KG_testing_euadr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40e21692-a376-440d-b4e2-b13810d90935",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b514b21e-0c8b-4efc-974f-7e2ed85d2418",
   "metadata": {},
   "source": [
    "### KB testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2a3249e-d88d-414c-a7d9-19470bfad6c7",
   "metadata": {},
   "source": [
    "### EUADR skip 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "3597183e-72c4-4a5c-b18b-1ebdcd4dabbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "51\n",
      "89\n",
      "178\n",
      "258\n",
      "349\n",
      "450\n",
      "582\n",
      "706\n",
      "798\n",
      "12\n",
      "44\n",
      "116\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "184\n",
      "268\n",
      "344\n",
      "455\n",
      "612\n",
      "736\n",
      "838\n",
      "6\n",
      "38\n",
      "108\n",
      "169\n",
      "235\n",
      "310\n",
      "405\n",
      "556\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "745\n",
      "837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3497506773.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "seeds = [0,42,144]\n",
    "results_df = pd.DataFrame(columns=['seed','% sample_size', '# of relations', '# of additional relations skip 1',\"% of additional relations skip 1\"])\n",
    "\n",
    "dataset = \"euadr\"\n",
    "# sample_size = 20\n",
    "for seed in seeds:\n",
    "    # print(f\"SEED value: {seed}\")\n",
    "    for sample_size in range(10,101, 10):\n",
    "        kb_ = pd.read_csv(f\"/home/016651544/gemma/samples/{seed}/{dataset}/processed_csvs/{sample_size}_sample_val_{dataset}.csv\")\n",
    "        original_sample = pd.read_csv(f\"/home/016651544/gemma/samples/{seed}/{dataset}/{sample_size}_sample_val_{dataset}.csv\")\n",
    "        full_data = pd.read_csv(f\"/home/016651544/gemma/{dataset}_unmasked.csv\")\n",
    "        # Create tuples of entity combinations in both dataframes\n",
    "        original_sample['entity_pair'] = original_sample.apply(lambda row: tuple(sorted([row['ENTITY1_TEXT'].lower(), row['ENTITY2_TEXT'].lower()])), axis=1)\n",
    "        kb_['entity_pair'] = kb_.apply(lambda row: tuple(sorted([row['ent1'].lower(), row['ent2'].lower()])), axis=1)\n",
    "        # Check if the entity pairs in df2 are present in df1\n",
    "        kb_['is_present'] = kb_['entity_pair'].isin(original_sample['entity_pair'])\n",
    "        \n",
    "        # Count the matches\n",
    "        matches_count = kb_['is_present'].sum()\n",
    "        # print(f\"Number of matching pairs: {matches_count}\")\n",
    "        # print(original_sample['entity_pair'].shape, kb_['entity_pair'].shape)\n",
    "        percentage_additional = ((kb_['entity_pair'].shape[0] - matches_count) / original_sample['entity_pair'].shape[0]) * 100\n",
    "        print(kb_.shape[0])\n",
    "        results_df = results_df.append({\n",
    "            'seed': seed,\n",
    "           '% sample_size': sample_size,\n",
    "             '# of relations': len(original_sample['entity_pair'].unique()),\n",
    "            '# of additional relations skip 1': kb_['entity_pair'].shape[0],\n",
    "            '% of additional relations skip 1': percentage_additional#(original_sample['entity_pair'].shape[0]+kb_['entity_pair'].shape[0])*100/original_sample['entity_pair'].shape[0]\n",
    "        }, ignore_index=True)\n",
    "\n",
    "\n",
    "results_df[['seed', '% sample_size','# of relations', '# of additional relations skip 1']] = results_df[['seed','% sample_size', '# of relations', '# of additional relations skip 1']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1853cae5-e511-4593-9706-e8e8d07bf691",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seed</th>\n",
       "      <th>% sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th># of additional relations skip 1</th>\n",
       "      <th>% of additional relations skip 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "      <td>34.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>69</td>\n",
       "      <td>51</td>\n",
       "      <td>71.830986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>98</td>\n",
       "      <td>89</td>\n",
       "      <td>83.962264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>128</td>\n",
       "      <td>178</td>\n",
       "      <td>125.352113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>258</td>\n",
       "      <td>145.762712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>192</td>\n",
       "      <td>349</td>\n",
       "      <td>163.849765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>219</td>\n",
       "      <td>450</td>\n",
       "      <td>181.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>245</td>\n",
       "      <td>582</td>\n",
       "      <td>204.929577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>273</td>\n",
       "      <td>706</td>\n",
       "      <td>221.316614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>290</td>\n",
       "      <td>798</td>\n",
       "      <td>224.788732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>34.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>67</td>\n",
       "      <td>44</td>\n",
       "      <td>61.971831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>102</td>\n",
       "      <td>116</td>\n",
       "      <td>109.433962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>135</td>\n",
       "      <td>184</td>\n",
       "      <td>129.577465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "      <td>166</td>\n",
       "      <td>268</td>\n",
       "      <td>151.412429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "      <td>60</td>\n",
       "      <td>192</td>\n",
       "      <td>344</td>\n",
       "      <td>161.502347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>455</td>\n",
       "      <td>183.467742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>42</td>\n",
       "      <td>80</td>\n",
       "      <td>246</td>\n",
       "      <td>612</td>\n",
       "      <td>215.492958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42</td>\n",
       "      <td>90</td>\n",
       "      <td>271</td>\n",
       "      <td>736</td>\n",
       "      <td>230.721003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>289</td>\n",
       "      <td>838</td>\n",
       "      <td>236.056338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>144</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>17.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>144</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>38</td>\n",
       "      <td>53.521127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>144</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>108</td>\n",
       "      <td>101.886792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>144</td>\n",
       "      <td>40</td>\n",
       "      <td>127</td>\n",
       "      <td>169</td>\n",
       "      <td>119.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>144</td>\n",
       "      <td>50</td>\n",
       "      <td>154</td>\n",
       "      <td>235</td>\n",
       "      <td>132.768362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>144</td>\n",
       "      <td>60</td>\n",
       "      <td>185</td>\n",
       "      <td>310</td>\n",
       "      <td>145.539906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>212</td>\n",
       "      <td>405</td>\n",
       "      <td>163.306452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>144</td>\n",
       "      <td>80</td>\n",
       "      <td>240</td>\n",
       "      <td>556</td>\n",
       "      <td>195.774648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>144</td>\n",
       "      <td>90</td>\n",
       "      <td>272</td>\n",
       "      <td>745</td>\n",
       "      <td>233.542320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>144</td>\n",
       "      <td>100</td>\n",
       "      <td>289</td>\n",
       "      <td>837</td>\n",
       "      <td>235.774648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    seed  % sample_size  # of relations  # of additional relations skip 1  \\\n",
       "0      0             10              33                                12   \n",
       "1      0             20              69                                51   \n",
       "2      0             30              98                                89   \n",
       "3      0             40             128                               178   \n",
       "4      0             50             160                               258   \n",
       "5      0             60             192                               349   \n",
       "6      0             70             219                               450   \n",
       "7      0             80             245                               582   \n",
       "8      0             90             273                               706   \n",
       "9      0            100             290                               798   \n",
       "10    42             10              35                                12   \n",
       "11    42             20              67                                44   \n",
       "12    42             30             102                               116   \n",
       "13    42             40             135                               184   \n",
       "14    42             50             166                               268   \n",
       "15    42             60             192                               344   \n",
       "16    42             70             218                               455   \n",
       "17    42             80             246                               612   \n",
       "18    42             90             271                               736   \n",
       "19    42            100             289                               838   \n",
       "20   144             10              32                                 6   \n",
       "21   144             20              65                                38   \n",
       "22   144             30              97                               108   \n",
       "23   144             40             127                               169   \n",
       "24   144             50             154                               235   \n",
       "25   144             60             185                               310   \n",
       "26   144             70             212                               405   \n",
       "27   144             80             240                               556   \n",
       "28   144             90             272                               745   \n",
       "29   144            100             289                               837   \n",
       "\n",
       "    % of additional relations skip 1  \n",
       "0                          34.285714  \n",
       "1                          71.830986  \n",
       "2                          83.962264  \n",
       "3                         125.352113  \n",
       "4                         145.762712  \n",
       "5                         163.849765  \n",
       "6                         181.451613  \n",
       "7                         204.929577  \n",
       "8                         221.316614  \n",
       "9                         224.788732  \n",
       "10                         34.285714  \n",
       "11                         61.971831  \n",
       "12                        109.433962  \n",
       "13                        129.577465  \n",
       "14                        151.412429  \n",
       "15                        161.502347  \n",
       "16                        183.467742  \n",
       "17                        215.492958  \n",
       "18                        230.721003  \n",
       "19                        236.056338  \n",
       "20                         17.142857  \n",
       "21                         53.521127  \n",
       "22                        101.886792  \n",
       "23                        119.014085  \n",
       "24                        132.768362  \n",
       "25                        145.539906  \n",
       "26                        163.306452  \n",
       "27                        195.774648  \n",
       "28                        233.542320  \n",
       "29                        235.774648  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "267a379e-5288-4693-b1e7-f39216275b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_df_1 = results_df.groupby('% sample_size').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "40c58f98-e972-4b2f-a697-c15346ba801b",
   "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>% sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th># of additional relations skip 1</th>\n",
       "      <th>% of additional relations skip 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>28.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>62.441315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>104.333333</td>\n",
       "      <td>98.427673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>124.647887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>253.666667</td>\n",
       "      <td>143.314501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>189.666667</td>\n",
       "      <td>334.333333</td>\n",
       "      <td>156.964006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>216.333333</td>\n",
       "      <td>436.666667</td>\n",
       "      <td>176.075269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>243.666667</td>\n",
       "      <td>583.333333</td>\n",
       "      <td>205.399061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>228.526646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>289.333333</td>\n",
       "      <td>824.333333</td>\n",
       "      <td>232.206573</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   % sample_size  # of relations  # of additional relations skip 1  \\\n",
       "0             10       33.333333                         10.000000   \n",
       "1             20       67.000000                         44.333333   \n",
       "2             30       99.000000                        104.333333   \n",
       "3             40      130.000000                        177.000000   \n",
       "4             50      160.000000                        253.666667   \n",
       "5             60      189.666667                        334.333333   \n",
       "6             70      216.333333                        436.666667   \n",
       "7             80      243.666667                        583.333333   \n",
       "8             90      272.000000                        729.000000   \n",
       "9            100      289.333333                        824.333333   \n",
       "\n",
       "   % of additional relations skip 1  \n",
       "0                         28.571429  \n",
       "1                         62.441315  \n",
       "2                         98.427673  \n",
       "3                        124.647887  \n",
       "4                        143.314501  \n",
       "5                        156.964006  \n",
       "6                        176.075269  \n",
       "7                        205.399061  \n",
       "8                        228.526646  \n",
       "9                        232.206573  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_df_1[['% sample_size','# of relations', '# of additional relations skip 1','% of additional relations skip 1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72149fd9-f119-4c92-b215-884b8aca4e56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eee8981e-bd38-49a0-8147-e0f7fb12a96b",
   "metadata": {},
   "source": [
    "### EUADR skip 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "7ab88ff6-cb72-47dd-8e52-cbd771cf4aef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEED value: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "SEED value: 42\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "SEED value: 144\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3885630333.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "seeds = [0,42,144]\n",
    "results_df = pd.DataFrame(columns=['seed','% sample_size', '# of relations', '# of additional relations skip 2',\"% of additional relations skip 2\"])\n",
    "\n",
    "dataset = \"euadr\"\n",
    "# sample_size = 20\n",
    "for seed in seeds:\n",
    "    print(f\"SEED value: {seed}\")\n",
    "    for sample_size in range(10,101, 10):\n",
    "        kb_ = pd.read_csv(f\"/home/016651544/gemma/samples/{seed}/{dataset}/processed_csvs_skip_2/{sample_size}_sample_val_{dataset}.csv\")\n",
    "        original_sample = pd.read_csv(f\"/home/016651544/gemma/samples/{seed}/{dataset}/{sample_size}_sample_val_{dataset}.csv\")\n",
    "        full_data = pd.read_csv(f\"/home/016651544/gemma/{dataset}_unmasked.csv\")\n",
    "        # Create tuples of entity combinations in both dataframes\n",
    "        original_sample['entity_pair'] = original_sample.apply(lambda row: tuple(sorted([row['ENTITY1_TEXT'].lower(), row['ENTITY2_TEXT'].lower()])), axis=1)\n",
    "        kb_['entity_pair'] = kb_.apply(lambda row: tuple(sorted([row['ent1'].lower(), row['ent2'].lower()])), axis=1)\n",
    "        # Check if the entity pairs in df2 are present in df1\n",
    "        kb_['is_present'] = kb_['entity_pair'].isin(original_sample['entity_pair'])\n",
    "        \n",
    "        # Count the matches\n",
    "        matches_count = kb_['is_present'].sum()\n",
    "        print(f\"Number of matching pairs: {matches_count}\")\n",
    "        # print(original_sample['entity_pair'].shape, kb_['entity_pair'].shape)\n",
    "        percentage_additional = ((kb_['entity_pair'].shape[0] - matches_count) / original_sample['entity_pair'].shape[0]) * 100\n",
    "\n",
    "        results_df = results_df.append({\n",
    "            'seed': seed,\n",
    "           '% sample_size': sample_size,\n",
    "             '# of relations': len(original_sample['entity_pair'].unique()),\n",
    "            '# of additional relations skip 2': kb_['entity_pair'].shape[0],\n",
    "            '% of additional relations skip 2': percentage_additional#(original_sample['entity_pair'].shape[0]+kb_['entity_pair'].shape[0])*100/original_sample['entity_pair'].shape[0]\n",
    "        }, ignore_index=True)\n",
    "\n",
    "\n",
    "results_df[['seed', '% sample_size','# of relations', '# of additional relations skip 2']] = results_df[['seed','% sample_size', '# of relations', '# of additional relations skip 2']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "13c581a6-efb9-4c36-822c-dd15fa62eabf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d6bf5373-bb61-4b07-be0c-bcdf8db104f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_df_2 = results_df.groupby('% sample_size').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "a2b36c82-f38d-4ca7-b6b6-80d2079f3559",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th># of additional relations skip 2</th>\n",
       "      <th>% of additional relations skip 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>29.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>51.333333</td>\n",
       "      <td>72.300469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>118.867925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>161.267606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>330.666667</td>\n",
       "      <td>186.817326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>189.666667</td>\n",
       "      <td>442.666667</td>\n",
       "      <td>207.824726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>216.333333</td>\n",
       "      <td>588.000000</td>\n",
       "      <td>237.096774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>243.666667</td>\n",
       "      <td>789.333333</td>\n",
       "      <td>277.934272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>988.333333</td>\n",
       "      <td>309.822362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>289.333333</td>\n",
       "      <td>1132.666667</td>\n",
       "      <td>319.061033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   % sample_size  # of relations  # of additional relations skip 2  \\\n",
       "0             10       33.333333                         10.333333   \n",
       "1             20       67.000000                         51.333333   \n",
       "2             30       99.000000                        126.000000   \n",
       "3             40      130.000000                        229.000000   \n",
       "4             50      160.000000                        330.666667   \n",
       "5             60      189.666667                        442.666667   \n",
       "6             70      216.333333                        588.000000   \n",
       "7             80      243.666667                        789.333333   \n",
       "8             90      272.000000                        988.333333   \n",
       "9            100      289.333333                       1132.666667   \n",
       "\n",
       "   % of additional relations skip 2  \n",
       "0                         29.523810  \n",
       "1                         72.300469  \n",
       "2                        118.867925  \n",
       "3                        161.267606  \n",
       "4                        186.817326  \n",
       "5                        207.824726  \n",
       "6                        237.096774  \n",
       "7                        277.934272  \n",
       "8                        309.822362  \n",
       "9                        319.061033  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_df_2[['% sample_size','# of relations', '# of additional relations skip 2','% of additional relations skip 2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5da792f9-c8fe-40a7-972b-59e1ff7460c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fc8a594-ce37-433d-8ab9-56741b8f5269",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c2a21fa-0b37-496e-a99c-4c0fce652cda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "282af7db-fd2b-4006-ac4b-658a224a4cd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "832a6e6e-a276-473d-8d06-7c448e192b0b",
   "metadata": {},
   "source": [
    "### SKIP 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6125ad0c-51b6-48ff-af05-011e4797ad0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "6d4a8de5-25a9-43c6-8500-374ed6e1e2f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SEED value: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "SEED value: 42\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "SEED value: 144\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n",
      "Number of matching pairs: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n",
      "/tmp/ipykernel_76037/3995674344.py:25: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  results_df = results_df.append({\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "seeds = [0,42,144]\n",
    "results_df = pd.DataFrame(columns=['seed','% sample_size', '# of relations', '# of additional relations skip 3',\"% of additional relations skip 3\"])\n",
    "\n",
    "dataset = \"euadr\"\n",
    "# sample_size = 20\n",
    "for seed in seeds:\n",
    "    print(f\"SEED value: {seed}\")\n",
    "    for sample_size in range(10,101, 10):\n",
    "        kb_ = pd.read_csv(f\"/home/016651544/gemma/samples/{seed}/{dataset}/processed_csvs_skip_3/{sample_size}_sample_val_{dataset}.csv\")\n",
    "        original_sample = pd.read_csv(f\"/home/016651544/gemma/samples/{seed}/{dataset}/{sample_size}_sample_val_{dataset}.csv\")\n",
    "        full_data = pd.read_csv(f\"/home/016651544/gemma/{dataset}_unmasked.csv\")\n",
    "        # Create tuples of entity combinations in both dataframes\n",
    "        original_sample['entity_pair'] = original_sample.apply(lambda row: tuple(sorted([row['ENTITY1_TEXT'].lower(), row['ENTITY2_TEXT'].lower()])), axis=1)\n",
    "        kb_['entity_pair'] = kb_.apply(lambda row: tuple(sorted([row['ent1'].lower(), row['ent2'].lower()])), axis=1)\n",
    "        # Check if the entity pairs in df2 are present in df1\n",
    "        kb_['is_present'] = kb_['entity_pair'].isin(original_sample['entity_pair'])\n",
    "        \n",
    "        # Count the matches\n",
    "        matches_count = kb_['is_present'].sum()\n",
    "        print(f\"Number of matching pairs: {matches_count}\")\n",
    "        # print(original_sample['entity_pair'].shape, kb_['entity_pair'].shape)\n",
    "        percentage_additional = ((kb_['entity_pair'].shape[0] - matches_count) / original_sample['entity_pair'].shape[0]) * 100\n",
    "\n",
    "        results_df = results_df.append({\n",
    "            'seed': seed,\n",
    "           '% sample_size': sample_size,\n",
    "             '# of relations': len(original_sample['entity_pair'].unique()),\n",
    "            '# of additional relations skip 3': kb_['entity_pair'].shape[0],\n",
    "            '% of additional relations skip 3': percentage_additional#(original_sample['entity_pair'].shape[0]+kb_['entity_pair'].shape[0])*100/original_sample['entity_pair'].shape[0]\n",
    "        }, ignore_index=True)\n",
    "\n",
    "\n",
    "results_df[['seed', '% sample_size','# of relations', '# of additional relations skip 3']] = results_df[['seed','% sample_size', '# of relations', '# of additional relations skip 3']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "a5a38f2b-4493-4998-a969-f6271977ecf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seed</th>\n",
       "      <th>% sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th># of additional relations skip 3</th>\n",
       "      <th>% of additional relations skip 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>13</td>\n",
       "      <td>37.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>69</td>\n",
       "      <td>58</td>\n",
       "      <td>81.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>98</td>\n",
       "      <td>116</td>\n",
       "      <td>109.433962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>128</td>\n",
       "      <td>319</td>\n",
       "      <td>224.647887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>160</td>\n",
       "      <td>458</td>\n",
       "      <td>258.757062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>192</td>\n",
       "      <td>608</td>\n",
       "      <td>285.446009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>219</td>\n",
       "      <td>784</td>\n",
       "      <td>316.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>245</td>\n",
       "      <td>998</td>\n",
       "      <td>351.408451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>273</td>\n",
       "      <td>1222</td>\n",
       "      <td>383.072100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>290</td>\n",
       "      <td>1447</td>\n",
       "      <td>407.605634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>34.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "      <td>81.690141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>102</td>\n",
       "      <td>157</td>\n",
       "      <td>148.113208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>42</td>\n",
       "      <td>40</td>\n",
       "      <td>135</td>\n",
       "      <td>269</td>\n",
       "      <td>189.436620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>42</td>\n",
       "      <td>50</td>\n",
       "      <td>166</td>\n",
       "      <td>444</td>\n",
       "      <td>250.847458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "      <td>60</td>\n",
       "      <td>192</td>\n",
       "      <td>606</td>\n",
       "      <td>284.507042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>42</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>801</td>\n",
       "      <td>322.983871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>42</td>\n",
       "      <td>80</td>\n",
       "      <td>246</td>\n",
       "      <td>1051</td>\n",
       "      <td>370.070423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>42</td>\n",
       "      <td>90</td>\n",
       "      <td>271</td>\n",
       "      <td>1295</td>\n",
       "      <td>405.956113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>289</td>\n",
       "      <td>1511</td>\n",
       "      <td>425.633803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>144</td>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>17.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>144</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>54</td>\n",
       "      <td>76.056338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>144</td>\n",
       "      <td>30</td>\n",
       "      <td>97</td>\n",
       "      <td>165</td>\n",
       "      <td>155.660377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>144</td>\n",
       "      <td>40</td>\n",
       "      <td>127</td>\n",
       "      <td>288</td>\n",
       "      <td>202.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>144</td>\n",
       "      <td>50</td>\n",
       "      <td>154</td>\n",
       "      <td>397</td>\n",
       "      <td>224.293785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>144</td>\n",
       "      <td>60</td>\n",
       "      <td>185</td>\n",
       "      <td>573</td>\n",
       "      <td>269.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>144</td>\n",
       "      <td>70</td>\n",
       "      <td>212</td>\n",
       "      <td>760</td>\n",
       "      <td>306.451613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>144</td>\n",
       "      <td>80</td>\n",
       "      <td>240</td>\n",
       "      <td>1058</td>\n",
       "      <td>372.535211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>144</td>\n",
       "      <td>90</td>\n",
       "      <td>272</td>\n",
       "      <td>1379</td>\n",
       "      <td>432.288401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>144</td>\n",
       "      <td>100</td>\n",
       "      <td>289</td>\n",
       "      <td>1515</td>\n",
       "      <td>426.760563</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    seed  % sample_size  # of relations  # of additional relations skip 3  \\\n",
       "0      0             10              33                                13   \n",
       "1      0             20              69                                58   \n",
       "2      0             30              98                               116   \n",
       "3      0             40             128                               319   \n",
       "4      0             50             160                               458   \n",
       "5      0             60             192                               608   \n",
       "6      0             70             219                               784   \n",
       "7      0             80             245                               998   \n",
       "8      0             90             273                              1222   \n",
       "9      0            100             290                              1447   \n",
       "10    42             10              35                                12   \n",
       "11    42             20              67                                58   \n",
       "12    42             30             102                               157   \n",
       "13    42             40             135                               269   \n",
       "14    42             50             166                               444   \n",
       "15    42             60             192                               606   \n",
       "16    42             70             218                               801   \n",
       "17    42             80             246                              1051   \n",
       "18    42             90             271                              1295   \n",
       "19    42            100             289                              1511   \n",
       "20   144             10              32                                 6   \n",
       "21   144             20              65                                54   \n",
       "22   144             30              97                               165   \n",
       "23   144             40             127                               288   \n",
       "24   144             50             154                               397   \n",
       "25   144             60             185                               573   \n",
       "26   144             70             212                               760   \n",
       "27   144             80             240                              1058   \n",
       "28   144             90             272                              1379   \n",
       "29   144            100             289                              1515   \n",
       "\n",
       "    % of additional relations skip 3  \n",
       "0                          37.142857  \n",
       "1                          81.690141  \n",
       "2                         109.433962  \n",
       "3                         224.647887  \n",
       "4                         258.757062  \n",
       "5                         285.446009  \n",
       "6                         316.129032  \n",
       "7                         351.408451  \n",
       "8                         383.072100  \n",
       "9                         407.605634  \n",
       "10                         34.285714  \n",
       "11                         81.690141  \n",
       "12                        148.113208  \n",
       "13                        189.436620  \n",
       "14                        250.847458  \n",
       "15                        284.507042  \n",
       "16                        322.983871  \n",
       "17                        370.070423  \n",
       "18                        405.956113  \n",
       "19                        425.633803  \n",
       "20                         17.142857  \n",
       "21                         76.056338  \n",
       "22                        155.660377  \n",
       "23                        202.816901  \n",
       "24                        224.293785  \n",
       "25                        269.014085  \n",
       "26                        306.451613  \n",
       "27                        372.535211  \n",
       "28                        432.288401  \n",
       "29                        426.760563  "
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "ea5849ad-902a-495f-b114-f3aa62dd126d",
   "metadata": {},
   "outputs": [],
   "source": [
    "average_df_3 = results_df.groupby('% sample_size').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "283f7b89-d6ff-491f-9dc1-943dbb9dcd31",
   "metadata": {},
   "outputs": [
    {
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       "      <th>% sample_size</th>\n",
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       "      <th>% of additional relations skip 3</th>\n",
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       "      <td>10.333333</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>79.812207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>137.735849</td>\n",
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       "      <th>3</th>\n",
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       "      <td>130.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>205.633803</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>244.632768</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>189.666667</td>\n",
       "      <td>595.666667</td>\n",
       "      <td>279.655712</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>216.333333</td>\n",
       "      <td>781.666667</td>\n",
       "      <td>315.188172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>243.666667</td>\n",
       "      <td>1035.666667</td>\n",
       "      <td>364.671362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>1298.666667</td>\n",
       "      <td>407.105538</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>289.333333</td>\n",
       "      <td>1491.000000</td>\n",
       "      <td>420.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   % sample_size  # of relations  # of additional relations skip 3  \\\n",
       "0             10       33.333333                         10.333333   \n",
       "1             20       67.000000                         56.666667   \n",
       "2             30       99.000000                        146.000000   \n",
       "3             40      130.000000                        292.000000   \n",
       "4             50      160.000000                        433.000000   \n",
       "5             60      189.666667                        595.666667   \n",
       "6             70      216.333333                        781.666667   \n",
       "7             80      243.666667                       1035.666667   \n",
       "8             90      272.000000                       1298.666667   \n",
       "9            100      289.333333                       1491.000000   \n",
       "\n",
       "   % of additional relations skip 3  \n",
       "0                         29.523810  \n",
       "1                         79.812207  \n",
       "2                        137.735849  \n",
       "3                        205.633803  \n",
       "4                        244.632768  \n",
       "5                        279.655712  \n",
       "6                        315.188172  \n",
       "7                        364.671362  \n",
       "8                        407.105538  \n",
       "9                        420.000000  "
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_df_3[['% sample_size','# of relations', '# of additional relations skip 3','% of additional relations skip 3']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "f0cd0c32-eade-4d81-a4d6-858d2fb99c5c",
   "metadata": {},
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       "      <td>177.000000</td>\n",
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       "      <td>436.666667</td>\n",
       "      <td>176.075269</td>\n",
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       "      <td>824.333333</td>\n",
       "      <td>232.206573</td>\n",
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       "   % sample_size  seed  # of relations  # of additional relations skip 1  \\\n",
       "0             10  62.0       33.333333                         10.000000   \n",
       "1             20  62.0       67.000000                         44.333333   \n",
       "2             30  62.0       99.000000                        104.333333   \n",
       "3             40  62.0      130.000000                        177.000000   \n",
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       "7             80  62.0      243.666667                        583.333333   \n",
       "8             90  62.0      272.000000                        729.000000   \n",
       "9            100  62.0      289.333333                        824.333333   \n",
       "\n",
       "   % of additional relations skip 1  \n",
       "0                         28.571429  \n",
       "1                         62.441315  \n",
       "2                         98.427673  \n",
       "3                        124.647887  \n",
       "4                        143.314501  \n",
       "5                        156.964006  \n",
       "6                        176.075269  \n",
       "7                        205.399061  \n",
       "8                        228.526646  \n",
       "9                        232.206573  "
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     "execution_count": 168,
     "metadata": {},
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   "execution_count": 169,
   "id": "11a4e5ca-91a2-4610-b40b-ecf794b7a2bb",
   "metadata": {},
   "outputs": [
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       "      <td>1132.666667</td>\n",
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      "text/plain": [
       "   % sample_size  seed  # of relations  # of additional relations skip 2  \\\n",
       "0             10  62.0       33.333333                         10.333333   \n",
       "1             20  62.0       67.000000                         51.333333   \n",
       "2             30  62.0       99.000000                        126.000000   \n",
       "3             40  62.0      130.000000                        229.000000   \n",
       "4             50  62.0      160.000000                        330.666667   \n",
       "5             60  62.0      189.666667                        442.666667   \n",
       "6             70  62.0      216.333333                        588.000000   \n",
       "7             80  62.0      243.666667                        789.333333   \n",
       "8             90  62.0      272.000000                        988.333333   \n",
       "9            100  62.0      289.333333                       1132.666667   \n",
       "\n",
       "   % of additional relations skip 2  \n",
       "0                         29.523810  \n",
       "1                         72.300469  \n",
       "2                        118.867925  \n",
       "3                        161.267606  \n",
       "4                        186.817326  \n",
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       "6                        237.096774  \n",
       "7                        277.934272  \n",
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   "execution_count": 170,
   "id": "108ae88a-58cc-4fb0-943f-73e43c2ab4c5",
   "metadata": {},
   "outputs": [
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       "      <th>% of additional relations skip 3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>62.0</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>29.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>62.0</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>79.812207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>62.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>137.735849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>62.0</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>205.633803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>62.0</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>244.632768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>62.0</td>\n",
       "      <td>189.666667</td>\n",
       "      <td>595.666667</td>\n",
       "      <td>279.655712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>62.0</td>\n",
       "      <td>216.333333</td>\n",
       "      <td>781.666667</td>\n",
       "      <td>315.188172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>62.0</td>\n",
       "      <td>243.666667</td>\n",
       "      <td>1035.666667</td>\n",
       "      <td>364.671362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>62.0</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>1298.666667</td>\n",
       "      <td>407.105538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>62.0</td>\n",
       "      <td>289.333333</td>\n",
       "      <td>1491.000000</td>\n",
       "      <td>420.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   % sample_size  seed  # of relations  # of additional relations skip 3  \\\n",
       "0             10  62.0       33.333333                         10.333333   \n",
       "1             20  62.0       67.000000                         56.666667   \n",
       "2             30  62.0       99.000000                        146.000000   \n",
       "3             40  62.0      130.000000                        292.000000   \n",
       "4             50  62.0      160.000000                        433.000000   \n",
       "5             60  62.0      189.666667                        595.666667   \n",
       "6             70  62.0      216.333333                        781.666667   \n",
       "7             80  62.0      243.666667                       1035.666667   \n",
       "8             90  62.0      272.000000                       1298.666667   \n",
       "9            100  62.0      289.333333                       1491.000000   \n",
       "\n",
       "   % of additional relations skip 3  \n",
       "0                         29.523810  \n",
       "1                         79.812207  \n",
       "2                        137.735849  \n",
       "3                        205.633803  \n",
       "4                        244.632768  \n",
       "5                        279.655712  \n",
       "6                        315.188172  \n",
       "7                        364.671362  \n",
       "8                        407.105538  \n",
       "9                        420.000000  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_df_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "b9642160-e866-4cdf-bbcf-c810147e0d68",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.merge(average_df_1, average_df_2, on = ['% sample_size','# of relations'], how=\"inner\")\n",
    "df_final = pd.merge(df_final, average_df_3, on = ['% sample_size','# of relations'], how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "238b2ee3-2225-4dfa-9e8f-f0b1a5120a1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(columns = ['seed','seed_x','seed_y'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "73b3e9b0-f764-4c60-8bb4-3fd75cf36bc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th># of additional relations skip 1</th>\n",
       "      <th>% of additional relations skip 1</th>\n",
       "      <th># of additional relations skip 2</th>\n",
       "      <th>% of additional relations skip 2</th>\n",
       "      <th># of additional relations skip 3</th>\n",
       "      <th>% of additional relations skip 3</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>28.571429</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>29.523810</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>29.523810</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>62.441315</td>\n",
       "      <td>51.333333</td>\n",
       "      <td>72.300469</td>\n",
       "      <td>56.666667</td>\n",
       "      <td>79.812207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>104.333333</td>\n",
       "      <td>98.427673</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>118.867925</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>137.735849</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>124.647887</td>\n",
       "      <td>229.000000</td>\n",
       "      <td>161.267606</td>\n",
       "      <td>292.000000</td>\n",
       "      <td>205.633803</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>253.666667</td>\n",
       "      <td>143.314501</td>\n",
       "      <td>330.666667</td>\n",
       "      <td>186.817326</td>\n",
       "      <td>433.000000</td>\n",
       "      <td>244.632768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>189.666667</td>\n",
       "      <td>334.333333</td>\n",
       "      <td>156.964006</td>\n",
       "      <td>442.666667</td>\n",
       "      <td>207.824726</td>\n",
       "      <td>595.666667</td>\n",
       "      <td>279.655712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>216.333333</td>\n",
       "      <td>436.666667</td>\n",
       "      <td>176.075269</td>\n",
       "      <td>588.000000</td>\n",
       "      <td>237.096774</td>\n",
       "      <td>781.666667</td>\n",
       "      <td>315.188172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>243.666667</td>\n",
       "      <td>583.333333</td>\n",
       "      <td>205.399061</td>\n",
       "      <td>789.333333</td>\n",
       "      <td>277.934272</td>\n",
       "      <td>1035.666667</td>\n",
       "      <td>364.671362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>228.526646</td>\n",
       "      <td>988.333333</td>\n",
       "      <td>309.822362</td>\n",
       "      <td>1298.666667</td>\n",
       "      <td>407.105538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>289.333333</td>\n",
       "      <td>824.333333</td>\n",
       "      <td>232.206573</td>\n",
       "      <td>1132.666667</td>\n",
       "      <td>319.061033</td>\n",
       "      <td>1491.000000</td>\n",
       "      <td>420.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   % sample_size  # of relations  # of additional relations skip 1  \\\n",
       "0             10       33.333333                         10.000000   \n",
       "1             20       67.000000                         44.333333   \n",
       "2             30       99.000000                        104.333333   \n",
       "3             40      130.000000                        177.000000   \n",
       "4             50      160.000000                        253.666667   \n",
       "5             60      189.666667                        334.333333   \n",
       "6             70      216.333333                        436.666667   \n",
       "7             80      243.666667                        583.333333   \n",
       "8             90      272.000000                        729.000000   \n",
       "9            100      289.333333                        824.333333   \n",
       "\n",
       "   % of additional relations skip 1  # of additional relations skip 2  \\\n",
       "0                         28.571429                         10.333333   \n",
       "1                         62.441315                         51.333333   \n",
       "2                         98.427673                        126.000000   \n",
       "3                        124.647887                        229.000000   \n",
       "4                        143.314501                        330.666667   \n",
       "5                        156.964006                        442.666667   \n",
       "6                        176.075269                        588.000000   \n",
       "7                        205.399061                        789.333333   \n",
       "8                        228.526646                        988.333333   \n",
       "9                        232.206573                       1132.666667   \n",
       "\n",
       "   % of additional relations skip 2  # of additional relations skip 3  \\\n",
       "0                         29.523810                         10.333333   \n",
       "1                         72.300469                         56.666667   \n",
       "2                        118.867925                        146.000000   \n",
       "3                        161.267606                        292.000000   \n",
       "4                        186.817326                        433.000000   \n",
       "5                        207.824726                        595.666667   \n",
       "6                        237.096774                        781.666667   \n",
       "7                        277.934272                       1035.666667   \n",
       "8                        309.822362                       1298.666667   \n",
       "9                        319.061033                       1491.000000   \n",
       "\n",
       "   % of additional relations skip 3  \n",
       "0                         29.523810  \n",
       "1                         79.812207  \n",
       "2                        137.735849  \n",
       "3                        205.633803  \n",
       "4                        244.632768  \n",
       "5                        279.655712  \n",
       "6                        315.188172  \n",
       "7                        364.671362  \n",
       "8                        407.105538  \n",
       "9                        420.000000  "
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "0b2e558a-5374-4be1-97a7-cc8844097f32",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th># of additional relations skip 1</th>\n",
       "      <th>% of additional relations skip 1</th>\n",
       "      <th># of additional relations skip 2</th>\n",
       "      <th>% of additional relations skip 2</th>\n",
       "      <th># of additional relations skip 3</th>\n",
       "      <th>% of additional relations skip 3</th>\n",
       "      <th>sample_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>33.33</td>\n",
       "      <td>10.00</td>\n",
       "      <td>28.57</td>\n",
       "      <td>10.33</td>\n",
       "      <td>29.52</td>\n",
       "      <td>10.33</td>\n",
       "      <td>29.52</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>67.00</td>\n",
       "      <td>44.33</td>\n",
       "      <td>62.44</td>\n",
       "      <td>51.33</td>\n",
       "      <td>72.30</td>\n",
       "      <td>56.67</td>\n",
       "      <td>79.81</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>99.00</td>\n",
       "      <td>104.33</td>\n",
       "      <td>98.43</td>\n",
       "      <td>126.00</td>\n",
       "      <td>118.87</td>\n",
       "      <td>146.00</td>\n",
       "      <td>137.74</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>130.00</td>\n",
       "      <td>177.00</td>\n",
       "      <td>124.65</td>\n",
       "      <td>229.00</td>\n",
       "      <td>161.27</td>\n",
       "      <td>292.00</td>\n",
       "      <td>205.63</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>160.00</td>\n",
       "      <td>253.67</td>\n",
       "      <td>143.31</td>\n",
       "      <td>330.67</td>\n",
       "      <td>186.82</td>\n",
       "      <td>433.00</td>\n",
       "      <td>244.63</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>60</td>\n",
       "      <td>189.67</td>\n",
       "      <td>334.33</td>\n",
       "      <td>156.96</td>\n",
       "      <td>442.67</td>\n",
       "      <td>207.82</td>\n",
       "      <td>595.67</td>\n",
       "      <td>279.66</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>216.33</td>\n",
       "      <td>436.67</td>\n",
       "      <td>176.08</td>\n",
       "      <td>588.00</td>\n",
       "      <td>237.10</td>\n",
       "      <td>781.67</td>\n",
       "      <td>315.19</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>243.67</td>\n",
       "      <td>583.33</td>\n",
       "      <td>205.40</td>\n",
       "      <td>789.33</td>\n",
       "      <td>277.93</td>\n",
       "      <td>1035.67</td>\n",
       "      <td>364.67</td>\n",
       "      <td>582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>90</td>\n",
       "      <td>272.00</td>\n",
       "      <td>729.00</td>\n",
       "      <td>228.53</td>\n",
       "      <td>988.33</td>\n",
       "      <td>309.82</td>\n",
       "      <td>1298.67</td>\n",
       "      <td>407.11</td>\n",
       "      <td>706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100</td>\n",
       "      <td>289.33</td>\n",
       "      <td>824.33</td>\n",
       "      <td>232.21</td>\n",
       "      <td>1132.67</td>\n",
       "      <td>319.06</td>\n",
       "      <td>1491.00</td>\n",
       "      <td>420.00</td>\n",
       "      <td>798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   % sample_size  # of relations  # of additional relations skip 1  \\\n",
       "0             10           33.33                             10.00   \n",
       "1             20           67.00                             44.33   \n",
       "2             30           99.00                            104.33   \n",
       "3             40          130.00                            177.00   \n",
       "4             50          160.00                            253.67   \n",
       "5             60          189.67                            334.33   \n",
       "6             70          216.33                            436.67   \n",
       "7             80          243.67                            583.33   \n",
       "8             90          272.00                            729.00   \n",
       "9            100          289.33                            824.33   \n",
       "\n",
       "   % of additional relations skip 1  # of additional relations skip 2  \\\n",
       "0                             28.57                             10.33   \n",
       "1                             62.44                             51.33   \n",
       "2                             98.43                            126.00   \n",
       "3                            124.65                            229.00   \n",
       "4                            143.31                            330.67   \n",
       "5                            156.96                            442.67   \n",
       "6                            176.08                            588.00   \n",
       "7                            205.40                            789.33   \n",
       "8                            228.53                            988.33   \n",
       "9                            232.21                           1132.67   \n",
       "\n",
       "   % of additional relations skip 2  # of additional relations skip 3  \\\n",
       "0                             29.52                             10.33   \n",
       "1                             72.30                             56.67   \n",
       "2                            118.87                            146.00   \n",
       "3                            161.27                            292.00   \n",
       "4                            186.82                            433.00   \n",
       "5                            207.82                            595.67   \n",
       "6                            237.10                            781.67   \n",
       "7                            277.93                           1035.67   \n",
       "8                            309.82                           1298.67   \n",
       "9                            319.06                           1491.00   \n",
       "\n",
       "   % of additional relations skip 3  sample_size  \n",
       "0                             29.52           12  \n",
       "1                             79.81           51  \n",
       "2                            137.74           89  \n",
       "3                            205.63          178  \n",
       "4                            244.63          258  \n",
       "5                            279.66          349  \n",
       "6                            315.19          450  \n",
       "7                            364.67          582  \n",
       "8                            407.11          706  \n",
       "9                            420.00          798  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['# of relations','# of additional relations skip 1','# of additional relations skip 2','# of additional relations skip 3',\n",
    "       '% of additional relations skip 1', '% of additional relations skip 2', \n",
    "       '% of additional relations skip 3']\n",
    "df_final[columns] = df_final[columns].round(2)\n",
    "df_final['sample_size'] = [12, 51, 89, 178, 258, 349, 450, 582, 706, 798]\n",
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b0f13ae3-d681-483e-8e02-b922a1fc9269",
   "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>sample_size</th>\n",
       "      <th># of relations</th>\n",
       "      <th>% of additional relations skip 1</th>\n",
       "      <th>% of additional relations skip 2</th>\n",
       "      <th>% of additional relations skip 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>33.33</td>\n",
       "      <td>28.57</td>\n",
       "      <td>29.52</td>\n",
       "      <td>29.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51</td>\n",
       "      <td>67.00</td>\n",
       "      <td>62.44</td>\n",
       "      <td>72.30</td>\n",
       "      <td>79.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89</td>\n",
       "      <td>99.00</td>\n",
       "      <td>98.43</td>\n",
       "      <td>118.87</td>\n",
       "      <td>137.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>178</td>\n",
       "      <td>130.00</td>\n",
       "      <td>124.65</td>\n",
       "      <td>161.27</td>\n",
       "      <td>205.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>258</td>\n",
       "      <td>160.00</td>\n",
       "      <td>143.31</td>\n",
       "      <td>186.82</td>\n",
       "      <td>244.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>349</td>\n",
       "      <td>189.67</td>\n",
       "      <td>156.96</td>\n",
       "      <td>207.82</td>\n",
       "      <td>279.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>450</td>\n",
       "      <td>216.33</td>\n",
       "      <td>176.08</td>\n",
       "      <td>237.10</td>\n",
       "      <td>315.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>582</td>\n",
       "      <td>243.67</td>\n",
       "      <td>205.40</td>\n",
       "      <td>277.93</td>\n",
       "      <td>364.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>706</td>\n",
       "      <td>272.00</td>\n",
       "      <td>228.53</td>\n",
       "      <td>309.82</td>\n",
       "      <td>407.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>798</td>\n",
       "      <td>289.33</td>\n",
       "      <td>232.21</td>\n",
       "      <td>319.06</td>\n",
       "      <td>420.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_size  # of relations  % of additional relations skip 1  \\\n",
       "0           12           33.33                             28.57   \n",
       "1           51           67.00                             62.44   \n",
       "2           89           99.00                             98.43   \n",
       "3          178          130.00                            124.65   \n",
       "4          258          160.00                            143.31   \n",
       "5          349          189.67                            156.96   \n",
       "6          450          216.33                            176.08   \n",
       "7          582          243.67                            205.40   \n",
       "8          706          272.00                            228.53   \n",
       "9          798          289.33                            232.21   \n",
       "\n",
       "   % of additional relations skip 2  % of additional relations skip 3  \n",
       "0                             29.52                             29.52  \n",
       "1                             72.30                             79.81  \n",
       "2                            118.87                            137.74  \n",
       "3                            161.27                            205.63  \n",
       "4                            186.82                            244.63  \n",
       "5                            207.82                            279.66  \n",
       "6                            237.10                            315.19  \n",
       "7                            277.93                            364.67  \n",
       "8                            309.82                            407.11  \n",
       "9                            319.06                            420.00  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols= [ 'sample_size','# of relations','% of additional relations skip 1', '% of additional relations skip 2', \n",
    "       '% of additional relations skip 3']\n",
    "df_final[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d447104b-4bb2-4361-8fe5-4360c6e85c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df_final['sample_size'], df_final['% of additional relations skip 1'], label='2-Hop', marker='o')\n",
    "plt.plot(df_final['sample_size'], df_final['% of additional relations skip 2'], label='3-Hop', marker='o')\n",
    "plt.plot(df_final['sample_size'], df_final['% of additional relations skip 3'], label='4-Hop', marker='o')\n",
    "\n",
    "# Labeling\n",
    "plt.title('Percentage of Additional Relations by Hop Level')\n",
    "plt.xlabel('Sample Size')\n",
    "plt.ylabel('Percentage of Additional Relations')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "aacdb9b2-3a7e-4090-95a1-0a655b3dafb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the dataset you want to filter by\n",
    "selected_dataset = \"GAD\"  # Change to \"EU-ADR\" if needed\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "for model in [\"BioBERT\", \"Gemma-2b\", \"Gemma-7b\", \"LLaMA2-7b\"]:\n",
    "    # Filter data for each model and plot across metrics for the selected dataset\n",
    "    filtered_data = df[df['Dataset'] == selected_dataset]\n",
    "    filtered_data = filtered_data[['Metric', 'Dataset', model]].melt(id_vars=['Metric', 'Dataset'], var_name='Model', value_name='Score')\n",
    "    metric_dataset = filtered_data['Metric'] + \" (\" + filtered_data['Dataset'] + \")\"\n",
    "    plt.plot(metric_dataset, filtered_data['Score'], marker='o', label=model)\n",
    "\n",
    "plt.title(f'Model Performance in {selected_dataset} Dataset Across Metrics')\n",
    "plt.xlabel('Metric (Dataset)')\n",
    "plt.ylabel('Score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title='Model')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "28dd3662-c859-4ce1-8383-16d6735977b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the dataset you want to filter by\n",
    "selected_dataset = \"EU-ADR\"  # Change to \"EU-ADR\" if needed\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(8, 6))\n",
    "for model in [\"BioBERT\", \"Gemma-2b\", \"Gemma-7b\", \"LLaMA2-7b\"]:\n",
    "    # Filter data for each model and plot across metrics for the selected dataset\n",
    "    filtered_data = df[df['Dataset'] == selected_dataset]\n",
    "    filtered_data = filtered_data[['Metric', 'Dataset', model]].melt(id_vars=['Metric', 'Dataset'], var_name='Model', value_name='Score')\n",
    "    metric_dataset = filtered_data['Metric'] + \" (\" + filtered_data['Dataset'] + \")\"\n",
    "    plt.plot(metric_dataset, filtered_data['Score'], marker='o', label=model)\n",
    "\n",
    "plt.title(f'Model Performance in {selected_dataset} Dataset Across Metrics')\n",
    "plt.xlabel('Metric (Dataset)')\n",
    "plt.ylabel('Score')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend(title='Model')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cacdfd3-a16a-4b02-8af9-624b8d89498e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e469527d-1fa7-4bd4-83d3-6fe789af5a40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c04554e-b8e2-4310-9962-c6d89615e22f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7aefd92f-fed8-4156-b629-7d1d04b71877",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ead6deaf-3eea-4798-ae07-7780de4bb542",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "906eade8-b1a4-4bb5-936d-ac44e764b38a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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