.099923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>22792</td>\n",
       "      <td>2035.3173</td>\n",
       "      <td>3.6693</td>\n",
       "      <td>3.4732</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.5004</td>\n",
       "      <td>74.4823</td>\n",
       "      <td>89.0137</td>\n",
       "      <td>94.6648</td>\n",
       "      <td>80.6160</td>\n",
       "      <td>99.4034</td>\n",
       "      <td>4.2599</td>\n",
       "      <td>50.2469</td>\n",
       "      <td>Standard</td>\n",
       "      <td>66.751790</td>\n",
       "      <td>0.272532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>24894</td>\n",
       "      <td>1749.6533</td>\n",
       "      <td>2.4899</td>\n",
       "      <td>2.4499</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.3529</td>\n",
       "      <td>78.5169</td>\n",
       "      <td>91.9900</td>\n",
       "      <td>97.1278</td>\n",
       "      <td>86.9045</td>\n",
       "      <td>99.5191</td>\n",
       "      <td>7.0872</td>\n",
       "      <td>56.3455</td>\n",
       "      <td>Standard</td>\n",
       "      <td>71.952880</td>\n",
       "      <td>0.401623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>15500</td>\n",
       "      <td>1707.3175</td>\n",
       "      <td>2.3366</td>\n",
       "      <td>2.2082</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.5303</td>\n",
       "      <td>83.8129</td>\n",
       "      <td>93.8258</td>\n",
       "      <td>97.7742</td>\n",
       "      <td>91.2065</td>\n",
       "      <td>99.5717</td>\n",
       "      <td>12.2068</td>\n",
       "      <td>61.3570</td>\n",
       "      <td>Standard</td>\n",
       "      <td>75.927051</td>\n",
       "      <td>0.427972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>3795</td>\n",
       "      <td>1785.0029</td>\n",
       "      <td>5.0759</td>\n",
       "      <td>4.1613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.1983</td>\n",
       "      <td>77.7339</td>\n",
       "      <td>91.7260</td>\n",
       "      <td>94.4664</td>\n",
       "      <td>90.0659</td>\n",
       "      <td>98.6420</td>\n",
       "      <td>17.1030</td>\n",
       "      <td>57.6516</td>\n",
       "      <td>Standard</td>\n",
       "      <td>72.771619</td>\n",
       "      <td>0.197009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>1284</td>\n",
       "      <td>3312.0167</td>\n",
       "      <td>7.9299</td>\n",
       "      <td>7.1622</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0821</td>\n",
       "      <td>75.0779</td>\n",
       "      <td>86.5265</td>\n",
       "      <td>90.1869</td>\n",
       "      <td>87.0717</td>\n",
       "      <td>97.3197</td>\n",
       "      <td>15.3724</td>\n",
       "      <td>61.7408</td>\n",
       "      <td>Standard</td>\n",
       "      <td>75.551078</td>\n",
       "      <td>0.126105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>1326</td>\n",
       "      <td>3716.4357</td>\n",
       "      <td>8.1591</td>\n",
       "      <td>6.2975</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.7385</td>\n",
       "      <td>79.2609</td>\n",
       "      <td>86.2745</td>\n",
       "      <td>91.1765</td>\n",
       "      <td>87.4057</td>\n",
       "      <td>96.9995</td>\n",
       "      <td>13.1699</td>\n",
       "      <td>65.9106</td>\n",
       "      <td>Standard</td>\n",
       "      <td>78.488630</td>\n",
       "      <td>0.122563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>1190</td>\n",
       "      <td>2775.2416</td>\n",
       "      <td>8.4655</td>\n",
       "      <td>5.1387</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.3792</td>\n",
       "      <td>80.5882</td>\n",
       "      <td>88.3193</td>\n",
       "      <td>91.8487</td>\n",
       "      <td>89.1597</td>\n",
       "      <td>97.8702</td>\n",
       "      <td>16.6267</td>\n",
       "      <td>68.0336</td>\n",
       "      <td>Standard</td>\n",
       "      <td>80.268952</td>\n",
       "      <td>0.118127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>1140</td>\n",
       "      <td>1992.9692</td>\n",
       "      <td>8.4123</td>\n",
       "      <td>6.2467</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.2435</td>\n",
       "      <td>81.7544</td>\n",
       "      <td>87.8947</td>\n",
       "      <td>91.4912</td>\n",
       "      <td>88.3333</td>\n",
       "      <td>97.0905</td>\n",
       "      <td>18.5636</td>\n",
       "      <td>65.6973</td>\n",
       "      <td>Standard</td>\n",
       "      <td>78.366852</td>\n",
       "      <td>0.118874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>816</td>\n",
       "      <td>2350.5206</td>\n",
       "      <td>7.6422</td>\n",
       "      <td>5.8253</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37.4371</td>\n",
       "      <td>81.7402</td>\n",
       "      <td>89.5833</td>\n",
       "      <td>92.0343</td>\n",
       "      <td>89.5833</td>\n",
       "      <td>97.2916</td>\n",
       "      <td>21.0188</td>\n",
       "      <td>65.2763</td>\n",
       "      <td>Standard</td>\n",
       "      <td>78.131484</td>\n",
       "      <td>0.130852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>357</td>\n",
       "      <td>1604.8418</td>\n",
       "      <td>16.0112</td>\n",
       "      <td>4.9169</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.9835</td>\n",
       "      <td>84.0336</td>\n",
       "      <td>91.5966</td>\n",
       "      <td>92.7171</td>\n",
       "      <td>90.4762</td>\n",
       "      <td>95.3276</td>\n",
       "      <td>33.4356</td>\n",
       "      <td>73.4735</td>\n",
       "      <td>Standard</td>\n",
       "      <td>82.985862</td>\n",
       "      <td>0.062456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>522</td>\n",
       "      <td>1341.6889</td>\n",
       "      <td>9.8295</td>\n",
       "      <td>5.0890</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.9784</td>\n",
       "      <td>85.8238</td>\n",
       "      <td>90.2299</td>\n",
       "      <td>91.9540</td>\n",
       "      <td>89.4636</td>\n",
       "      <td>97.0844</td>\n",
       "      <td>29.1763</td>\n",
       "      <td>67.5052</td>\n",
       "      <td>Standard</td>\n",
       "      <td>79.636889</td>\n",
       "      <td>0.101735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>247</td>\n",
       "      <td>1975.0277</td>\n",
       "      <td>12.3684</td>\n",
       "      <td>6.3730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.3953</td>\n",
       "      <td>81.7814</td>\n",
       "      <td>91.9028</td>\n",
       "      <td>92.7126</td>\n",
       "      <td>89.4737</td>\n",
       "      <td>91.4905</td>\n",
       "      <td>34.2641</td>\n",
       "      <td>66.6235</td>\n",
       "      <td>Standard</td>\n",
       "      <td>77.101551</td>\n",
       "      <td>0.080851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>260</td>\n",
       "      <td>1028.0062</td>\n",
       "      <td>11.9577</td>\n",
       "      <td>8.1744</td>\n",
       "      <td>1.0</td>\n",
       "      <td>63.0436</td>\n",
       "      <td>89.6154</td>\n",
       "      <td>93.8462</td>\n",
       "      <td>94.6154</td>\n",
       "      <td>93.8462</td>\n",
       "      <td>96.6868</td>\n",
       "      <td>41.6592</td>\n",
       "      <td>77.1421</td>\n",
       "      <td>Standard</td>\n",
       "      <td>85.815682</td>\n",
       "      <td>0.083628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>210</td>\n",
       "      <td>3573.7598</td>\n",
       "      <td>8.9476</td>\n",
       "      <td>4.2260</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.7402</td>\n",
       "      <td>84.7619</td>\n",
       "      <td>93.3333</td>\n",
       "      <td>94.7619</td>\n",
       "      <td>92.3810</td>\n",
       "      <td>90.9990</td>\n",
       "      <td>20.9892</td>\n",
       "      <td>70.0596</td>\n",
       "      <td>Standard</td>\n",
       "      <td>79.168123</td>\n",
       "      <td>0.111762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>396</td>\n",
       "      <td>1959.8988</td>\n",
       "      <td>12.0202</td>\n",
       "      <td>7.0408</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.9793</td>\n",
       "      <td>82.0707</td>\n",
       "      <td>88.1313</td>\n",
       "      <td>91.1616</td>\n",
       "      <td>88.8889</td>\n",
       "      <td>94.6751</td>\n",
       "      <td>30.7065</td>\n",
       "      <td>67.8296</td>\n",
       "      <td>Standard</td>\n",
       "      <td>79.034935</td>\n",
       "      <td>0.083193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>207</td>\n",
       "      <td>6668.1755</td>\n",
       "      <td>12.6908</td>\n",
       "      <td>7.9366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.5647</td>\n",
       "      <td>80.1932</td>\n",
       "      <td>90.8213</td>\n",
       "      <td>92.7536</td>\n",
       "      <td>90.8213</td>\n",
       "      <td>81.6593</td>\n",
       "      <td>10.9978</td>\n",
       "      <td>54.2951</td>\n",
       "      <td>Standard</td>\n",
       "      <td>65.223338</td>\n",
       "      <td>0.078797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>144</td>\n",
       "      <td>2089.0896</td>\n",
       "      <td>11.9583</td>\n",
       "      <td>5.0845</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.9910</td>\n",
       "      <td>89.5833</td>\n",
       "      <td>93.7500</td>\n",
       "      <td>93.7500</td>\n",
       "      <td>93.0556</td>\n",
       "      <td>91.6581</td>\n",
       "      <td>37.5534</td>\n",
       "      <td>77.5147</td>\n",
       "      <td>Standard</td>\n",
       "      <td>83.995183</td>\n",
       "      <td>0.083624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>200</td>\n",
       "      <td>2737.7088</td>\n",
       "      <td>9.5050</td>\n",
       "      <td>7.0402</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.6926</td>\n",
       "      <td>81.5000</td>\n",
       "      <td>87.5000</td>\n",
       "      <td>91.0000</td>\n",
       "      <td>89.5000</td>\n",
       "      <td>89.9967</td>\n",
       "      <td>20.4056</td>\n",
       "      <td>59.0954</td>\n",
       "      <td>Standard</td>\n",
       "      <td>71.343699</td>\n",
       "      <td>0.105208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>182</td>\n",
       "      <td>3618.2491</td>\n",
       "      <td>8.5330</td>\n",
       "      <td>5.8177</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.6113</td>\n",
       "      <td>86.8132</td>\n",
       "      <td>91.7582</td>\n",
       "      <td>92.8571</td>\n",
       "      <td>92.3077</td>\n",
       "      <td>88.5141</td>\n",
       "      <td>27.5227</td>\n",
       "      <td>71.8774</td>\n",
       "      <td>Standard</td>\n",
       "      <td>79.332924</td>\n",
       "      <td>0.117192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>189</td>\n",
       "      <td>6569.7812</td>\n",
       "      <td>10.2963</td>\n",
       "      <td>5.0588</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.3504</td>\n",
       "      <td>71.9577</td>\n",
       "      <td>87.3016</td>\n",
       "      <td>90.4762</td>\n",
       "      <td>88.3598</td>\n",
       "      <td>75.8404</td>\n",
       "      <td>15.3411</td>\n",
       "      <td>44.1954</td>\n",
       "      <td>Standard</td>\n",
       "      <td>55.846619</td>\n",
       "      <td>0.097122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>140</td>\n",
       "      <td>3784.5589</td>\n",
       "      <td>7.0571</td>\n",
       "      <td>7.0571</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.4885</td>\n",
       "      <td>82.1429</td>\n",
       "      <td>91.4286</td>\n",
       "      <td>93.5714</td>\n",
       "      <td>93.5714</td>\n",
       "      <td>85.7860</td>\n",
       "      <td>18.4283</td>\n",
       "      <td>62.5224</td>\n",
       "      <td>Standard</td>\n",
       "      <td>72.329640</td>\n",
       "      <td>0.141701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>174</td>\n",
       "      <td>4418.9840</td>\n",
       "      <td>10.7414</td>\n",
       "      <td>5.0523</td>\n",
       "      <td>1.0</td>\n",
       "      <td>56.8683</td>\n",
       "      <td>84.4828</td>\n",
       "      <td>92.5287</td>\n",
       "      <td>93.6782</td>\n",
       "      <td>94.8276</td>\n",
       "      <td>83.5604</td>\n",
       "      <td>28.2358</td>\n",
       "      <td>63.4723</td>\n",
       "      <td>Standard</td>\n",
       "      <td>72.144098</td>\n",
       "      <td>0.093098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>240</td>\n",
       "      <td>3233.6006</td>\n",
       "      <td>5.1875</td>\n",
       "      <td>3.1172</td>\n",
       "      <td>1.0</td>\n",
       "      <td>33.3914</td>\n",
       "      <td>87.9167</td>\n",
       "      <td>92.0833</td>\n",
       "      <td>93.7500</td>\n",
       "      <td>97.0833</td>\n",
       "      <td>88.3074</td>\n",
       "      <td>13.4761</td>\n",
       "      <td>68.0280</td>\n",
       "      <td>Standard</td>\n",
       "      <td>76.852406</td>\n",
       "      <td>0.192771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>74</td>\n",
       "      <td>73</td>\n",
       "      <td>24.5969</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>7.4000</td>\n",
       "      <td>99.6873</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.843405</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>78</td>\n",
       "      <td>231</td>\n",
       "      <td>58.8862</td>\n",
       "      <td>4.9567</td>\n",
       "      <td>4.9567</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.8744</td>\n",
       "      <td>98.2684</td>\n",
       "      <td>98.7013</td>\n",
       "      <td>99.1342</td>\n",
       "      <td>98.7013</td>\n",
       "      <td>92.6531</td>\n",
       "      <td>67.7989</td>\n",
       "      <td>85.5156</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>88.941385</td>\n",
       "      <td>0.201747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>80</td>\n",
       "      <td>79</td>\n",
       "      <td>0.5675</td>\n",
       "      <td>6.6835</td>\n",
       "      <td>6.6835</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.1957</td>\n",
       "      <td>98.7342</td>\n",
       "      <td>98.7342</td>\n",
       "      <td>98.7342</td>\n",
       "      <td>98.7342</td>\n",
       "      <td>98.7662</td>\n",
       "      <td>7.8051</td>\n",
       "      <td>98.7342</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>98.750197</td>\n",
       "      <td>0.149622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>86</td>\n",
       "      <td>85</td>\n",
       "      <td>0.0186</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>87</td>\n",
       "      <td>86</td>\n",
       "      <td>0.1546</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>89</td>\n",
       "      <td>88</td>\n",
       "      <td>0.0450</td>\n",
       "      <td>1.2386</td>\n",
       "      <td>1.2386</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.7320</td>\n",
       "      <td>96.5909</td>\n",
       "      <td>98.8636</td>\n",
       "      <td>98.8636</td>\n",
       "      <td>96.5909</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>58.9499</td>\n",
       "      <td>86.0058</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>92.476471</td>\n",
       "      <td>0.807363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>98</td>\n",
       "      <td>97</td>\n",
       "      <td>0.4440</td>\n",
       "      <td>4.2062</td>\n",
       "      <td>4.2062</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.4141</td>\n",
       "      <td>98.9691</td>\n",
       "      <td>98.9691</td>\n",
       "      <td>98.9691</td>\n",
       "      <td>98.9691</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>91.6077</td>\n",
       "      <td>98.9691</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.481879</td>\n",
       "      <td>0.237744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>99</td>\n",
       "      <td>196</td>\n",
       "      <td>0.4924</td>\n",
       "      <td>4.1786</td>\n",
       "      <td>4.1786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.2546</td>\n",
       "      <td>98.4694</td>\n",
       "      <td>98.9796</td>\n",
       "      <td>98.9796</td>\n",
       "      <td>98.4694</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.9273</td>\n",
       "      <td>96.3484</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>98.140245</td>\n",
       "      <td>0.239315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>100</td>\n",
       "      <td>396</td>\n",
       "      <td>0.4535</td>\n",
       "      <td>4.1414</td>\n",
       "      <td>4.1414</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0984</td>\n",
       "      <td>98.9899</td>\n",
       "      <td>98.9899</td>\n",
       "      <td>98.9899</td>\n",
       "      <td>98.9899</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>99.1899</td>\n",
       "      <td>98.9899</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.492386</td>\n",
       "      <td>0.241464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>117</td>\n",
       "      <td>116</td>\n",
       "      <td>3.5216</td>\n",
       "      <td>5.3017</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.1309</td>\n",
       "      <td>99.1379</td>\n",
       "      <td>99.1379</td>\n",
       "      <td>99.1379</td>\n",
       "      <td>99.1379</td>\n",
       "      <td>99.1379</td>\n",
       "      <td>32.2449</td>\n",
       "      <td>96.0633</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>97.576386</td>\n",
       "      <td>0.188619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>121</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0270</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>125</td>\n",
       "      <td>124</td>\n",
       "      <td>0.3558</td>\n",
       "      <td>5.0242</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>44.6305</td>\n",
       "      <td>99.1935</td>\n",
       "      <td>99.1935</td>\n",
       "      <td>99.1935</td>\n",
       "      <td>99.1935</td>\n",
       "      <td>99.1935</td>\n",
       "      <td>81.7351</td>\n",
       "      <td>99.1935</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.193500</td>\n",
       "      <td>0.199037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>136</td>\n",
       "      <td>135</td>\n",
       "      <td>0.0505</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>94.7357</td>\n",
       "      <td>95.5549</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>97.726930</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>138</td>\n",
       "      <td>137</td>\n",
       "      <td>0.3518</td>\n",
       "      <td>2.4964</td>\n",
       "      <td>2.4964</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.3099</td>\n",
       "      <td>97.0803</td>\n",
       "      <td>97.0803</td>\n",
       "      <td>97.0803</td>\n",
       "      <td>97.8102</td>\n",
       "      <td>99.9947</td>\n",
       "      <td>50.9633</td>\n",
       "      <td>87.0249</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>93.060073</td>\n",
       "      <td>0.400577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>139</td>\n",
       "      <td>138</td>\n",
       "      <td>0.3248</td>\n",
       "      <td>2.6377</td>\n",
       "      <td>2.6377</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.4078</td>\n",
       "      <td>96.3768</td>\n",
       "      <td>96.3768</td>\n",
       "      <td>96.3768</td>\n",
       "      <td>97.1014</td>\n",
       "      <td>99.9947</td>\n",
       "      <td>51.5262</td>\n",
       "      <td>84.4435</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>91.563488</td>\n",
       "      <td>0.379118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>140</td>\n",
       "      <td>139</td>\n",
       "      <td>0.3271</td>\n",
       "      <td>2.6978</td>\n",
       "      <td>2.6978</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5997</td>\n",
       "      <td>96.4029</td>\n",
       "      <td>96.4029</td>\n",
       "      <td>96.4029</td>\n",
       "      <td>97.1223</td>\n",
       "      <td>99.9948</td>\n",
       "      <td>52.1151</td>\n",
       "      <td>82.5364</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>90.430686</td>\n",
       "      <td>0.370672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>152</td>\n",
       "      <td>151</td>\n",
       "      <td>0.3544</td>\n",
       "      <td>4.4834</td>\n",
       "      <td>4.4834</td>\n",
       "      <td>1.0</td>\n",
       "      <td>36.9085</td>\n",
       "      <td>98.6755</td>\n",
       "      <td>98.6755</td>\n",
       "      <td>98.6755</td>\n",
       "      <td>99.3377</td>\n",
       "      <td>99.3465</td>\n",
       "      <td>15.0026</td>\n",
       "      <td>98.6556</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>98.999845</td>\n",
       "      <td>0.223045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>159</td>\n",
       "      <td>158</td>\n",
       "      <td>0.4085</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>82.0944</td>\n",
       "      <td>97.6965</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>98.834830</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>165</td>\n",
       "      <td>164</td>\n",
       "      <td>0.2889</td>\n",
       "      <td>1.6463</td>\n",
       "      <td>1.6463</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.8643</td>\n",
       "      <td>98.7805</td>\n",
       "      <td>98.7805</td>\n",
       "      <td>98.7805</td>\n",
       "      <td>99.3902</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>69.3881</td>\n",
       "      <td>90.3090</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>94.907755</td>\n",
       "      <td>0.607423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>166</td>\n",
       "      <td>330</td>\n",
       "      <td>0.3010</td>\n",
       "      <td>1.7273</td>\n",
       "      <td>1.7273</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.9171</td>\n",
       "      <td>98.1818</td>\n",
       "      <td>98.1818</td>\n",
       "      <td>98.1818</td>\n",
       "      <td>98.7879</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>70.2365</td>\n",
       "      <td>88.2130</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>93.737415</td>\n",
       "      <td>0.578938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>169</td>\n",
       "      <td>168</td>\n",
       "      <td>48.0724</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>16.9000</td>\n",
       "      <td>99.8479</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.923892</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>172</td>\n",
       "      <td>171</td>\n",
       "      <td>0.2694</td>\n",
       "      <td>1.6316</td>\n",
       "      <td>1.6316</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.6613</td>\n",
       "      <td>98.2456</td>\n",
       "      <td>98.2456</td>\n",
       "      <td>98.8304</td>\n",
       "      <td>99.4152</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>75.8330</td>\n",
       "      <td>83.2155</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>90.838930</td>\n",
       "      <td>0.612895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>174</td>\n",
       "      <td>173</td>\n",
       "      <td>0.3016</td>\n",
       "      <td>1.6069</td>\n",
       "      <td>1.6069</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.6006</td>\n",
       "      <td>98.2659</td>\n",
       "      <td>98.2659</td>\n",
       "      <td>98.8439</td>\n",
       "      <td>99.4220</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>78.0412</td>\n",
       "      <td>86.6177</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>92.829030</td>\n",
       "      <td>0.622316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>177</td>\n",
       "      <td>352</td>\n",
       "      <td>0.2638</td>\n",
       "      <td>1.5028</td>\n",
       "      <td>1.5028</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.4685</td>\n",
       "      <td>99.1477</td>\n",
       "      <td>99.1477</td>\n",
       "      <td>99.4318</td>\n",
       "      <td>99.7159</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>81.2064</td>\n",
       "      <td>98.0871</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.034314</td>\n",
       "      <td>0.665425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>181</td>\n",
       "      <td>1260</td>\n",
       "      <td>0.2592</td>\n",
       "      <td>1.4778</td>\n",
       "      <td>1.4778</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.3922</td>\n",
       "      <td>99.4444</td>\n",
       "      <td>99.4444</td>\n",
       "      <td>99.4444</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>86.6843</td>\n",
       "      <td>99.7315</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.865570</td>\n",
       "      <td>0.676682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>182</td>\n",
       "      <td>362</td>\n",
       "      <td>0.2701</td>\n",
       "      <td>1.4779</td>\n",
       "      <td>1.4779</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.3747</td>\n",
       "      <td>99.1713</td>\n",
       "      <td>99.4475</td>\n",
       "      <td>99.4475</td>\n",
       "      <td>99.7238</td>\n",
       "      <td>99.9985</td>\n",
       "      <td>88.3022</td>\n",
       "      <td>97.5388</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>98.753336</td>\n",
       "      <td>0.676636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>183</td>\n",
       "      <td>364</td>\n",
       "      <td>0.2705</td>\n",
       "      <td>1.4808</td>\n",
       "      <td>1.4808</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.3572</td>\n",
       "      <td>98.6264</td>\n",
       "      <td>99.4505</td>\n",
       "      <td>99.4505</td>\n",
       "      <td>99.7253</td>\n",
       "      <td>99.9955</td>\n",
       "      <td>90.0849</td>\n",
       "      <td>95.8058</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>97.855825</td>\n",
       "      <td>0.675311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>380</td>\n",
       "      <td>379</td>\n",
       "      <td>0.2527</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>99.8294</td>\n",
       "      <td>97.1615</td>\n",
       "      <td>98.9263</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.375798</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>403</td>\n",
       "      <td>402</td>\n",
       "      <td>23.2286</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>40.3000</td>\n",
       "      <td>99.9290</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>99.964487</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>791</td>\n",
       "      <td>790</td>\n",
       "      <td>0.1722</td>\n",
       "      <td>1.5684</td>\n",
       "      <td>1.5684</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.9646</td>\n",
       "      <td>99.8734</td>\n",
       "      <td>99.8734</td>\n",
       "      <td>99.8734</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>92.1362</td>\n",
       "      <td>99.8694</td>\n",
       "      <td>92.1362</td>\n",
       "      <td>Typed \\&amp; Backoff</td>\n",
       "      <td>92.136200</td>\n",
       "      <td>0.637592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>264 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    numNonTypes  subjects    duration     mean  meanOfHits  median    stddev  \\\n",
       "1             2      2414  10511.0329  86.7498     40.9176    11.0  159.6074   \n",
       "2             3      4298   8789.6953  49.0021     31.6770    21.0  110.5881   \n",
       "3             4      2496   6587.6079  38.3385     25.4090     3.0   91.8028   \n",
       "4             5      7080   4020.3970  16.2027     12.7617     1.0   56.0671   \n",
       "5             6      7040   3387.6348  10.2548      9.3488     1.0   42.6847   \n",
       "..          ...       ...         ...      ...         ...     ...       ...   \n",
       "84          182       362      0.2701   1.4779      1.4779     1.0    6.3747   \n",
       "85          183       364      0.2705   1.4808      1.4808     1.0    6.3572   \n",
       "86          380       379      0.2527   1.0000      1.0000     1.0    0.0000   \n",
       "87          403       402     23.2286   1.0000      1.0000     1.0    0.0000   \n",
       "88          791       790      0.1722   1.5684      1.5684     1.0   15.9646   \n",
       "\n",
       "        top1      top5     top10      topL    recall  precision  precisionAtL  \\\n",
       "1    19.8012   31.1516   31.1516   19.8012   90.0166     5.2850       19.8012   \n",
       "2    40.4374   44.3229   45.1605   40.5305   94.1019     9.2746       40.4723   \n",
       "3    42.3478   57.6122   66.3462   46.5946   95.1522    12.2951       42.1207   \n",
       "4    71.2288   72.5989   73.7994   71.4689   98.5064    45.6096       69.7434   \n",
       "5    71.8608   75.5966   91.8892   72.4148   98.7637    34.9995       67.6870   \n",
       "..       ...       ...       ...       ...       ...        ...           ...   \n",
       "84   99.1713   99.4475   99.4475   99.7238   99.9985    88.3022       97.5388   \n",
       "85   98.6264   99.4505   99.4505   99.7253   99.9955    90.0849       95.8058   \n",
       "86  100.0000  100.0000  100.0000  100.0000   99.8294    97.1615       98.9263   \n",
       "87  100.0000  100.0000  100.0000  100.0000  100.0000    40.3000       99.9290   \n",
       "88   99.8734   99.8734   99.8734  100.0000   92.1362    99.8694       92.1362   \n",
       "\n",
       "         Recommender         f1        RR  \n",
       "1           Standard  32.461708  0.011527  \n",
       "2           Standard  56.601047  0.020407  \n",
       "3           Standard  58.392840  0.026083  \n",
       "4           Standard  81.666323  0.061718  \n",
       "5           Standard  80.324307  0.097515  \n",
       "..               ...        ...       ...  \n",
       "84  Typed \\& Backoff  98.753336  0.676636  \n",
       "85  Typed \\& Backoff  97.855825  0.675311  \n",
       "86  Typed \\& Backoff  99.375798  1.000000  \n",
       "87  Typed \\& Backoff  99.964487  1.000000  \n",
       "88  Typed \\& Backoff  92.136200  0.637592  \n",
       "\n",
       "[264 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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