Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "opening-combining",
   "metadata": {},
   "source": [
    "# Plots for the Report and Paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "continuous-cooperative",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib\n",
    "plt.style.use('seaborn-dark-palette')\n",
    "#sns.set_theme(font='serif')\n",
    "sns.set_style('whitegrid')\n",
    "matplotlib.rc(\"font\", family=\"DeJaVu Serif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "removable-sustainability",
   "metadata": {},
   "source": [
    "## Mobile OS Market Share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "superb-desert",
   "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>Android</th>\n",
       "      <th>iOS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>28.21</td>\n",
       "      <td>24.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>29.25</td>\n",
       "      <td>23.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>30.19</td>\n",
       "      <td>23.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>31.67</td>\n",
       "      <td>23.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>33.19</td>\n",
       "      <td>23.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>72.92</td>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>71.18</td>\n",
       "      <td>28.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>72.48</td>\n",
       "      <td>26.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>71.93</td>\n",
       "      <td>27.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>71.90</td>\n",
       "      <td>27.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Android    iOS\n",
       "Date                      \n",
       "2012-08-01    28.21  24.48\n",
       "2012-09-01    29.25  23.63\n",
       "2012-10-01    30.19  23.72\n",
       "2012-11-01    31.67  23.73\n",
       "2012-12-01    33.19  23.26\n",
       "...             ...    ...\n",
       "2020-10-01    72.92  26.53\n",
       "2020-11-01    71.18  28.19\n",
       "2020-12-01    72.48  26.91\n",
       "2021-01-01    71.93  27.47\n",
       "2021-02-01    71.90  27.33\n",
       "\n",
       "[103 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share = pd.read_csv(\n",
    "    \"https://gs.statcounter.com/os-market-share/mobile/chart.php?device=Mobile&device_hidden=mobile&statType_hidden=os_combined&region_hidden=ww&granularity=monthly&statType=Operating%20System&region=Worldwide&fromInt=201208&toInt=202102&fromMonthYear=2012-08&toMonthYear=2021-02&csv=1\",\n",
    "    parse_dates=['Date'],  \n",
    "    date_parser=lambda x: datetime.strptime(x, '%Y-%m')\n",
    ").set_index('Date')\n",
    "share = share[['Android', 'iOS']]\n",
    "share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "perfect-satin",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=share).set(ylabel='Market Share (%)', xlabel='Year', ylim=[0,100]);\n",
    "plt.tight_layout()\n",
    "plt.savefig('mobile_os_share.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "civilian-waste",
   "metadata": {},
   "source": [
    "## Smart Devices Market Share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "muslim-fraud",
   "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>Desktop</th>\n",
       "      <th>Mobile</th>\n",
       "      <th>Tablet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-08-01</th>\n",
       "      <td>85.69</td>\n",
       "      <td>11.44</td>\n",
       "      <td>2.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-09-01</th>\n",
       "      <td>85.33</td>\n",
       "      <td>11.66</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-10-01</th>\n",
       "      <td>85.02</td>\n",
       "      <td>11.92</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-01</th>\n",
       "      <td>84.13</td>\n",
       "      <td>12.66</td>\n",
       "      <td>3.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-01</th>\n",
       "      <td>82.42</td>\n",
       "      <td>14.04</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-10-01</th>\n",
       "      <td>48.88</td>\n",
       "      <td>48.62</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-01</th>\n",
       "      <td>44.22</td>\n",
       "      <td>52.95</td>\n",
       "      <td>2.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-01</th>\n",
       "      <td>41.46</td>\n",
       "      <td>55.73</td>\n",
       "      <td>2.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-01</th>\n",
       "      <td>41.45</td>\n",
       "      <td>55.68</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>42.63</td>\n",
       "      <td>54.46</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Desktop  Mobile  Tablet\n",
       "Date                               \n",
       "2012-08-01    85.69   11.44    2.86\n",
       "2012-09-01    85.33   11.66    3.01\n",
       "2012-10-01    85.02   11.92    3.06\n",
       "2012-11-01    84.13   12.66    3.22\n",
       "2012-12-01    82.42   14.04    3.54\n",
       "...             ...     ...     ...\n",
       "2020-10-01    48.88   48.62    2.50\n",
       "2020-11-01    44.22   52.95    2.83\n",
       "2020-12-01    41.46   55.73    2.81\n",
       "2021-01-01    41.45   55.68    2.87\n",
       "2021-02-01    42.63   54.46    2.91\n",
       "\n",
       "[103 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share = pd.read_csv(\n",
    "    \"https://gs.statcounter.com/platform-market-share/desktop-mobile-tablet/worldwide/chart.php?device=Desktop%20%26%20Mobile%20%26%20Tablet&device_hidden=desktop%2Bmobile%2Btablet&multi-device=true&statType_hidden=comparison&region_hidden=ww&granularity=monthly&statType=Platform%20Comparison&region=Worldwide&fromInt=201208&toInt=202102&fromMonthYear=2012-08&toMonthYear=2021-02&csv=1\",\n",
    "    parse_dates=['Date'],  \n",
    "    date_parser=lambda x: datetime.strptime(x, '%Y-%m')\n",
    ").set_index('Date')\n",
    "share"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "israeli-mexican",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=share).set(ylabel='Market Share (%)', xlabel='Year', ylim=[0,100]);\n",
    "plt.tight_layout()\n",
    "plt.savefig('device_share.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surface-society",
   "metadata": {},
   "source": [
    "## Comparision of Accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "flying-anthony",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wandb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "close-copying",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = wandb.Api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "unnecessary-eugene",
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = api.runs(\"vinayakakv/malware_homo_testing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "improved-powell",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_list = [] \n",
    "config_list = [] \n",
    "name_list = [] \n",
    "for run in runs: \n",
    "    # run.summary are the output key/values like accuracy.  We call ._json_dict to omit large files \n",
    "    summary_list.append(run.summary._json_dict) \n",
    "\n",
    "    # run.config is the input metrics.  We remove special values that start with _.\n",
    "    config_list.append({k:v for k,v in run.config.items() if not k.startswith('_')}) \n",
    "\n",
    "    # run.name is the name of the run.\n",
    "    name_list.append(run.name)       \n",
    "\n",
    "import pandas as pd \n",
    "summary_df = pd.DataFrame.from_records(summary_list) \n",
    "config_df = pd.DataFrame.from_records(config_list) \n",
    "name_df = pd.DataFrame({'name': name_list}) \n",
    "all_df = pd.concat([name_df, config_df,summary_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "vital-density",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>features</th>\n",
       "      <th>input_dimension</th>\n",
       "      <th>convolution_count</th>\n",
       "      <th>convolution_algorithm</th>\n",
       "      <th>test_type</th>\n",
       "      <th>test_recall</th>\n",
       "      <th>ROC</th>\n",
       "      <th>test_f1</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>_runtime</th>\n",
       "      <th>_timestamp</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>test_precision</th>\n",
       "      <th>test_confusion_matrix</th>\n",
       "      <th>PRC</th>\n",
       "      <th>_step</th>\n",
       "      <th>epoch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>honest-lion-40</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.731320</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_30eb7ab1.plotly.j...</td>\n",
       "      <td>0.767626</td>\n",
       "      <td>0.483147</td>\n",
       "      <td>10</td>\n",
       "      <td>1613321899</td>\n",
       "      <td>0.779668</td>\n",
       "      <td>0.807726</td>\n",
       "      <td>{'size': 33568, '_type': 'image-file', 'width'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_da631dd0.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dandy-cherry-39</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.600954</td>\n",
       "      <td>{'size': 87317, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.591101</td>\n",
       "      <td>6</td>\n",
       "      <td>1613321885</td>\n",
       "      <td>0.667722</td>\n",
       "      <td>0.691042</td>\n",
       "      <td>{'width': 640, 'format': 'png', 'height': 480,...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_9068b746.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>autumn-bird-38</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.596184</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_47772c0d.plotly.j...</td>\n",
       "      <td>0.625261</td>\n",
       "      <td>0.652178</td>\n",
       "      <td>12</td>\n",
       "      <td>1613321873</td>\n",
       "      <td>0.644383</td>\n",
       "      <td>0.657318</td>\n",
       "      <td>{'height': 480, 'sha256': 'f98e3fc6d4e33253efd...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_decb1bef.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>firm-violet-37</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.895072</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_b8b3a0fb.plotly.j...</td>\n",
       "      <td>0.901160</td>\n",
       "      <td>0.311922</td>\n",
       "      <td>12</td>\n",
       "      <td>1613321824</td>\n",
       "      <td>0.902294</td>\n",
       "      <td>0.907333</td>\n",
       "      <td>{'width': 640, 'format': 'png', 'height': 480,...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': 'd0202273e1...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>iconic-fog-36</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.375199</td>\n",
       "      <td>{'size': 88589, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.478702</td>\n",
       "      <td>0.670026</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321808</td>\n",
       "      <td>0.593354</td>\n",
       "      <td>0.661064</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'sha256': '1b12d30f369c66a01efbf6773bef0109d8...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>drawn-forest-35</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.414149</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_abfbd629.plotly.j...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.661118</td>\n",
       "      <td>8</td>\n",
       "      <td>1613321797</td>\n",
       "      <td>0.587816</td>\n",
       "      <td>0.630751</td>\n",
       "      <td>{'height': 480, 'sha256': '2961d795f7e1b4c2d92...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_ce96b71c.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>devoted-wave-34</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>2</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.897456</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_035bc987.plotly.j...</td>\n",
       "      <td>0.899602</td>\n",
       "      <td>0.295598</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321785</td>\n",
       "      <td>0.900316</td>\n",
       "      <td>0.901757</td>\n",
       "      <td>{'sha256': '2d9e1c4e9e39d85c23aa8cf4fe475297df...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_6c43fd53.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cosmic-water-33</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.399046</td>\n",
       "      <td>{'size': 89990, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.506815</td>\n",
       "      <td>0.663883</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321770</td>\n",
       "      <td>0.613528</td>\n",
       "      <td>0.694329</td>\n",
       "      <td>{'height': 480, 'sha256': 'b9ca923d214afd941b6...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_96be0b95.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>mild-flower-32</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.453895</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_4b6ffda7.plotly.j...</td>\n",
       "      <td>0.529194</td>\n",
       "      <td>0.661981</td>\n",
       "      <td>8</td>\n",
       "      <td>1613321757</td>\n",
       "      <td>0.598101</td>\n",
       "      <td>0.634444</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': '8a5fb835f4...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>noble-sunset-31</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.903021</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_9fcda238.plotly.j...</td>\n",
       "      <td>0.897669</td>\n",
       "      <td>0.303778</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321743</td>\n",
       "      <td>0.897547</td>\n",
       "      <td>0.892380</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': '5788e302ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gentle-cosmos-30</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.614467</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_2bd1f262.plotly.j...</td>\n",
       "      <td>0.649853</td>\n",
       "      <td>0.580079</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321729</td>\n",
       "      <td>0.670491</td>\n",
       "      <td>0.689563</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_e35153da.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>volcanic-cherry-29</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DotGatConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.476948</td>\n",
       "      <td>{'sha256': '7d5887cdc1db052db6be7913bd66d2afa2...</td>\n",
       "      <td>0.546697</td>\n",
       "      <td>0.665017</td>\n",
       "      <td>13</td>\n",
       "      <td>1613321718</td>\n",
       "      <td>0.606408</td>\n",
       "      <td>0.640342</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': '8d...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_46b2d4fe.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>fresh-blaze-28</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_79d10720.plotly.j...</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>0.272167</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321650</td>\n",
       "      <td>0.919304</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_10d974c3.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>woven-fire-27</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>{'sha256': '447d19c77c687432304824d484a98202bb...</td>\n",
       "      <td>0.693061</td>\n",
       "      <td>0.572262</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321635</td>\n",
       "      <td>0.714794</td>\n",
       "      <td>0.746104</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': 'a5...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': 'ab6f99c343...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>deft-sky-26</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.539746</td>\n",
       "      <td>{'size': 90780, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.620658</td>\n",
       "      <td>0.622288</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321622</td>\n",
       "      <td>0.671677</td>\n",
       "      <td>0.730108</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_f2f6e9b2.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>azure-frost-25</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>2</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.905405</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_1c78ec99.plotly.j...</td>\n",
       "      <td>0.909018</td>\n",
       "      <td>0.281225</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321611</td>\n",
       "      <td>0.909810</td>\n",
       "      <td>0.912660</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': 'f7...</td>\n",
       "      <td>{'size': 85446, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sparkling-bird-24</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.646264</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_220c6406.plotly.j...</td>\n",
       "      <td>0.686366</td>\n",
       "      <td>0.574910</td>\n",
       "      <td>6</td>\n",
       "      <td>1613321594</td>\n",
       "      <td>0.706092</td>\n",
       "      <td>0.731773</td>\n",
       "      <td>{'size': 33103, '_type': 'image-file', 'width'...</td>\n",
       "      <td>{'size': 88665, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>rural-paper-23</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.494436</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_1708b813.plotly.j...</td>\n",
       "      <td>0.590413</td>\n",
       "      <td>0.642523</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321584</td>\n",
       "      <td>0.658623</td>\n",
       "      <td>0.732627</td>\n",
       "      <td>{'size': 33953, '_type': 'image-file', 'width'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_c3ef5523.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>hopeful-salad-22</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.895866</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': '1f007f737f...</td>\n",
       "      <td>0.898724</td>\n",
       "      <td>0.293036</td>\n",
       "      <td>10</td>\n",
       "      <td>1613321571</td>\n",
       "      <td>0.899525</td>\n",
       "      <td>0.901600</td>\n",
       "      <td>{'sha256': '3d276c6e6d47cb7df654b30173aae211f1...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_0aff8678.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>dandy-cherry-21</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.596979</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_089ae3eb.plotly.j...</td>\n",
       "      <td>0.646856</td>\n",
       "      <td>0.591148</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321557</td>\n",
       "      <td>0.675633</td>\n",
       "      <td>0.705827</td>\n",
       "      <td>{'width': 640, 'format': 'png', 'height': 480,...</td>\n",
       "      <td>{'size': 89083, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>sunny-disco-20</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>TAGConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.593005</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_19b382e1.plotly.j...</td>\n",
       "      <td>0.627418</td>\n",
       "      <td>0.649883</td>\n",
       "      <td>13</td>\n",
       "      <td>1613321546</td>\n",
       "      <td>0.649525</td>\n",
       "      <td>0.666071</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_9a735dcb.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>zesty-disco-19</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.898251</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_ce331d5d.plotly.j...</td>\n",
       "      <td>0.918699</td>\n",
       "      <td>0.217029</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321463</td>\n",
       "      <td>0.920886</td>\n",
       "      <td>0.940100</td>\n",
       "      <td>{'width': 640, 'format': 'png', 'height': 480,...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': '5f3b9b2502...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>legendary-haze-18</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.793323</td>\n",
       "      <td>{'size': 91426, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.836196</td>\n",
       "      <td>0.394767</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321448</td>\n",
       "      <td>0.845332</td>\n",
       "      <td>0.883968</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': '60...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_18127b91.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>dry-thunder-17</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.697933</td>\n",
       "      <td>{'size': 91698, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.756897</td>\n",
       "      <td>0.475679</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321437</td>\n",
       "      <td>0.776899</td>\n",
       "      <td>0.826742</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_009d717e.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>dulcet-sky-16</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>2</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.920509</td>\n",
       "      <td>{'sha256': '2ef201f8556cbb56c368ed79fcec54bde3...</td>\n",
       "      <td>0.922342</td>\n",
       "      <td>0.228596</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321424</td>\n",
       "      <td>0.922864</td>\n",
       "      <td>0.924182</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': '34...</td>\n",
       "      <td>{'size': 77204, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>pretty-tree-15</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.686010</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_35b5c3ff.plotly.j...</td>\n",
       "      <td>0.753383</td>\n",
       "      <td>0.462514</td>\n",
       "      <td>6</td>\n",
       "      <td>1613321409</td>\n",
       "      <td>0.776503</td>\n",
       "      <td>0.835431</td>\n",
       "      <td>{'height': 480, 'sha256': '4787fe15033bae01f81...</td>\n",
       "      <td>{'size': 89055, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>misty-lake-14</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.672496</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_22eab885.plotly.j...</td>\n",
       "      <td>0.736292</td>\n",
       "      <td>0.540524</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321399</td>\n",
       "      <td>0.760285</td>\n",
       "      <td>0.813462</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_7f857718.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>crimson-frost-13</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.861685</td>\n",
       "      <td>{'sha256': '73ea2be61a3049a46fe1bcdcfee491c362...</td>\n",
       "      <td>0.880227</td>\n",
       "      <td>0.284259</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321386</td>\n",
       "      <td>0.883307</td>\n",
       "      <td>0.899585</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_dc5f85c6.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fresh-valley-12</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.632750</td>\n",
       "      <td>{'size': 89920, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.690074</td>\n",
       "      <td>0.543652</td>\n",
       "      <td>6</td>\n",
       "      <td>1613321369</td>\n",
       "      <td>0.717168</td>\n",
       "      <td>0.758818</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_1387672b.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>rose-sky-11</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAGEConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.654213</td>\n",
       "      <td>{'sha256': 'ffb598a9a490d03394ed42c0c48a7f3839...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.626029</td>\n",
       "      <td>10</td>\n",
       "      <td>1613321359</td>\n",
       "      <td>0.674446</td>\n",
       "      <td>0.679604</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': 'f4...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_50177a47.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>lemon-bee-10</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>3</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.906200</td>\n",
       "      <td>{'sha256': '06a7e351aee857a06c308c19c8e920eca1...</td>\n",
       "      <td>0.917136</td>\n",
       "      <td>0.254944</td>\n",
       "      <td>12</td>\n",
       "      <td>1613321238</td>\n",
       "      <td>0.918513</td>\n",
       "      <td>0.928339</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_883f1cec.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>proud-snowball-9</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.793323</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_ab94f933.plotly.j...</td>\n",
       "      <td>0.780602</td>\n",
       "      <td>0.469596</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321222</td>\n",
       "      <td>0.778085</td>\n",
       "      <td>0.768283</td>\n",
       "      <td>{'_type': 'image-file', 'width': 640, 'format'...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': '5a9fe0cfec...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>olive-sound-8</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.678855</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': '174672bc03...</td>\n",
       "      <td>0.651908</td>\n",
       "      <td>0.630201</td>\n",
       "      <td>8</td>\n",
       "      <td>1613321211</td>\n",
       "      <td>0.639241</td>\n",
       "      <td>0.627019</td>\n",
       "      <td>{'height': 480, 'sha256': 'ed1923c8b69fbb4130e...</td>\n",
       "      <td>{'sha256': '382de19e12b8fab6f3bf021c7ae86c4891...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>major-dream-7</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>2</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.902226</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_8a6328dd.plotly.j...</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.273080</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321197</td>\n",
       "      <td>0.910206</td>\n",
       "      <td>0.916061</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': 'f8...</td>\n",
       "      <td>{'sha256': 'e719822c1b28e9507c65a3e62a2927d745...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>prime-fog-6</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.667727</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_076a9cc4.plotly.j...</td>\n",
       "      <td>0.732026</td>\n",
       "      <td>0.498726</td>\n",
       "      <td>8</td>\n",
       "      <td>1613321180</td>\n",
       "      <td>0.756725</td>\n",
       "      <td>0.810029</td>\n",
       "      <td>{'size': 34282, '_type': 'image-file', 'width'...</td>\n",
       "      <td>{'size': 90118, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>wild-frost-5</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.562798</td>\n",
       "      <td>{'path': 'media/plotly/ROC_0_c97e4bc8.plotly.j...</td>\n",
       "      <td>0.596713</td>\n",
       "      <td>0.641396</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321166</td>\n",
       "      <td>0.621440</td>\n",
       "      <td>0.634978</td>\n",
       "      <td>{'height': 480, 'sha256': '5e080a21d79095eb7f4...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_3b3daf10.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>rosy-cherry-4</td>\n",
       "      <td>hybrid</td>\n",
       "      <td>247</td>\n",
       "      <td>1</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.905405</td>\n",
       "      <td>{'size': 93559, '_type': 'plotly-file', 'sha25...</td>\n",
       "      <td>0.895440</td>\n",
       "      <td>0.316766</td>\n",
       "      <td>11</td>\n",
       "      <td>1613321155</td>\n",
       "      <td>0.894778</td>\n",
       "      <td>0.885692</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': 'c7...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_64416a4b.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>easy-glitter-3</td>\n",
       "      <td>method</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.689984</td>\n",
       "      <td>{'sha256': '3d2d2b62bf336ddd74f0184c5f92005a37...</td>\n",
       "      <td>0.714109</td>\n",
       "      <td>0.548712</td>\n",
       "      <td>7</td>\n",
       "      <td>1613321138</td>\n",
       "      <td>0.725079</td>\n",
       "      <td>0.739983</td>\n",
       "      <td>{'path': 'media/images/test_confusion_matrix_0...</td>\n",
       "      <td>{'path': 'media/plotly/PRC_0_ed93bd78.plotly.j...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>feasible-silence-2</td>\n",
       "      <td>degree</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GraphConv</td>\n",
       "      <td>best</td>\n",
       "      <td>0.549285</td>\n",
       "      <td>{'sha256': '74da80596fd5478b19e615c575cad44ecd...</td>\n",
       "      <td>0.594664</td>\n",
       "      <td>0.648644</td>\n",
       "      <td>13</td>\n",
       "      <td>1613321127</td>\n",
       "      <td>0.627373</td>\n",
       "      <td>0.648218</td>\n",
       "      <td>{'format': 'png', 'height': 480, 'sha256': '4e...</td>\n",
       "      <td>{'_type': 'plotly-file', 'sha256': 'ab467d8459...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  name features  input_dimension  convolution_count  \\\n",
       "0       honest-lion-40   hybrid              247                  0   \n",
       "1      dandy-cherry-39   method                5                  0   \n",
       "2       autumn-bird-38   degree                1                  0   \n",
       "3       firm-violet-37   hybrid              247                  3   \n",
       "4        iconic-fog-36   method                5                  3   \n",
       "5      drawn-forest-35   degree                1                  3   \n",
       "6      devoted-wave-34   hybrid              247                  2   \n",
       "7      cosmic-water-33   method                5                  2   \n",
       "8       mild-flower-32   degree                1                  2   \n",
       "9      noble-sunset-31   hybrid              247                  1   \n",
       "10    gentle-cosmos-30   method                5                  1   \n",
       "11  volcanic-cherry-29   degree                1                  1   \n",
       "12      fresh-blaze-28   hybrid              247                  3   \n",
       "13       woven-fire-27   method                5                  3   \n",
       "14         deft-sky-26   degree                1                  3   \n",
       "15      azure-frost-25   hybrid              247                  2   \n",
       "16   sparkling-bird-24   method                5                  2   \n",
       "17      rural-paper-23   degree                1                  2   \n",
       "18    hopeful-salad-22   hybrid              247                  1   \n",
       "19     dandy-cherry-21   method                5                  1   \n",
       "20      sunny-disco-20   degree                1                  1   \n",
       "21      zesty-disco-19   hybrid              247                  3   \n",
       "22   legendary-haze-18   method                5                  3   \n",
       "23      dry-thunder-17   degree                1                  3   \n",
       "24       dulcet-sky-16   hybrid              247                  2   \n",
       "25      pretty-tree-15   method                5                  2   \n",
       "26       misty-lake-14   degree                1                  2   \n",
       "27    crimson-frost-13   hybrid              247                  1   \n",
       "28     fresh-valley-12   method                5                  1   \n",
       "29         rose-sky-11   degree                1                  1   \n",
       "30        lemon-bee-10   hybrid              247                  3   \n",
       "31    proud-snowball-9   method                5                  3   \n",
       "32       olive-sound-8   degree                1                  3   \n",
       "33       major-dream-7   hybrid              247                  2   \n",
       "34         prime-fog-6   method                5                  2   \n",
       "35        wild-frost-5   degree                1                  2   \n",
       "36       rosy-cherry-4   hybrid              247                  1   \n",
       "37      easy-glitter-3   method                5                  1   \n",
       "38  feasible-silence-2   degree                1                  1   \n",
       "\n",
       "   convolution_algorithm test_type  test_recall  \\\n",
       "0              GraphConv      best     0.731320   \n",
       "1              GraphConv      best     0.600954   \n",
       "2              GraphConv      best     0.596184   \n",
       "3             DotGatConv      best     0.895072   \n",
       "4             DotGatConv      best     0.375199   \n",
       "5             DotGatConv      best     0.414149   \n",
       "6             DotGatConv      best     0.897456   \n",
       "7             DotGatConv      best     0.399046   \n",
       "8             DotGatConv      best     0.453895   \n",
       "9             DotGatConv      best     0.903021   \n",
       "10            DotGatConv      best     0.614467   \n",
       "11            DotGatConv      best     0.476948   \n",
       "12               TAGConv      best     0.918919   \n",
       "13               TAGConv      best     0.647059   \n",
       "14               TAGConv      best     0.539746   \n",
       "15               TAGConv      best     0.905405   \n",
       "16               TAGConv      best     0.646264   \n",
       "17               TAGConv      best     0.494436   \n",
       "18               TAGConv      best     0.895866   \n",
       "19               TAGConv      best     0.596979   \n",
       "20               TAGConv      best     0.593005   \n",
       "21              SAGEConv      best     0.898251   \n",
       "22              SAGEConv      best     0.793323   \n",
       "23              SAGEConv      best     0.697933   \n",
       "24              SAGEConv      best     0.920509   \n",
       "25              SAGEConv      best     0.686010   \n",
       "26              SAGEConv      best     0.672496   \n",
       "27              SAGEConv      best     0.861685   \n",
       "28              SAGEConv      best     0.632750   \n",
       "29              SAGEConv      best     0.654213   \n",
       "30             GraphConv      best     0.906200   \n",
       "31             GraphConv      best     0.793323   \n",
       "32             GraphConv      best     0.678855   \n",
       "33             GraphConv      best     0.902226   \n",
       "34             GraphConv      best     0.667727   \n",
       "35             GraphConv      best     0.562798   \n",
       "36             GraphConv      best     0.905405   \n",
       "37             GraphConv      best     0.689984   \n",
       "38             GraphConv      best     0.549285   \n",
       "\n",
       "                                                  ROC   test_f1  test_loss  \\\n",
       "0   {'path': 'media/plotly/ROC_0_30eb7ab1.plotly.j...  0.767626   0.483147   \n",
       "1   {'size': 87317, '_type': 'plotly-file', 'sha25...  0.642857   0.591101   \n",
       "2   {'path': 'media/plotly/ROC_0_47772c0d.plotly.j...  0.625261   0.652178   \n",
       "3   {'path': 'media/plotly/ROC_0_b8b3a0fb.plotly.j...  0.901160   0.311922   \n",
       "4   {'size': 88589, '_type': 'plotly-file', 'sha25...  0.478702   0.670026   \n",
       "5   {'path': 'media/plotly/ROC_0_abfbd629.plotly.j...  0.500000   0.661118   \n",
       "6   {'path': 'media/plotly/ROC_0_035bc987.plotly.j...  0.899602   0.295598   \n",
       "7   {'size': 89990, '_type': 'plotly-file', 'sha25...  0.506815   0.663883   \n",
       "8   {'path': 'media/plotly/ROC_0_4b6ffda7.plotly.j...  0.529194   0.661981   \n",
       "9   {'path': 'media/plotly/ROC_0_9fcda238.plotly.j...  0.897669   0.303778   \n",
       "10  {'path': 'media/plotly/ROC_0_2bd1f262.plotly.j...  0.649853   0.580079   \n",
       "11  {'sha256': '7d5887cdc1db052db6be7913bd66d2afa2...  0.546697   0.665017   \n",
       "12  {'path': 'media/plotly/ROC_0_79d10720.plotly.j...  0.918919   0.272167   \n",
       "13  {'sha256': '447d19c77c687432304824d484a98202bb...  0.693061   0.572262   \n",
       "14  {'size': 90780, '_type': 'plotly-file', 'sha25...  0.620658   0.622288   \n",
       "15  {'path': 'media/plotly/ROC_0_1c78ec99.plotly.j...  0.909018   0.281225   \n",
       "16  {'path': 'media/plotly/ROC_0_220c6406.plotly.j...  0.686366   0.574910   \n",
       "17  {'path': 'media/plotly/ROC_0_1708b813.plotly.j...  0.590413   0.642523   \n",
       "18  {'_type': 'plotly-file', 'sha256': '1f007f737f...  0.898724   0.293036   \n",
       "19  {'path': 'media/plotly/ROC_0_089ae3eb.plotly.j...  0.646856   0.591148   \n",
       "20  {'path': 'media/plotly/ROC_0_19b382e1.plotly.j...  0.627418   0.649883   \n",
       "21  {'path': 'media/plotly/ROC_0_ce331d5d.plotly.j...  0.918699   0.217029   \n",
       "22  {'size': 91426, '_type': 'plotly-file', 'sha25...  0.836196   0.394767   \n",
       "23  {'size': 91698, '_type': 'plotly-file', 'sha25...  0.756897   0.475679   \n",
       "24  {'sha256': '2ef201f8556cbb56c368ed79fcec54bde3...  0.922342   0.228596   \n",
       "25  {'path': 'media/plotly/ROC_0_35b5c3ff.plotly.j...  0.753383   0.462514   \n",
       "26  {'path': 'media/plotly/ROC_0_22eab885.plotly.j...  0.736292   0.540524   \n",
       "27  {'sha256': '73ea2be61a3049a46fe1bcdcfee491c362...  0.880227   0.284259   \n",
       "28  {'size': 89920, '_type': 'plotly-file', 'sha25...  0.690074   0.543652   \n",
       "29  {'sha256': 'ffb598a9a490d03394ed42c0c48a7f3839...  0.666667   0.626029   \n",
       "30  {'sha256': '06a7e351aee857a06c308c19c8e920eca1...  0.917136   0.254944   \n",
       "31  {'path': 'media/plotly/ROC_0_ab94f933.plotly.j...  0.780602   0.469596   \n",
       "32  {'_type': 'plotly-file', 'sha256': '174672bc03...  0.651908   0.630201   \n",
       "33  {'path': 'media/plotly/ROC_0_8a6328dd.plotly.j...  0.909091   0.273080   \n",
       "34  {'path': 'media/plotly/ROC_0_076a9cc4.plotly.j...  0.732026   0.498726   \n",
       "35  {'path': 'media/plotly/ROC_0_c97e4bc8.plotly.j...  0.596713   0.641396   \n",
       "36  {'size': 93559, '_type': 'plotly-file', 'sha25...  0.895440   0.316766   \n",
       "37  {'sha256': '3d2d2b62bf336ddd74f0184c5f92005a37...  0.714109   0.548712   \n",
       "38  {'sha256': '74da80596fd5478b19e615c575cad44ecd...  0.594664   0.648644   \n",
       "\n",
       "    _runtime  _timestamp  test_accuracy  test_precision  \\\n",
       "0         10  1613321899       0.779668        0.807726   \n",
       "1          6  1613321885       0.667722        0.691042   \n",
       "2         12  1613321873       0.644383        0.657318   \n",
       "3         12  1613321824       0.902294        0.907333   \n",
       "4          7  1613321808       0.593354        0.661064   \n",
       "5          8  1613321797       0.587816        0.630751   \n",
       "6         11  1613321785       0.900316        0.901757   \n",
       "7          7  1613321770       0.613528        0.694329   \n",
       "8          8  1613321757       0.598101        0.634444   \n",
       "9         11  1613321743       0.897547        0.892380   \n",
       "10         7  1613321729       0.670491        0.689563   \n",
       "11        13  1613321718       0.606408        0.640342   \n",
       "12        11  1613321650       0.919304        0.918919   \n",
       "13         7  1613321635       0.714794        0.746104   \n",
       "14         7  1613321622       0.671677        0.730108   \n",
       "15        11  1613321611       0.909810        0.912660   \n",
       "16         6  1613321594       0.706092        0.731773   \n",
       "17         7  1613321584       0.658623        0.732627   \n",
       "18        10  1613321571       0.899525        0.901600   \n",
       "19         7  1613321557       0.675633        0.705827   \n",
       "20        13  1613321546       0.649525        0.666071   \n",
       "21        11  1613321463       0.920886        0.940100   \n",
       "22         7  1613321448       0.845332        0.883968   \n",
       "23         7  1613321437       0.776899        0.826742   \n",
       "24        11  1613321424       0.922864        0.924182   \n",
       "25         6  1613321409       0.776503        0.835431   \n",
       "26         7  1613321399       0.760285        0.813462   \n",
       "27        11  1613321386       0.883307        0.899585   \n",
       "28         6  1613321369       0.717168        0.758818   \n",
       "29        10  1613321359       0.674446        0.679604   \n",
       "30        12  1613321238       0.918513        0.928339   \n",
       "31         7  1613321222       0.778085        0.768283   \n",
       "32         8  1613321211       0.639241        0.627019   \n",
       "33        11  1613321197       0.910206        0.916061   \n",
       "34         8  1613321180       0.756725        0.810029   \n",
       "35         7  1613321166       0.621440        0.634978   \n",
       "36        11  1613321155       0.894778        0.885692   \n",
       "37         7  1613321138       0.725079        0.739983   \n",
       "38        13  1613321127       0.627373        0.648218   \n",
       "\n",
       "                                test_confusion_matrix  \\\n",
       "0   {'size': 33568, '_type': 'image-file', 'width'...   \n",
       "1   {'width': 640, 'format': 'png', 'height': 480,...   \n",
       "2   {'height': 480, 'sha256': 'f98e3fc6d4e33253efd...   \n",
       "3   {'width': 640, 'format': 'png', 'height': 480,...   \n",
       "4   {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "5   {'height': 480, 'sha256': '2961d795f7e1b4c2d92...   \n",
       "6   {'sha256': '2d9e1c4e9e39d85c23aa8cf4fe475297df...   \n",
       "7   {'height': 480, 'sha256': 'b9ca923d214afd941b6...   \n",
       "8   {'path': 'media/images/test_confusion_matrix_0...   \n",
       "9   {'path': 'media/images/test_confusion_matrix_0...   \n",
       "10  {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "11  {'format': 'png', 'height': 480, 'sha256': '8d...   \n",
       "12  {'path': 'media/images/test_confusion_matrix_0...   \n",
       "13  {'format': 'png', 'height': 480, 'sha256': 'a5...   \n",
       "14  {'path': 'media/images/test_confusion_matrix_0...   \n",
       "15  {'format': 'png', 'height': 480, 'sha256': 'f7...   \n",
       "16  {'size': 33103, '_type': 'image-file', 'width'...   \n",
       "17  {'size': 33953, '_type': 'image-file', 'width'...   \n",
       "18  {'sha256': '3d276c6e6d47cb7df654b30173aae211f1...   \n",
       "19  {'width': 640, 'format': 'png', 'height': 480,...   \n",
       "20  {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "21  {'width': 640, 'format': 'png', 'height': 480,...   \n",
       "22  {'format': 'png', 'height': 480, 'sha256': '60...   \n",
       "23  {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "24  {'format': 'png', 'height': 480, 'sha256': '34...   \n",
       "25  {'height': 480, 'sha256': '4787fe15033bae01f81...   \n",
       "26  {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "27  {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "28  {'path': 'media/images/test_confusion_matrix_0...   \n",
       "29  {'format': 'png', 'height': 480, 'sha256': 'f4...   \n",
       "30  {'path': 'media/images/test_confusion_matrix_0...   \n",
       "31  {'_type': 'image-file', 'width': 640, 'format'...   \n",
       "32  {'height': 480, 'sha256': 'ed1923c8b69fbb4130e...   \n",
       "33  {'format': 'png', 'height': 480, 'sha256': 'f8...   \n",
       "34  {'size': 34282, '_type': 'image-file', 'width'...   \n",
       "35  {'height': 480, 'sha256': '5e080a21d79095eb7f4...   \n",
       "36  {'format': 'png', 'height': 480, 'sha256': 'c7...   \n",
       "37  {'path': 'media/images/test_confusion_matrix_0...   \n",
       "38  {'format': 'png', 'height': 480, 'sha256': '4e...   \n",
       "\n",
       "                                                  PRC  _step  epoch  \n",
       "0   {'path': 'media/plotly/PRC_0_da631dd0.plotly.j...      0      0  \n",
       "1   {'path': 'media/plotly/PRC_0_9068b746.plotly.j...      0      0  \n",
       "2   {'path': 'media/plotly/PRC_0_decb1bef.plotly.j...      0      0  \n",
       "3   {'_type': 'plotly-file', 'sha256': 'd0202273e1...      0      0  \n",
       "4   {'sha256': '1b12d30f369c66a01efbf6773bef0109d8...      0      0  \n",
       "5   {'path': 'media/plotly/PRC_0_ce96b71c.plotly.j...      0      0  \n",
       "6   {'path': 'media/plotly/PRC_0_6c43fd53.plotly.j...      0      0  \n",
       "7   {'path': 'media/plotly/PRC_0_96be0b95.plotly.j...      0      0  \n",
       "8   {'_type': 'plotly-file', 'sha256': '8a5fb835f4...      0      0  \n",
       "9   {'_type': 'plotly-file', 'sha256': '5788e302ba...      0      0  \n",
       "10  {'path': 'media/plotly/PRC_0_e35153da.plotly.j...      0      0  \n",
       "11  {'path': 'media/plotly/PRC_0_46b2d4fe.plotly.j...      0      0  \n",
       "12  {'path': 'media/plotly/PRC_0_10d974c3.plotly.j...      0      0  \n",
       "13  {'_type': 'plotly-file', 'sha256': 'ab6f99c343...      0      0  \n",
       "14  {'path': 'media/plotly/PRC_0_f2f6e9b2.plotly.j...      0      0  \n",
       "15  {'size': 85446, '_type': 'plotly-file', 'sha25...      0      0  \n",
       "16  {'size': 88665, '_type': 'plotly-file', 'sha25...      0      0  \n",
       "17  {'path': 'media/plotly/PRC_0_c3ef5523.plotly.j...      0      0  \n",
       "18  {'path': 'media/plotly/PRC_0_0aff8678.plotly.j...      0      0  \n",
       "19  {'size': 89083, '_type': 'plotly-file', 'sha25...      0      0  \n",
       "20  {'path': 'media/plotly/PRC_0_9a735dcb.plotly.j...      0      0  \n",
       "21  {'_type': 'plotly-file', 'sha256': '5f3b9b2502...      0      0  \n",
       "22  {'path': 'media/plotly/PRC_0_18127b91.plotly.j...      0      0  \n",
       "23  {'path': 'media/plotly/PRC_0_009d717e.plotly.j...      0      0  \n",
       "24  {'size': 77204, '_type': 'plotly-file', 'sha25...      0      0  \n",
       "25  {'size': 89055, '_type': 'plotly-file', 'sha25...      0      0  \n",
       "26  {'path': 'media/plotly/PRC_0_7f857718.plotly.j...      0      0  \n",
       "27  {'path': 'media/plotly/PRC_0_dc5f85c6.plotly.j...      0      0  \n",
       "28  {'path': 'media/plotly/PRC_0_1387672b.plotly.j...      0      0  \n",
       "29  {'path': 'media/plotly/PRC_0_50177a47.plotly.j...      0      0  \n",
       "30  {'path': 'media/plotly/PRC_0_883f1cec.plotly.j...      0      0  \n",
       "31  {'_type': 'plotly-file', 'sha256': '5a9fe0cfec...      0      0  \n",
       "32  {'sha256': '382de19e12b8fab6f3bf021c7ae86c4891...      0      0  \n",
       "33  {'sha256': 'e719822c1b28e9507c65a3e62a2927d745...      0      0  \n",
       "34  {'size': 90118, '_type': 'plotly-file', 'sha25...      0      0  \n",
       "35  {'path': 'media/plotly/PRC_0_3b3daf10.plotly.j...      0      0  \n",
       "36  {'path': 'media/plotly/PRC_0_64416a4b.plotly.j...      0      0  \n",
       "37  {'path': 'media/plotly/PRC_0_ed93bd78.plotly.j...      0      0  \n",
       "38  {'_type': 'plotly-file', 'sha256': 'ab467d8459...      0      0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "reliable-white",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=2, style='ticks')\n",
    "matplotlib.rc(\"font\", family=\"DeJaVu Serif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "computational-spare",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1603.45x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(all_df[['features', 'convolution_count', 'convolution_algorithm', 'test_accuracy', 'test_precision', 'test_recall', 'test_f1']])\n",
    "results.columns = ['$F$', 'n', 'Algorithm', 'Accuracy (%)', 'Precision', 'Recall', 'F1-Score']\n",
    "results['Accuracy (%)'] *= 100\n",
    "results.loc[results['Algorithm']=='DotGatConv', ['Algorithm']] = 'DotGATConv'\n",
    "g = sns.relplot(\n",
    "    data=results.query('n!=0'),\n",
    "    x='n',\n",
    "    y=\"Accuracy (%)\",\n",
    "    hue='$F$',\n",
    "    style='$F$',\n",
    "    col='Algorithm',\n",
    "    kind='line',\n",
    "    markers=True,\n",
    "    dashes=True,\n",
    "    linewidth=6,\n",
    "    col_order=['GraphConv', 'SAGEConv', 'DotGATConv', 'TAGConv'],\n",
    ").set(\n",
    "    xticks=[1,2,3],\n",
    "    xlabel='$n$'\n",
    ")\n",
    "#plt.tight_layout()\n",
    "plt.savefig('acc_plot.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "funky-dairy",
   "metadata": {},
   "source": [
    "## Dataset Balancing steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "irish-bonus",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: pytorch\n"
     ]
    }
   ],
   "source": [
    "import joblib as J\n",
    "import dgl\n",
    "from pathlib import Path\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "available-italian",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cut_plot_histogram(\n",
    "        x,\n",
    "        hue,\n",
    "        data,\n",
    "        bins,\n",
    "        hue_order,\n",
    "        top_limit,\n",
    "        bottom_limit,\n",
    "        xlabel,\n",
    "        ylabel,\n",
    "        file_name\n",
    "):\n",
    "    f, (ax, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "    sns.histplot(x=x, hue=hue, data=data, bins=bins, ax=ax, multiple='dodge', hue_order=hue_order)\n",
    "    sns.histplot(x=x, hue=hue, data=data, bins=bins, ax=ax2, multiple='dodge', hue_order=hue_order)\n",
    "    ax.set_ylim(*top_limit)\n",
    "    ax2.set_ylim(*bottom_limit)\n",
    "    ax.spines['bottom'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax2.spines['top'].set_visible(False)\n",
    "    ax2.spines['right'].set_visible(False)\n",
    "    ax.xaxis.tick_top()\n",
    "    ax.tick_params(labeltop='off')  # don't put tick labels at the top\n",
    "    ax2.xaxis.tick_bottom()\n",
    "    d = .015  # how big to make the diagonal lines in axes coordinates\n",
    "    # arguments to pass plot, just so we don't keep repeating them\n",
    "    kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)\n",
    "    ax.plot((-d, +d), (-d, +d), **kwargs)  # top-left diagonal\n",
    "    # ax.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal\n",
    "\n",
    "    kwargs.update(transform=ax2.transAxes)  # switch to the bottom axes\n",
    "    ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal\n",
    "    # ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal\n",
    "    ax.set_ylabel(\"\")\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax2.set_xlabel(xlabel)\n",
    "    ax2.set_ylabel(ylabel)\n",
    "    ax.xaxis.set_visible(False)\n",
    "    ax2.get_legend().remove()\n",
    "    plt.savefig(file_name, dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "endless-pregnancy",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "breeding-literature",
   "metadata": {},
   "source": [
    "### MalDroid APK Size distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "final-reasoning",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(file):\n",
    "    return {\n",
    "        \"file_name\": str(file),\n",
    "        \"file_size\": file.stat().st_size,\n",
    "        \"label\": \"Benign\" if \"Ben\" in str(file) else \"Malware\"\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "fantastic-committee",
   "metadata": {},
   "outputs": [],
   "source": [
    "maldroid_path = Path(\"/home2/vinayak/datasets/MalDroid2020/raw/\")\n",
    "androzoo_path = Path(\"/home2/vinayak/datasets/ZooDroid/raw/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "danish-northwest",
   "metadata": {},
   "outputs": [],
   "source": [
    "maldroid_sizes = J.Parallel(n_jobs=40)(J.delayed(process)(x) for x in maldroid_path.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "defined-andorra",
   "metadata": {},
   "outputs": [],
   "source": [
    "androzoo_sizes = J.Parallel(n_jobs=40)(J.delayed(process)(x) for x in androzoo_path.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "duplicate-roberts",
   "metadata": {},
   "outputs": [],
   "source": [
    "maldroid_sizes = pd.DataFrame.from_records(maldroid_sizes)\n",
    "androzoo_sizes = pd.DataFrame.from_records(androzoo_sizes)\n",
    "total_sizes = pd.concat([maldroid_sizes, androzoo_sizes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "foreign-seeker",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(0, np.max(total_sizes['file_size']), 0.5e7) # 5MB Bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "above-beatles",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='file_size', ylabel='Count'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    x='file_size',\n",
    "    hue='label',\n",
    "    data=maldroid_sizes,\n",
    "    bins=bins,\n",
    "    multiple='dodge',\n",
    "    hue_order=['Benign', 'Malware']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "necessary-mitchell",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut_plot_histogram(\n",
    "    x='file_size',\n",
    "    hue='label',\n",
    "    data=maldroid_sizes,\n",
    "    bins=bins,\n",
    "    hue_order=['Benign', 'Malware'],\n",
    "    top_limit=(9000, 11000),\n",
    "    bottom_limit=(0, 2000),\n",
    "    xlabel='APK Size (Bytes)',\n",
    "    ylabel='Number of APKs',\n",
    "    file_name='initial_apk_sizes.pdf'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "straight-capitol",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='file_size', ylabel='Count'>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    x='file_size',\n",
    "    hue='label',\n",
    "    data=total_sizes,\n",
    "    bins=bins,\n",
    "    multiple='dodge',\n",
    "    hue_order=['Benign', 'Malware']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "wound-location",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut_plot_histogram(\n",
    "    x='file_size',\n",
    "    hue='label',\n",
    "    data=total_sizes,\n",
    "    bins=bins,\n",
    "    hue_order=['Benign', 'Malware'],\n",
    "    top_limit=(9000, 11000),\n",
    "    bottom_limit=(0, 2000),\n",
    "    xlabel='APK Size (Bytes)',\n",
    "    ylabel='Number of APKs',\n",
    "    file_name='final_apk_sizes.pdf'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unavailable-american",
   "metadata": {},
   "source": [
    "### Initial Number of Nodes distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "brilliant-morocco",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(file):\n",
    "    graphs, labels = dgl.load_graphs(str(file))\n",
    "    graph = graphs[0]\n",
    "    return {\n",
    "        \"num_nodes\": graph.num_nodes(),\n",
    "        \"file_name\": file,\n",
    "        \"label\": \"Benign\" if \"Ben\" in str(file) else \"Malware\"\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "invalid-stranger",
   "metadata": {},
   "outputs": [],
   "source": [
    "maldroid_path = Path(\"/home2/vinayak/Homo\")\n",
    "androzoo_path = Path(\"/home2/vinayak/Homo1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "competent-breathing",
   "metadata": {},
   "outputs": [],
   "source": [
    "maldroid_sizes = J.Parallel(n_jobs=40)(J.delayed(process)(x) for x in maldroid_path.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "floppy-suicide",
   "metadata": {},
   "outputs": [],
   "source": [
    "androzoo_sizes = J.Parallel(n_jobs=40)(J.delayed(process)(x) for x in androzoo_path.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "numerous-prefix",
   "metadata": {},
   "outputs": [],
   "source": [
    "maldroid_sizes = pd.DataFrame.from_records(maldroid_sizes)\n",
    "androzoo_sizes = pd.DataFrame.from_records(androzoo_sizes)\n",
    "total_sizes = pd.concat([maldroid_sizes, androzoo_sizes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "functioning-pressing",
   "metadata": {},
   "outputs": [],
   "source": [
    "q1 = total_sizes['num_nodes'].quantile(0.25)\n",
    "q3 = total_sizes['num_nodes'].quantile(0.75)\n",
    "iqr = q3 - q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "french-candy",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_sizes = total_sizes.query(\"@q1 - 1.5*@iqr  < num_nodes < @q3 + 1.5*@iqr \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "proprietary-basket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24518, 20911)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(total_sizes), len(filtered_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "raising-singles",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(0, np.max(filtered_sizes['num_nodes']), 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "nonprofit-rhythm",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_nodes', ylabel='Count'>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    x='num_nodes',\n",
    "    hue='label',\n",
    "    data=filtered_sizes,\n",
    "    bins=bins,\n",
    "    multiple='dodge',\n",
    "    hue_order=['Benign', 'Malware']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "north-render",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut_plot_histogram(\n",
    "    x='num_nodes',\n",
    "    hue='label',\n",
    "    data=filtered_sizes,\n",
    "    bins=bins,\n",
    "    hue_order=['Benign', 'Malware'],\n",
    "    top_limit=(5000, 7000),\n",
    "    bottom_limit=(0, 2000),\n",
    "    xlabel='Number of nodes $|M|$ of FCG $G(M,E)$',\n",
    "    ylabel='Number of FCGs',\n",
    "    file_name='fcg_imbal.pdf'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "missing-secretary",
   "metadata": {},
   "source": [
    "## Balanced Number of nodes distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "appointed-standing",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = Path(\"/home2/vinayak/homo_train\")\n",
    "test_path = Path(\"/home2/vinayak/homo_test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "desperate-terry",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sizes = J.Parallel(n_jobs=40)(J.delayed(process)(x) for x in train_path.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "biological-henry",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_sizes = J.Parallel(n_jobs=40)(J.delayed(process)(x) for x in test_path.iterdir())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "completed-variable",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sizes = pd.DataFrame.from_records(train_sizes)\n",
    "test_sizes = pd.DataFrame.from_records(test_sizes)\n",
    "total_sizes = pd.concat([train_sizes, test_sizes])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "static-daily",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.arange(0, np.max(total_sizes['num_nodes']), 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "random-context",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cut_plot_histogram(\n",
    "    x='num_nodes',\n",
    "    hue='label',\n",
    "    data=total_sizes,\n",
    "    bins=bins,\n",
    "    hue_order=['Benign', 'Malware'],\n",
    "    top_limit=(5000, 7000),\n",
    "    bottom_limit=(0, 2000),\n",
    "    xlabel='Number of nodes $|M|$ of FCG $G(M,E)$',\n",
    "    ylabel='Number of FCGs',\n",
    "    file_name='fcg_bal.pdf'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "relevant-concentration",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}