z_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/table_dict.pkl', 'rb') as f:\n",
    "    td = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in td.keys():\n",
    "    if (td[k].shape[0] == 0) or (td[k].shape[1] == 0):\n",
    "        print('problems')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "nans = 0\n",
    "nan_tables = []\n",
    "for k in td.keys():\n",
    "    if td[k] == None:\n",
    "        nans += 1\n",
    "        nan_tables.append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "286327"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['471.142163.csv',\n",
       " '349.18982.csv',\n",
       " '155.107247.csv',\n",
       " '219.46729.csv',\n",
       " '629.8417.csv']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_tables[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/csv_tables/train/471.142163.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Paolo Persoglia</td>\n",
       "      <td>Boys' -66 kg</td>\n",
       "      <td>{{flagIOCathlete| Ghazaryan|ARM|2010 Summer Yo...</td>\n",
       "      <td>Repechage\\n{{flagIOCathlete|[[Farshid Ghasemi ...</td>\n",
       "      <td>Did not advance</td>\n",
       "      <td>Did not advance</td>\n",
       "      <td>Did not advance</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0             1  \\\n",
       "0  Paolo Persoglia  Boys' -66 kg   \n",
       "\n",
       "                                                   2  \\\n",
       "0  {{flagIOCathlete| Ghazaryan|ARM|2010 Summer Yo...   \n",
       "\n",
       "                                                   3                4  \\\n",
       "0  Repechage\\n{{flagIOCathlete|[[Farshid Ghasemi ...  Did not advance   \n",
       "\n",
       "                 5                6   7  \n",
       "0  Did not advance  Did not advance  17  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/csv_tables/train/349.18982.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155.39558</td>\n",
       "      <td>437.71460</td>\n",
       "      <td>34</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.001394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.80474</td>\n",
       "      <td>53.41837</td>\n",
       "      <td>6</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>331.55837</td>\n",
       "      <td>497.29414</td>\n",
       "      <td>50</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.870237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.96752</td>\n",
       "      <td>230.72970</td>\n",
       "      <td>40</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.794872</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>3.025835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>237.29154</td>\n",
       "      <td>603.25921</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        r_id       s_id  seeds      jsim  jsim_time     josie  josie_time  \\\n",
       "0  155.39558  437.71460     34  0.400000   0.000015  0.608696    0.000006   \n",
       "1  323.80474   53.41837      6  0.583333   0.000014  0.736842    0.000006   \n",
       "2  331.55837  497.29414     50  0.592593   0.000015  0.761905    0.000007   \n",
       "3  124.96752  230.72970     40  0.516667   0.000007  0.794872    0.000003   \n",
       "4  237.29154  603.25921     15  1.000000   0.000002  1.000000    0.000001   \n",
       "\n",
       "         a%  total_time  \n",
       "0  0.625000    0.001394  \n",
       "1  0.363636    0.000401  \n",
       "2  0.277778    0.870237  \n",
       "3  0.187500    3.025835  \n",
       "4  1.000000    0.000336  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['330.92197.csv',\n",
       " '419.54000.csv',\n",
       " '188.52114.csv',\n",
       " '208.413.csv',\n",
       " '486.86352.csv']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(td.keys())[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<graph.Graph at 0x7fbbacdae250>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td['208.413.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['330.92197.csv',\n",
       " '419.54000.csv',\n",
       " '188.52114.csv',\n",
       " '208.413.csv',\n",
       " '486.86352.csv',\n",
       " '487.18323.csv',\n",
       " '590.30067.csv',\n",
       " '578.90909.csv',\n",
       " '497.14774.csv',\n",
       " '434.126782.csv']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(td.keys())[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "def rename_labels(df: pd.DataFrame, names_set: set):\n",
    "    r_ids = []\n",
    "    s_ids = []\n",
    "    for r in tqdm(range(df.shape[0])):\n",
    "        r_id = str(df.iloc[r]['r_id'])\n",
    "        while r_id+'.csv' not in names_set:\n",
    "            r_id+='0'\n",
    "        r_ids.append(r_id + '.csv')\n",
    "\n",
    "        s_id = str(df.iloc[r]['s_id'])\n",
    "        while s_id+'.csv' not in names_set:\n",
    "            s_id+='0'\n",
    "        s_ids.append(s_id + '.csv')\n",
    "    df['r_id'] = r_ids\n",
    "    df['s_id'] = s_ids\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500000/500000 [00:36<00:00, 13555.60it/s]\n"
     ]
    }
   ],
   "source": [
    "train = rename_labels(pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/train_test_valid_wrong_names/train.csv'), set(td.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60000/60000 [00:04<00:00, 13679.46it/s]\n"
     ]
    }
   ],
   "source": [
    "test = rename_labels(pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/train_test_valid_wrong_names/test.csv'), set(td.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60000/60000 [00:04<00:00, 13591.40it/s]\n"
     ]
    }
   ],
   "source": [
    "valid = rename_labels(pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/train_test_valid_wrong_names/valid.csv'), set(td.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/train.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.to_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/test.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid.to_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/valid.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155.39558.csv</td>\n",
       "      <td>437.7146.csv</td>\n",
       "      <td>34</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>5.722046e-06</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.001394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>323.80474.csv</td>\n",
       "      <td>53.41837.csv</td>\n",
       "      <td>6</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>6.198883e-06</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>331.55837.csv</td>\n",
       "      <td>497.29414.csv</td>\n",
       "      <td>50</td>\n",
       "      <td>0.592593</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>6.675720e-06</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.870237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.96752.csv</td>\n",
       "      <td>230.7297.csv</td>\n",
       "      <td>40</td>\n",
       "      <td>0.516667</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.794872</td>\n",
       "      <td>2.861023e-06</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>3.025835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>237.29154.csv</td>\n",
       "      <td>603.25921.csv</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499995</th>\n",
       "      <td>425.77964.csv</td>\n",
       "      <td>426.139959.csv</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499996</th>\n",
       "      <td>142.36784.csv</td>\n",
       "      <td>426.63559.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>7.152557e-07</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499997</th>\n",
       "      <td>474.8903.csv</td>\n",
       "      <td>485.23755.csv</td>\n",
       "      <td>25</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>2.145767e-06</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.000570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499998</th>\n",
       "      <td>384.32709.csv</td>\n",
       "      <td>501.26246.csv</td>\n",
       "      <td>11</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499999</th>\n",
       "      <td>408.11004.csv</td>\n",
       "      <td>86.33626.csv</td>\n",
       "      <td>29</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.430511e-06</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.003572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 r_id            s_id  seeds      jsim  jsim_time     josie  \\\n",
       "0       155.39558.csv    437.7146.csv     34  0.400000   0.000015  0.608696   \n",
       "1       323.80474.csv    53.41837.csv      6  0.583333   0.000014  0.736842   \n",
       "2       331.55837.csv   497.29414.csv     50  0.592593   0.000015  0.761905   \n",
       "3       124.96752.csv    230.7297.csv     40  0.516667   0.000007  0.794872   \n",
       "4       237.29154.csv   603.25921.csv     15  1.000000   0.000002  1.000000   \n",
       "...               ...             ...    ...       ...        ...       ...   \n",
       "499995  425.77964.csv  426.139959.csv     13  1.000000   0.000003  1.000000   \n",
       "499996  142.36784.csv   426.63559.csv      4  0.333333   0.000003  0.727273   \n",
       "499997   474.8903.csv   485.23755.csv     25  0.920000   0.000005  0.958333   \n",
       "499998  384.32709.csv   501.26246.csv     11  0.846154   0.000003  1.000000   \n",
       "499999  408.11004.csv    86.33626.csv     29  0.333333   0.000004  0.500000   \n",
       "\n",
       "          josie_time        a%  total_time  \n",
       "0       5.722046e-06  0.625000    0.001394  \n",
       "1       6.198883e-06  0.363636    0.000401  \n",
       "2       6.675720e-06  0.277778    0.870237  \n",
       "3       2.861023e-06  0.187500    3.025835  \n",
       "4       1.192093e-06  1.000000    0.000336  \n",
       "...              ...       ...         ...  \n",
       "499995  9.536743e-07  1.000000    0.000534  \n",
       "499996  7.152557e-07  0.400000    0.000235  \n",
       "499997  2.145767e-06  0.909091    0.000570  \n",
       "499998  9.536743e-07  1.000000    0.000530  \n",
       "499999  1.430511e-06  0.166667    0.003572  \n",
       "\n",
       "[500000 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<graph.Graph at 0x7fbaddcfb3d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td['437.7146.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_old = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>jsim_time</th>\n",
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       "      <td>0.363636</td>\n",
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       "      <td>0.187500</td>\n",
       "      <td>3.025835</td>\n",
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       "      <td>426.139959</td>\n",
       "      <td>13</td>\n",
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       "      <td>9.536743e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000534</td>\n",
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       "      <th>499996</th>\n",
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       "      <td>474.89030</td>\n",
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       "             r_id        s_id  seeds      jsim  jsim_time     josie  \\\n",
       "0       155.39558  437.714600     34  0.400000   0.000015  0.608696   \n",
       "1       323.80474   53.418370      6  0.583333   0.000014  0.736842   \n",
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       "...           ...         ...    ...       ...        ...       ...   \n",
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       "499999  408.11004   86.336260     29  0.333333   0.000004  0.500000   \n",
       "\n",
       "          josie_time        a%  total_time  \n",
       "0       5.722046e-06  0.625000    0.001394  \n",
       "1       6.198883e-06  0.363636    0.000401  \n",
       "2       6.675720e-06  0.277778    0.870237  \n",
       "3       2.861023e-06  0.187500    3.025835  \n",
       "4       1.192093e-06  1.000000    0.000336  \n",
       "...              ...       ...         ...  \n",
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       "499996  7.152557e-07  0.400000    0.000235  \n",
       "499997  2.145767e-06  0.909091    0.000570  \n",
       "499998  9.536743e-07  1.000000    0.000530  \n",
       "499999  1.430511e-06  0.166667    0.003572  \n",
       "\n",
       "[500000 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: <bound method IPythonKernel._clean_thread_parent_frames of <ipykernel.ipkernel.IPythonKernel object at 0x7fbca72d1650>>\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/francesco.pugnaloni/miniconda3/envs/GNNTE/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 770, in _clean_thread_parent_frames\n",
      "    def _clean_thread_parent_frames(\n",
      "\n",
      "KeyboardInterrupt: \n"
     ]
    }
   ],
   "source": [
    "with open('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/graph_dict.pkl', 'rb') as f:\n",
    "    gd = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 60000/60000 [00:04<00:00, 14502.15it/s]\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/valid.csv')\n",
    "for r in tqdm(range(train.shape[0])):\n",
    "    try:\n",
    "        td[train.iloc[r]['r_id']]\n",
    "        td[train.iloc[r]['s_id']]\n",
    "    except:\n",
    "        print(f'problems in row {r}')\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>369.25277.csv</td>\n",
       "      <td>386.124131.csv</td>\n",
       "      <td>11</td>\n",
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       "      <td>0.000004</td>\n",
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       "      <td>0.846154</td>\n",
       "      <td>0.000648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101.33062.csv</td>\n",
       "      <td>366.28041.csv</td>\n",
       "      <td>10</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>2.145767e-06</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>480.45556.csv</td>\n",
       "      <td>485.67349.csv</td>\n",
       "      <td>8</td>\n",
       "      <td>0.693431</td>\n",
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       "      <td>6.675720e-06</td>\n",
       "      <td>0.871795</td>\n",
       "      <td>0.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>416.21973.csv</td>\n",
       "      <td>476.36749.csv</td>\n",
       "      <td>36</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150.21534.csv</td>\n",
       "      <td>192.45177.csv</td>\n",
       "      <td>6</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>1.907349e-06</td>\n",
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       "      <td>0.000339</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>422.159895.csv</td>\n",
       "      <td>426.123759.csv</td>\n",
       "      <td>11</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>429.136565.csv</td>\n",
       "      <td>429.145896.csv</td>\n",
       "      <td>3</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>2.145767e-06</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>497.106001.csv</td>\n",
       "      <td>498.68459.csv</td>\n",
       "      <td>32</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.861023e-06</td>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.001431</td>\n",
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       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>489.16571.csv</td>\n",
       "      <td>492.131694.csv</td>\n",
       "      <td>23</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.384186e-06</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.001912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>345.125102.csv</td>\n",
       "      <td>405.84760.csv</td>\n",
       "      <td>39</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>1.907349e-06</td>\n",
       "      <td>0.177778</td>\n",
       "      <td>0.618880</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "                 r_id            s_id  seeds      jsim  jsim_time     josie  \\\n",
       "0       369.25277.csv  386.124131.csv     11  0.647059   0.000004  0.846154   \n",
       "1       101.33062.csv   366.28041.csv     10  0.538462   0.000005  0.700000   \n",
       "2       480.45556.csv   485.67349.csv      8  0.693431   0.000018  0.950000   \n",
       "3       416.21973.csv   476.36749.csv     36  0.600000   0.000002  0.750000   \n",
       "4       150.21534.csv   192.45177.csv      6  0.727273   0.000005  0.842105   \n",
       "...               ...             ...    ...       ...        ...       ...   \n",
       "59995  422.159895.csv  426.123759.csv     11  0.846154   0.000002  1.000000   \n",
       "59996  429.136565.csv  429.145896.csv      3  0.769231   0.000003  0.909091   \n",
       "59997  497.106001.csv   498.68459.csv     32  1.000000   0.000008  1.000000   \n",
       "59998   489.16571.csv  492.131694.csv     23  0.975000   0.000006  1.000000   \n",
       "59999  345.125102.csv   405.84760.csv     39  0.500000   0.000005  0.680000   \n",
       "\n",
       "         josie_time        a%  total_time  \n",
       "0      1.430511e-06  0.846154    0.000648  \n",
       "1      2.145767e-06  0.500000    0.000441  \n",
       "2      6.675720e-06  0.871795    0.000860  \n",
       "3      9.536743e-07  0.666667    0.000318  \n",
       "4      1.907349e-06  0.484848    0.000339  \n",
       "...             ...       ...         ...  \n",
       "59995  9.536743e-07  1.000000    0.000457  \n",
       "59996  2.145767e-06  0.666667    0.000240  \n",
       "59997  2.861023e-06  0.476190    0.001431  \n",
       "59998  2.384186e-06  0.380952    0.001912  \n",
       "59999  1.907349e-06  0.177778    0.618880  \n",
       "\n",
       "[60000 rows x 9 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/valid.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "d = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.35588.csv</td>\n",
       "      <td>574.6628.csv</td>\n",
       "      <td>70</td>\n",
       "      <td>0.439716</td>\n",
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       "      <td>0.639175</td>\n",
       "      <td>0.000004</td>\n",
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       "      <td>3.087128</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225.64426.csv</td>\n",
       "      <td>401.116864.csv</td>\n",
       "      <td>17</td>\n",
       "      <td>0.692308</td>\n",
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       "      <td>0.000005</td>\n",
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       "    <tr>\n",
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       "      <td>0.000003</td>\n",
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       "      <td>11.562310</td>\n",
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       "      <th>59996</th>\n",
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       "                 r_id            s_id  seeds      jsim  jsim_time     josie  \\\n",
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       "59999  422.109201.csv  422.141642.csv     21  0.359551   0.000009  0.581818   \n",
       "\n",
       "       josie_time        a%  total_time  \n",
       "0        0.000001  0.215385    3.205166  \n",
       "1        0.000001  0.250000    0.000202  \n",
       "2        0.000004  0.105769    3.087128  \n",
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       "...           ...       ...         ...  \n",
       "59995    0.000003  0.058065   11.562310  \n",
       "59996    0.000005  0.200000    3.126973  \n",
       "59997    0.000002  0.333333    0.000358  \n",
       "59998    0.000002  0.400000    0.000246  \n",
       "59999    0.000003  0.111111    0.005207  \n",
       "\n",
       "[60000 rows x 9 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>half_life_csv_licensed.zip_2019-GOAT-HiConsump...</td>\n",
       "      <td>id_csv_licensed.zip_1629725_3.csv</td>\n",
       "      <td>0.160000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>half_life_csv_licensed.zip_13-14_244.csv</td>\n",
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       "      <td>0.818182</td>\n",
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       "      <td>id_csv_licensed.zip_31701.csv</td>\n",
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       "    <tr>\n",
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       "      <td>object_csv_licensed.zip_TotalResultsofRhino.v1...</td>\n",
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       "      <td>object_csv_licensed.zip_funcs_by_name_161.csv</td>\n",
       "      <td>then_csv_licensed.zip_17-18_431.csv</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <th>499999</th>\n",
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       "      <td>real_time_csv_licensed.zip_0131_203241_Initial...</td>\n",
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       "<p>500000 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "                                                     r_id  \\\n",
       "0       half_life_csv_licensed.zip_2019-GOAT-HiConsump...   \n",
       "1                half_life_csv_licensed.zip_13-14_244.csv   \n",
       "2       in_time_csv_licensed.zip_0225_174842_Initializ...   \n",
       "3                           id_csv_licensed.zip_28920.csv   \n",
       "4                   id_csv_licensed.zip_fm_policytc_8.csv   \n",
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       "499995        show_time_csv_licensed.zip_1987-02-28_5.csv   \n",
       "499996     lead_time_csv_licensed.zip_2017恋练有词考研词组背多分.csv   \n",
       "499997      object_csv_licensed.zip_funcs_by_name_161.csv   \n",
       "499998  speed_of_light_csv_licensed.zip_funcs_by_addre...   \n",
       "499999  real_time_csv_licensed.zip_PREDICTION_RealTime...   \n",
       "\n",
       "                                                     s_id        a%  \n",
       "0                       id_csv_licensed.zip_1629725_3.csv  0.160000  \n",
       "1             running_time_csv_licensed.zip_13-14_429.csv  0.818182  \n",
       "2       real_time_csv_licensed.zip_0203_201327_Initial...  0.500000  \n",
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       "499995    real_time_csv_licensed.zip_Inception-v2_203.csv  0.000000  \n",
       "499996  object_csv_licensed.zip_TotalResultsofRhino.v1...  0.000000  \n",
       "499997                then_csv_licensed.zip_17-18_431.csv  0.000000  \n",
       "499998  inflation_rate_csv_licensed.zip_011-DA_User_ba...  0.000000  \n",
       "499999  real_time_csv_licensed.zip_0131_203241_Initial...  0.000000  \n",
       "\n",
       "[500000 rows x 3 columns]"
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     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>309.87528.csv</td>\n",
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       "      <td>392.26789.csv</td>\n",
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       "</table>\n",
       "<p>60000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 r_id            s_id        a%\n",
       "0        198.7650.csv   198.97756.csv  0.215385\n",
       "1       309.87528.csv   309.87529.csv  0.250000\n",
       "2       124.35588.csv    574.6628.csv  0.105769\n",
       "3       225.64426.csv  401.116864.csv  0.800000\n",
       "4      441.155903.csv   618.72349.csv  0.281250\n",
       "...               ...             ...       ...\n",
       "59995   245.13162.csv   377.69517.csv  0.058065\n",
       "59996   392.26789.csv  447.114168.csv  0.200000\n",
       "59997     382.160.csv   440.84900.csv  0.333333\n",
       "59998   322.68550.csv  488.146192.csv  0.400000\n",
       "59999  422.109201.csv  422.141642.csv  0.111111\n",
       "\n",
       "[60000 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[['r_id','s_id','a%']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dd.iloc[0]['a%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d.iloc[0]['seeds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "r_id          124.96752.csv\n",
       "s_id           230.7297.csv\n",
       "seeds                    40\n",
       "jsim               0.516667\n",
       "jsim_time          0.000007\n",
       "josie              0.794872\n",
       "josie_time         0.000003\n",
       "a%                   0.1875\n",
       "total_time         3.025835\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<graph.Graph at 0x7fbb856bb410>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "td['230.72970.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230.7297"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "230.72970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>a%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGS2BRH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGTN98H.csv</td>\n",
       "      <td>0.473684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>in_time_csv_licensed.zip_0203_002257_Initializ...</td>\n",
       "      <td>in_time_csv_licensed.zip_0212_004622_Initializ...</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_csv_licensed.zip_01_language_5.csv</td>\n",
       "      <td>id_csv_licensed.zip_Hospitalization22MR_BA_gri...</td>\n",
       "      <td>0.314103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>in_time_csv_licensed.zip_0224_015304_Initializ...</td>\n",
       "      <td>real_time_csv_licensed.zip_0210_194429_Initial...</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>object_csv_licensed.zip_Configuration3_window2...</td>\n",
       "      <td>object_csv_licensed.zip_Configuration3_window8...</td>\n",
       "      <td>0.367868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>id_csv_licensed.zip_10.1038_ncomms8482.csv</td>\n",
       "      <td>parent_csv_licensed.zip_2017-12-12_2.csv</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>rate_of_return_csv_licensed.zip_messages_pt_25...</td>\n",
       "      <td>object_csv_licensed.zip_31f.0.testMap.csv</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>id_csv_licensed.zip_es-MX_13.csv</td>\n",
       "      <td>safe_period_csv_licensed.zip_funcs_by_name_97.csv</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>whole_csv_licensed.zip_01-02_274.csv</td>\n",
       "      <td>in_time_csv_licensed.zip_0203_110150_Initializ...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>parent_csv_licensed.zip_13-14_780.csv</td>\n",
       "      <td>parent_csv_licensed.zip_production_workCenter_...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    r_id  \\\n",
       "0          seek_time_csv_licensed.zip_PL1331LAGS2BRH.csv   \n",
       "1      in_time_csv_licensed.zip_0203_002257_Initializ...   \n",
       "2                  id_csv_licensed.zip_01_language_5.csv   \n",
       "3      in_time_csv_licensed.zip_0224_015304_Initializ...   \n",
       "4      object_csv_licensed.zip_Configuration3_window2...   \n",
       "...                                                  ...   \n",
       "99995         id_csv_licensed.zip_10.1038_ncomms8482.csv   \n",
       "99996  rate_of_return_csv_licensed.zip_messages_pt_25...   \n",
       "99997                   id_csv_licensed.zip_es-MX_13.csv   \n",
       "99998               whole_csv_licensed.zip_01-02_274.csv   \n",
       "99999              parent_csv_licensed.zip_13-14_780.csv   \n",
       "\n",
       "                                                    s_id        a%  \n",
       "0          seek_time_csv_licensed.zip_PL1331LAGTN98H.csv  0.473684  \n",
       "1      in_time_csv_licensed.zip_0212_004622_Initializ...  0.500000  \n",
       "2      id_csv_licensed.zip_Hospitalization22MR_BA_gri...  0.314103  \n",
       "3      real_time_csv_licensed.zip_0210_194429_Initial...  0.500000  \n",
       "4      object_csv_licensed.zip_Configuration3_window8...  0.367868  \n",
       "...                                                  ...       ...  \n",
       "99995           parent_csv_licensed.zip_2017-12-12_2.csv  0.000000  \n",
       "99996          object_csv_licensed.zip_31f.0.testMap.csv  0.000000  \n",
       "99997  safe_period_csv_licensed.zip_funcs_by_name_97.csv  0.000000  \n",
       "99998  in_time_csv_licensed.zip_0203_110150_Initializ...  0.000000  \n",
       "99999  parent_csv_licensed.zip_production_workCenter_...  0.000000  \n",
       "\n",
       "[100000 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "git = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/evaluation_test_last_with_sloth_times_embeddings_retested.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>a%</th>\n",
       "      <th>sloth_time</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>os_sim</th>\n",
       "      <th>os_sim_time</th>\n",
       "      <th>AE_jsim</th>\n",
       "      <th>AE_os_sim</th>\n",
       "      <th>graphs_generation_time</th>\n",
       "      <th>embeddings_generation_time</th>\n",
       "      <th>cos_sim_time_armadillo</th>\n",
       "      <th>armadillo_total_time</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>AE_armadillo</th>\n",
       "      <th>overlap_computations_repeated_armadillo</th>\n",
       "      <th>overlap_computations_no_read_armadillo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGS2BRH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGTN98H.csv</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>0.030304</td>\n",
       "      <td>0.238009</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.462185</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.235675</td>\n",
       "      <td>0.011499</td>\n",
       "      <td>0.777510</td>\n",
       "      <td>0.165847</td>\n",
       "      <td>0.051219</td>\n",
       "      <td>0.994586</td>\n",
       "      <td>0.706485</td>\n",
       "      <td>0.232801</td>\n",
       "      <td>0.019754</td>\n",
       "      <td>0.018656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>in_time_csv_licensed.zip_0203_002257_Initializ...</td>\n",
       "      <td>in_time_csv_licensed.zip_0212_004622_Initializ...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.502674</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.671429</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.002674</td>\n",
       "      <td>0.171429</td>\n",
       "      <td>0.020989</td>\n",
       "      <td>0.003316</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.024487</td>\n",
       "      <td>0.565258</td>\n",
       "      <td>0.065258</td>\n",
       "      <td>0.001265</td>\n",
       "      <td>0.001041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id_csv_licensed.zip_01_language_5.csv</td>\n",
       "      <td>id_csv_licensed.zip_Hospitalization22MR_BA_gri...</td>\n",
       "      <td>0.314103</td>\n",
       "      <td>0.000845</td>\n",
       "      <td>0.175627</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.138475</td>\n",
       "      <td>0.152564</td>\n",
       "      <td>0.028612</td>\n",
       "      <td>0.002071</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.030846</td>\n",
       "      <td>0.390899</td>\n",
       "      <td>0.076797</td>\n",
       "      <td>0.001065</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>in_time_csv_licensed.zip_0224_015304_Initializ...</td>\n",
       "      <td>real_time_csv_licensed.zip_0210_194429_Initial...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.647887</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.030612</td>\n",
       "      <td>0.147887</td>\n",
       "      <td>0.020275</td>\n",
       "      <td>0.001904</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.022332</td>\n",
       "      <td>0.578556</td>\n",
       "      <td>0.078556</td>\n",
       "      <td>0.001097</td>\n",
       "      <td>0.001001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>object_csv_licensed.zip_Configuration3_window2...</td>\n",
       "      <td>object_csv_licensed.zip_Configuration3_window8...</td>\n",
       "      <td>0.367868</td>\n",
       "      <td>19.989065</td>\n",
       "      <td>0.477702</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.699749</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.109834</td>\n",
       "      <td>0.331881</td>\n",
       "      <td>0.311355</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>0.000183</td>\n",
       "      <td>0.314563</td>\n",
       "      <td>0.267510</td>\n",
       "      <td>0.100357</td>\n",
       "      <td>0.001072</td>\n",
       "      <td>0.000974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                r_id  \\\n",
       "0      seek_time_csv_licensed.zip_PL1331LAGS2BRH.csv   \n",
       "1  in_time_csv_licensed.zip_0203_002257_Initializ...   \n",
       "2              id_csv_licensed.zip_01_language_5.csv   \n",
       "3  in_time_csv_licensed.zip_0224_015304_Initializ...   \n",
       "4  object_csv_licensed.zip_Configuration3_window2...   \n",
       "\n",
       "                                                s_id        a%  sloth_time  \\\n",
       "0      seek_time_csv_licensed.zip_PL1331LAGTN98H.csv  0.473684    0.030304   \n",
       "1  in_time_csv_licensed.zip_0212_004622_Initializ...  0.500000    0.000382   \n",
       "2  id_csv_licensed.zip_Hospitalization22MR_BA_gri...  0.314103    0.000845   \n",
       "3  real_time_csv_licensed.zip_0210_194429_Initial...  0.500000    0.000364   \n",
       "4  object_csv_licensed.zip_Configuration3_window8...  0.367868   19.989065   \n",
       "\n",
       "       jsim  jsim_time    os_sim  os_sim_time   AE_jsim  AE_os_sim  \\\n",
       "0  0.238009   0.000201  0.462185     0.000061  0.235675   0.011499   \n",
       "1  0.502674   0.000017  0.671429     0.000007  0.002674   0.171429   \n",
       "2  0.175627   0.000015  0.466667     0.000004  0.138475   0.152564   \n",
       "3  0.469388   0.000016  0.647887     0.000006  0.030612   0.147887   \n",
       "4  0.477702   0.000113  0.699749     0.000044  0.109834   0.331881   \n",
       "\n",
       "   graphs_generation_time  embeddings_generation_time  cos_sim_time_armadillo  \\\n",
       "0                0.777510                    0.165847                0.051219   \n",
       "1                0.020989                    0.003316                0.000179   \n",
       "2                0.028612                    0.002071                0.000159   \n",
       "3                0.020275                    0.001904                0.000149   \n",
       "4                0.311355                    0.003020                0.000183   \n",
       "\n",
       "   armadillo_total_time  armadillo  AE_armadillo  \\\n",
       "0              0.994586   0.706485      0.232801   \n",
       "1              0.024487   0.565258      0.065258   \n",
       "2              0.030846   0.390899      0.076797   \n",
       "3              0.022332   0.578556      0.078556   \n",
       "4              0.314563   0.267510      0.100357   \n",
       "\n",
       "   overlap_computations_repeated_armadillo  \\\n",
       "0                                 0.019754   \n",
       "1                                 0.001265   \n",
       "2                                 0.001065   \n",
       "3                                 0.001097   \n",
       "4                                 0.001072   \n",
       "\n",
       "   overlap_computations_no_read_armadillo  \n",
       "0                                0.018656  \n",
       "1                                0.001041  \n",
       "2                                0.000969  \n",
       "3                                0.001001  \n",
       "4                                0.000974  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "git.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_dict_796970_good.pkl', 'rb') as f:\n",
    "    td_git = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "git = git[git['jsim']>=0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1484, 18)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "git.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "git = git[git['os_sim']>=0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "git = git[git['a%']<=0.4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "good = []\n",
    "for r in range(git.shape[0]):\n",
    "    t1 = td_git[git.iloc[r]['r_id']]\n",
    "    t2 = td_git[git.iloc[r]['s_id']]\n",
    "    if ((t1.shape[0]*t1.shape[0]) + (t2.shape[0]*t2.shape[1])) < 100:\n",
    "        good.append(r)\n",
    "    print((t1.shape[0]*t1.shape[0]) + (t2.shape[0]*t2.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/francesco.pugnaloni/miniconda3/envs/GNNTE/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/graph_dict.pkl', 'rb') as f:\n",
    "    gd = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in gd.keys():\n",
    "    if gd[k] == None:\n",
    "        print('arg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = list(gd.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'330.92197.csv'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[10,  4, 10,  0, 11,  4, 11,  1, 12,  4, 12,  2, 13,  4, 13,  3, 14,  5,\n",
       "         14,  0, 15,  5, 15,  1, 16,  5, 16,  2, 17,  5, 17,  3, 18,  6, 18,  0,\n",
       "         19,  6, 19,  1, 20,  6, 20,  2, 21,  6, 21,  3, 22,  7, 22,  0, 23,  7,\n",
       "         23,  1, 24,  7, 24,  2, 25,  7, 25,  3, 26,  8, 26,  0, 27,  8, 27,  1,\n",
       "         28,  8, 28,  2, 29,  8, 29,  3, 30,  9, 30,  0, 31,  9, 31,  1, 32,  9,\n",
       "         32,  2, 33,  9, 33,  3],\n",
       "        [ 4, 10,  0, 10,  4, 11,  1, 11,  4, 12,  2, 12,  4, 13,  3, 13,  5, 14,\n",
       "          0, 14,  5, 15,  1, 15,  5, 16,  2, 16,  5, 17,  3, 17,  6, 18,  0, 18,\n",
       "          6, 19,  1, 19,  6, 20,  2, 20,  6, 21,  3, 21,  7, 22,  0, 22,  7, 23,\n",
       "          1, 23,  7, 24,  2, 24,  7, 25,  3, 25,  8, 26,  0, 26,  8, 27,  1, 27,\n",
       "          8, 28,  2, 28,  8, 29,  3, 29,  9, 30,  0, 30,  9, 31,  1, 31,  9, 32,\n",
       "          2, 32,  9, 33,  3, 33]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd['330.92197.csv'].edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>overlap_armadillo</th>\n",
       "      <th>AE_armadillo</th>\n",
       "      <th>AE_jsim</th>\n",
       "      <th>AE_oset_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.941967</td>\n",
       "      <td>0.647238</td>\n",
       "      <td>5.192626e-06</td>\n",
       "      <td>0.820302</td>\n",
       "      <td>1.992679e-06</td>\n",
       "      <td>0.496343</td>\n",
       "      <td>0.914817</td>\n",
       "      <td>0.503128</td>\n",
       "      <td>0.070008</td>\n",
       "      <td>0.219903</td>\n",
       "      <td>0.325091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>160.590663</td>\n",
       "      <td>0.203651</td>\n",
       "      <td>3.881562e-06</td>\n",
       "      <td>0.143001</td>\n",
       "      <td>1.404466e-06</td>\n",
       "      <td>0.291760</td>\n",
       "      <td>2.065447</td>\n",
       "      <td>0.299269</td>\n",
       "      <td>0.091184</td>\n",
       "      <td>0.192537</td>\n",
       "      <td>0.252332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>0.189655</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>3.099442e-06</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.251781</td>\n",
       "      <td>0.000718</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>4.291534e-06</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>1.668930e-06</td>\n",
       "      <td>0.498705</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.487401</td>\n",
       "      <td>0.040061</td>\n",
       "      <td>0.169312</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>5.960464e-06</td>\n",
       "      <td>0.945205</td>\n",
       "      <td>2.384186e-06</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.174530</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.102252</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1428.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.418591e-04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.029274e-05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.229370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.984375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              seeds          jsim     jsim_time         josie    josie_time  \\\n",
       "count  60000.000000  60000.000000  6.000000e+04  60000.000000  6.000000e+04   \n",
       "mean      67.941967      0.647238  5.192626e-06      0.820302  1.992679e-06   \n",
       "std      160.590663      0.203651  3.881562e-06      0.143001  1.404466e-06   \n",
       "min        1.000000      0.068627  2.384186e-07      0.189655  4.768372e-07   \n",
       "25%        9.000000      0.488372  3.099442e-06      0.720000  1.192093e-06   \n",
       "50%       22.000000      0.634615  4.291534e-06      0.842105  1.668930e-06   \n",
       "75%       41.000000      0.794118  5.960464e-06      0.945205  2.384186e-06   \n",
       "max     1428.000000      1.000000  1.418591e-04      1.000000  4.029274e-05   \n",
       "\n",
       "                 a%    total_time  overlap_armadillo  AE_armadillo  \\\n",
       "count  60000.000000  60000.000000       60000.000000  60000.000000   \n",
       "mean       0.496343      0.914817           0.503128      0.070008   \n",
       "std        0.291760      2.065447           0.299269      0.091184   \n",
       "min        0.011494      0.000091           0.000000      0.000000   \n",
       "25%        0.250000      0.000334           0.251781      0.000718   \n",
       "50%        0.498705      0.000616           0.487401      0.040061   \n",
       "75%        0.750000      0.174530           0.769231      0.102252   \n",
       "max        1.000000     37.229370           1.000000      1.000000   \n",
       "\n",
       "            AE_jsim   AE_oset_sim  \n",
       "count  60000.000000  60000.000000  \n",
       "mean       0.219903      0.325091  \n",
       "std        0.192537      0.252332  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.083333      0.100000  \n",
       "50%        0.169312      0.333333  \n",
       "75%        0.300000      0.500000  \n",
       "max        0.984375      0.984375  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/effectiveness/effectiveness.csv')\n",
    "df['AE_jsim'] = abs(df['jsim'] - df['a%'])\n",
    "df['AE_oset_sim'] = abs(df['josie'] - df['a%'])\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['AE_jsim'] = abs(df['jsim'] - df['a%'])\n",
    "df['AE_oset_sim'] = abs(df['josie'] - df['a%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>graphs_generation_time</th>\n",
       "      <th>embeddings_generation_time</th>\n",
       "      <th>cos_sim_time_armadillo</th>\n",
       "      <th>armadillo_total_time</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>AE_armadillo</th>\n",
       "      <th>overlap_computations_repeated_armadillo</th>\n",
       "      <th>overlap_computations_no_read_armadillo</th>\n",
       "      <th>AE_jsim</th>\n",
       "      <th>AE_oset_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.941967</td>\n",
       "      <td>0.647238</td>\n",
       "      <td>5.192626e-06</td>\n",
       "      <td>0.820302</td>\n",
       "      <td>1.992679e-06</td>\n",
       "      <td>0.496343</td>\n",
       "      <td>0.914817</td>\n",
       "      <td>0.006343</td>\n",
       "      <td>0.001689</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.008180</td>\n",
       "      <td>0.562836</td>\n",
       "      <td>0.119146</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.219903</td>\n",
       "      <td>0.325091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>160.590663</td>\n",
       "      <td>0.203651</td>\n",
       "      <td>3.881562e-06</td>\n",
       "      <td>0.143001</td>\n",
       "      <td>1.404466e-06</td>\n",
       "      <td>0.291760</td>\n",
       "      <td>2.065447</td>\n",
       "      <td>0.008727</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.008813</td>\n",
       "      <td>0.266788</td>\n",
       "      <td>0.142798</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000179</td>\n",
       "      <td>0.192537</td>\n",
       "      <td>0.252332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>0.189655</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.001019</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>3.099442e-06</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.004048</td>\n",
       "      <td>0.369021</td>\n",
       "      <td>0.019978</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>4.291534e-06</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>1.668930e-06</td>\n",
       "      <td>0.498705</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.001658</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.005319</td>\n",
       "      <td>0.563093</td>\n",
       "      <td>0.075787</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.169312</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>5.960464e-06</td>\n",
       "      <td>0.945205</td>\n",
       "      <td>2.384186e-06</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.174530</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>0.001691</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.007007</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.163311</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1428.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.418591e-04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.029274e-05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.229370</td>\n",
       "      <td>0.092574</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.004153</td>\n",
       "      <td>0.095008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020289</td>\n",
       "      <td>0.019195</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.984375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              seeds          jsim     jsim_time         josie    josie_time  \\\n",
       "count  60000.000000  60000.000000  6.000000e+04  60000.000000  6.000000e+04   \n",
       "mean      67.941967      0.647238  5.192626e-06      0.820302  1.992679e-06   \n",
       "std      160.590663      0.203651  3.881562e-06      0.143001  1.404466e-06   \n",
       "min        1.000000      0.068627  2.384186e-07      0.189655  4.768372e-07   \n",
       "25%        9.000000      0.488372  3.099442e-06      0.720000  1.192093e-06   \n",
       "50%       22.000000      0.634615  4.291534e-06      0.842105  1.668930e-06   \n",
       "75%       41.000000      0.794118  5.960464e-06      0.945205  2.384186e-06   \n",
       "max     1428.000000      1.000000  1.418591e-04      1.000000  4.029274e-05   \n",
       "\n",
       "                 a%    total_time  graphs_generation_time  \\\n",
       "count  60000.000000  60000.000000            60000.000000   \n",
       "mean       0.496343      0.914817                0.006343   \n",
       "std        0.291760      2.065447                0.008727   \n",
       "min        0.011494      0.000091                0.001019   \n",
       "25%        0.250000      0.000334                0.002236   \n",
       "50%        0.498705      0.000616                0.003515   \n",
       "75%        0.750000      0.174530                0.005172   \n",
       "max        1.000000     37.229370                0.092574   \n",
       "\n",
       "       embeddings_generation_time  cos_sim_time_armadillo  \\\n",
       "count                60000.000000            60000.000000   \n",
       "mean                     0.001689                0.000146   \n",
       "std                      0.000295                0.000021   \n",
       "min                      0.001604                0.000141   \n",
       "25%                      0.001637                0.000144   \n",
       "50%                      0.001658                0.000145   \n",
       "75%                      0.001691                0.000147   \n",
       "max                      0.012824                0.004153   \n",
       "\n",
       "       armadillo_total_time     armadillo  AE_armadillo  \\\n",
       "count          60000.000000  60000.000000  60000.000000   \n",
       "mean               0.008180      0.562836      0.119146   \n",
       "std                0.008813      0.266788      0.142798   \n",
       "min                0.002794      0.000000      0.000000   \n",
       "25%                0.004048      0.369021      0.019978   \n",
       "50%                0.005319      0.563093      0.075787   \n",
       "75%                0.007007      0.769231      0.163311   \n",
       "max                0.095008      1.000000      1.000000   \n",
       "\n",
       "       overlap_computations_repeated_armadillo  \\\n",
       "count                             60000.000000   \n",
       "mean                                  0.000227   \n",
       "std                                   0.000181   \n",
       "min                                   0.000215   \n",
       "25%                                   0.000219   \n",
       "50%                                   0.000220   \n",
       "75%                                   0.000221   \n",
       "max                                   0.020289   \n",
       "\n",
       "       overlap_computations_no_read_armadillo       AE_jsim   AE_oset_sim  \n",
       "count                            60000.000000  60000.000000  60000.000000  \n",
       "mean                                 0.000135      0.219903      0.325091  \n",
       "std                                  0.000179      0.192537      0.252332  \n",
       "min                                  0.000126      0.000000      0.000000  \n",
       "25%                                  0.000128      0.083333      0.100000  \n",
       "50%                                  0.000129      0.169312      0.333333  \n",
       "75%                                  0.000129      0.300000      0.500000  \n",
       "max                                  0.019195      0.984375      0.984375  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ff = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/efficiency/efficiency.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ff' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mff\u001b[49m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ff' is not defined"
     ]
    }
   ],
   "source": [
    "ff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dd = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_querying/table_querying_tmp_data/sloth_predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
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       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>object_csv_licensed.zip_appidsvc.dll.csv__3865...</td>\n",
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       "      <td>8000001</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>then_csv_licensed.zip_FOXNEWS.201210.csv</td>\n",
       "      <td>0.064431</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.529730</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.475248</td>\n",
       "      <td>0.008166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8000002</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>id_csv_licensed.zip_0021700946_4.csv</td>\n",
       "      <td>0.015710</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>14.486244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000003</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH2V01J.csv</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.612150</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.460545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8000004</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>living_thing_csv_licensed.zip_mrswunschel.csv</td>\n",
       "      <td>0.056291</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.148936</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.091802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999995</th>\n",
       "      <td>6999995</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.001652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999996</th>\n",
       "      <td>6999996</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_metadata-info_58.csv</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999997</th>\n",
       "      <td>6999997</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>speed_of_light_csv_licensed.zip_weather-2015-0...</td>\n",
       "      <td>0.103306</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.257732</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.002311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999998</th>\n",
       "      <td>6999998</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_alg.exe.csv__417245484...</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999999</th>\n",
       "      <td>6999999</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>data_rate_csv_licensed.zip_14-15_32.csv</td>\n",
       "      <td>0.021798</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994376 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                           r_id  \\\n",
       "0        8000000  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "1        8000001  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "2        8000002  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "3        8000003  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "4        8000004  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "...          ...                                            ...   \n",
       "9999995  6999995   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9999996  6999996   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9999997  6999997   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9999998  6999998   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9999999  6999999   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "\n",
       "                                                      s_id      jsim  \\\n",
       "0        object_csv_licensed.zip_appidsvc.dll.csv__3865...  0.000000   \n",
       "1                 then_csv_licensed.zip_FOXNEWS.201210.csv  0.064431   \n",
       "2                     id_csv_licensed.zip_0021700946_4.csv  0.015710   \n",
       "3            seek_time_csv_licensed.zip_PL2331LAH2V01J.csv  0.086355   \n",
       "4            living_thing_csv_licensed.zip_mrswunschel.csv  0.056291   \n",
       "...                                                    ...       ...   \n",
       "9999995      seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv  0.072464   \n",
       "9999996       object_csv_licensed.zip_metadata-info_58.csv  0.000000   \n",
       "9999997  speed_of_light_csv_licensed.zip_weather-2015-0...  0.103306   \n",
       "9999998  object_csv_licensed.zip_alg.exe.csv__417245484...  0.004184   \n",
       "9999999            data_rate_csv_licensed.zip_14-15_32.csv  0.021798   \n",
       "\n",
       "         jsim_time     josie  josie_time        a%  total_time  \n",
       "0         0.000055  0.000000    0.000004  0.000000    0.012150  \n",
       "1         0.000051  0.529730    0.000005  0.475248    0.008166  \n",
       "2         0.000065  0.105263    0.000006  0.078125   14.486244  \n",
       "3         0.000067  0.612150    0.000010  0.526316    9.460545  \n",
       "4         0.000096  0.148936    0.000023  0.091424    0.091802  \n",
       "...            ...       ...         ...       ...         ...  \n",
       "9999995   0.000006  0.196078    0.000002  0.052632    0.001652  \n",
       "9999996   0.000010  0.000000    0.000003  0.000000    0.000860  \n",
       "9999997   0.000024  0.257732    0.000006  0.035714    0.002311  \n",
       "9999998   0.000010  0.010309    0.000003  0.100000    0.001717  \n",
       "9999999   0.000020  0.082474    0.000005  0.100000    0.001408  \n",
       "\n",
       "[9994376 rows x 9 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ndcg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = dd[dd['r_id'] == 'seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.47524752],\n",
       "       [0.078125  ],\n",
       "       ...,\n",
       "       [0.03571429],\n",
       "       [0.        ],\n",
       "       [0.16198347]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(tmp[['a%']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8587242218520094"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(y_true=np.array(tmp[['a%']]).reshape(1,-1)*10, y_score=np.array(tmp[['jsim']]).reshape(1,-1)*10,k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46586474446268206"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(y_true=np.array(tmp[['a%']]).reshape(1,-1)*10, y_score=np.array(tmp[['josie']]).reshape(1,-1)*10, k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    ".1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9008969474123476"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(np.array([[0.9**(1/2),0.4**(1/2),0.6**(1/2), 0.2**(1/2)]]),np.array([[0.1,0.4,0.3,0.2]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.array([[0.9**(1/2),0.4**(1/2),0.6**(1/2), 0.2**(1/2)]])[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.48683298, 6.32455532, 7.74596669, 4.47213595]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[0.9**(1/2),0.4**(1/2),0.6**(1/2), 0.2**(1/2)]])*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.array(tmp[['a%']])[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 9975)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(tmp['a%']).reshape(1,-1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_querying/table_querying_tmp_data/table_querying_results_armadillo_no_csv.pkl', 'rb') as f:\n",
    "    dda = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['seek_time_csv_licensed.zip_PL1331LAGRZ6NH.csv', 'processing_time_csv_licensed.zip_J01-2004_721.csv', 'then_csv_licensed.zip_J01-2004_324.csv', 'seek_time_csv_licensed.zip_PL1331LAHDNTTH.csv', 'id_csv_licensed.zip_technician_shifts_480.csv', 'seek_time_csv_licensed.zip_PL2331LAGSURSJ.csv', 'id_csv_licensed.zip_requested_services_330.csv', 'parent_csv_licensed.zip_Configuration2_window7_split8_typetest_projectpoi1.5.csv', 'seek_time_csv_licensed.zip_PL2331LAGN44KJ.csv', 'id_csv_licensed.zip_0021900491_10.csv', 'thing_csv_licensed.zip_P05-1013_123.csv', 'show_time_csv_licensed.zip_messages_ru_73.csv', 'data_rate_csv_licensed.zip_A00-2018_akanksha_194.csv', 'escape_velocity_csv_licensed.zip_Configuration2_window15_split7_typetest_projectvelocity1.6.csv', 'seek_time_csv_licensed.zip_PL2331LAGPUYNJ.csv', 'id_csv_licensed.zip_boxscore_0021700338.csv', 'object_csv_licensed.zip_Configuration3_window1_split10_typetest_projectsynapse1.1.csv', 'seek_time_csv_licensed.zip_PL2331LAHDWZAJ.csv', 'then_csv_licensed.zip_J01-2004_aakansha_113.csv', 'object_csv_licensed.zip_Configuration3_window6_split2_typetest_projectsynapse1.0.csv', 'id_csv_licensed.zip_boxscore_0021700226.csv', 'processing_time_csv_licensed.zip_J01-2004_swastika_95.csv', 'seek_time_csv_licensed.zip_PL2331LAHD9XXJ.csv', 'processing_time_csv_licensed.zip_P05-1013_swastika_153.csv', 'seek_time_csv_licensed.zip_PL2331LAHB6W1J.csv', 'whole_csv_licensed.zip_P08-1028_3.csv', 'seek_time_csv_licensed.zip_PL1331LAGSAAZH.csv', 'id_csv_licensed.zip_test_data_records_51.csv', 'id_csv_licensed.zip_0021800295.csv', 'object_csv_licensed.zip_00005-136.csv', 'real_time_csv_licensed.zip_episode 5_6.csv', 'id_csv_licensed.zip_fm_policytc_44.csv', 'parent_csv_licensed.zip_Configuration1_window18_split11_typetypetest_projectpoi2.5.csv', 'show_time_csv_licensed.zip_J01-2004_swastika_200.csv', 'data_rate_csv_licensed.zip_A00-2018_867.csv', 'rate_of_return_csv_licensed.zip_J01-2004_31.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_23.csv', 'seek_time_csv_licensed.zip_PL2331LAGRX0NJ.csv', 'whole_csv_licensed.zip_P08-1028_swastika_188.csv', 'id_csv_licensed.zip_natures_67.csv', 'whole_csv_licensed.zip_P08-1028_563.csv', 'data_rate_csv_licensed.zip_A00-2018_sweta_21.csv', 'processing_time_csv_licensed.zip_P05-1013_834.csv', 'object_csv_licensed.zip_DolbyDecMFT.dll.csv__2351325777__.csv', 'show_time_csv_licensed.zip_J01-2004_swastika_48.csv', 'question_time_csv_licensed.zip_episode 13_2.csv', 'id_csv_licensed.zip_gul_summary_map_77.csv', 'seek_time_csv_licensed.zip_PL1331LAHBY1ZH.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_54.csv', 'then_csv_licensed.zip_J01-2004_aakansha_77.csv', 'object_csv_licensed.zip_DragDropExperienceDataExchangeDelegated.dll.csv__3433142928__.csv', 'seek_time_csv_licensed.zip_PL2331LAHD7KLJ.csv', 'object_csv_licensed.zip_PREDICTION_Object Oriented Analysis and Design_8.csv', 'object_csv_licensed.zip_00002-111.csv', 'id_csv_licensed.zip_boxscore_0021500377.csv', 'parent_csv_licensed.zip_1442305_hhRelMale.csv', 'parent_csv_licensed.zip_A00-2018_sweta_112.csv', 'seek_time_csv_licensed.zip_PL2331LAHDN50J.csv', 'lead_time_csv_licensed.zip_J01-2004_6.csv', 'half_life_csv_licensed.zip_2017-GOAT-TheWrap.csv', 'half_life_csv_licensed.zip_tokens_16.csv', 'data_rate_csv_licensed.zip_A00-2018_vardha_213.csv', 'real_time_csv_licensed.zip_0201_092942_InitializeSampleHD.csv', 'parent_csv_licensed.zip_1421110_hhRelMale.csv', 'parent_csv_licensed.zip_Configuration1_window15_split3_typetypetest_projectpoi2.5.csv', 'seek_time_csv_licensed.zip_PL1331LAHBS2ZH.csv', 'object_csv_licensed.zip_Configuration1_window16_split14_typetypetest_projectsynapse1.2.csv', 'object_csv_licensed.zip_winbiosensoradapter.dll.csv__175567660__.csv', 'object_csv_licensed.zip_00011-370.csv', 'id_csv_licensed.zip_boxscore_0021500172.csv', 'running_time_csv_licensed.zip_episode 5_3.csv', 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'parent_csv_licensed.zip_example_818.csv', 'show_time_csv_licensed.zip_P05-1013.annv3_91.csv', 'thing_csv_licensed.zip_A00-2018_akanksha_179.csv', 'object_csv_licensed.zip_Configuration1_window7_split15_typetypetest_projectsynapse1.2.csv', 'data_rate_csv_licensed.zip_A00-2018_akanksha_42.csv', 'seek_time_csv_licensed.zip_PL1331LAGM3B6H.csv', 'data_rate_csv_licensed.zip_W11-2123_418.csv', 'then_csv_licensed.zip_P08-1028_aakansha_68.csv', 'seek_time_csv_licensed.zip_PL2331LAGSSV0J.csv', 'show_time_csv_licensed.zip_P05-1013_595.csv', 'quick_time_csv_licensed.zip_recipe_1646.csv', 'id_csv_licensed.zip_a4a214334df3b54c8fffa74c29638644383a07511b022adaf866a78d15bf2952.csv', 'id_csv_licensed.zip_10.1038_ncomms8231.csv', 'processing_time_csv_licensed.zip_J01-2004_659.csv', 'parent_csv_licensed.zip_1411305_hhRelMale.csv', 'seek_time_csv_licensed.zip_PL2331LAHDL5DJ.csv', 'id_csv_licensed.zip_0021800283.csv', 'id_csv_licensed.zip_item_context_7.csv', 'object_csv_licensed.zip_00000-093.csv', 'id_csv_licensed.zip_0022000310.csv', 'id_csv_licensed.zip_test_data_records_129.csv', 'id_csv_licensed.zip_0021800819.csv', 'whole_csv_licensed.zip_P08-1028_aakansha_151.csv', 'object_csv_licensed.zip_Configuration1_window11_split9_typetypetest_projectsynapse1.2.csv', 'seek_time_csv_licensed.zip_PL1331LAGS8P8H.csv', 'object_csv_licensed.zip_00006-180.csv', 'object_csv_licensed.zip_Configuration1_window15_split10_typetypetest_projectsynapse1.2.csv', 'id_csv_licensed.zip_0021600827.csv', 'processing_time_csv_licensed.zip_P05-1013_447.csv', 'seek_time_csv_licensed.zip_PL2331LAGSUS1J.csv', 'then_csv_licensed.zip_W99-0623_21.csv', 'seek_time_csv_licensed.zip_PL2331LAHDE5EJ.csv', 'in_time_csv_licensed.zip_es-CL_151.csv', 'object_csv_licensed.zip_00007-124.csv', 'seek_time_csv_licensed.zip_PL2331LAHDAE3J.csv', 'dead_air_csv_licensed.zip_episode 2_2.csv', 'id_csv_licensed.zip_0021800908.csv', 'then_csv_licensed.zip_A00-2018_13.csv', 'id_csv_licensed.zip_0021500343.csv', 'object_csv_licensed.zip_WpdMtpIP.dll.csv__2933608856__.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_159.csv', 'seek_time_csv_licensed.zip_PL2331LAGST8SJ.csv', 'seek_time_csv_licensed.zip_PL2331LAGUL9YJ.csv', 'processing_time_csv_licensed.zip_J01-2004_aakansha_150.csv', 'whole_csv_licensed.zip_P08-1028_swastika_129.csv', 'object_csv_licensed.zip_Configuration1_window13_split11_typetest_projectsynapse1.0.csv', 'object_csv_licensed.zip_00018-130.csv', 'quick_time_csv_licensed.zip_episode 6_6.csv', 'then_csv_licensed.zip_docStyles_fall.csv', 'id_csv_licensed.zip_01_ophciexamination_visual_acuity_unit_value_2.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_210.csv', 'processing_time_csv_licensed.zip_J01-2004_aakansha_112.csv', 'seek_time_csv_licensed.zip_PL1331LAGTGSDH.csv', 'seek_time_csv_licensed.zip_PL1331LAGS5DDH.csv', 'object_csv_licensed.zip_Configuration3_window2_split6_typetest_projectsynapse1.1.csv', 'seek_time_csv_licensed.zip_PL2331LAGN4DPJ.csv', 'parent_csv_licensed.zip_mdl_question_750.csv', 'id_csv_licensed.zip_ir.model.access_23887.csv', 'id_csv_licensed.zip_0041600301.csv', 'id_csv_licensed.zip_boxscore_0021500379.csv', 'seek_time_csv_licensed.zip_PL1331LAGTVRHH.csv', 'then_csv_licensed.zip_J01-2004_424.csv', 'id_csv_licensed.zip_technician_shifts_525.csv', 'id_csv_licensed.zip_1922.csv', 'id_csv_licensed.zip_account.tax.template_2347.csv', 'show_time_csv_licensed.zip_J01-2004_865.csv', 'parent_csv_licensed.zip_1402024_hhRelMale.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_208.csv', 'half_life_csv_licensed.zip_2010-08-28.csv', 'id_csv_licensed.zip_boxscore_0021500181.csv', 'quick_time_csv_licensed.zip_recipe_1490.csv', 'whole_csv_licensed.zip_P08-1028_swastika_44.csv', 'seek_time_csv_licensed.zip_PL2331LAGRM30J.csv', 'object_csv_licensed.zip_00002-160.csv', 'escape_velocity_csv_licensed.zip_Configuration1_window11_split12_typetest_projectvelocity1.5.csv', 'parent_csv_licensed.zip_Configuration2_window13_split11_typetest_projectpoi1.5.csv', 'object_csv_licensed.zip_Configuration1_window12_split3_typetest_projectsynapse1.0.csv', 'object_csv_licensed.zip_Configuration1_window16_split13_typetypetest_projectsynapse1.2.csv', 'id_csv_licensed.zip_Coordinate.csv', 'running_time_csv_licensed.zip_J01-2004.annv3_28.csv', 'object_csv_licensed.zip_00007-260_1.csv', 'parent_csv_licensed.zip_A00-2018_481.csv', 'then_csv_licensed.zip_A00-2018_774.csv', 'parent_csv_licensed.zip_A00-2018_akanksha_21.csv', 'id_csv_licensed.zip_boxscore_0021500728.csv', 'thing_csv_licensed.zip_A00-2018_akanksha_27.csv', 'id_csv_licensed.zip_0021901253.csv', 'seek_time_csv_licensed.zip_PL1331LAHD3SKH.csv', 'seek_time_csv_licensed.zip_PL2331LAGUM58J.csv', 'show_time_csv_licensed.zip_J01-2004_243.csv', 'in_time_csv_licensed.zip_0201_161543_InitializeSampleRD.csv', 'object_csv_licensed.zip_Configuration3_window1_split6_typetest_projectsynapse1.0.csv', 'escape_velocity_csv_licensed.zip_Configuration3_window10_split16_typetest_projectvelocity1.6.csv', 'id_csv_licensed.zip_0021900304_6.csv', 'id_csv_licensed.zip_requested_services_23.csv', 'parent_csv_licensed.zip_Configuration2_window17_split13_typetest_projectpoi1.5.csv', 'id_csv_licensed.zip_fm_summary_map_25.csv', 'seek_time_csv_licensed.zip_PL2331LAHBWWLJ.csv', 'id_csv_licensed.zip_CHARTEVENTS_DM_1.csv', 'parent_csv_licensed.zip_account.tax.template_119.csv', 'id_csv_licensed.zip_10.1038_ncomms8262.csv', 'id_csv_licensed.zip_boxscore_0021400633.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_146.csv', 'id_csv_licensed.zip_0021900772_10.csv', 'seek_time_csv_licensed.zip_PL1331LAGR5VSH.csv', 'in_time_csv_licensed.zip_0130_184928_InitializeSampleGD.csv', 'show_time_csv_licensed.zip_J01-2004_aakansha_67.csv', 'show_time_csv_licensed.zip_P05-1013_4.csv', 'in_time_csv_licensed.zip_DATETIMEEVENTS_DM.csv', 'seek_time_csv_licensed.zip_PL1331LAGTGRRH.csv', 'half_life_csv_licensed.zip_tokens_58.csv', 'show_time_csv_licensed.zip_J01-2004_aakansha_158.csv', 'seek_time_csv_licensed.zip_PL2331LAGMPK8J.csv', 'object_csv_licensed.zip_ir.model.access_218.csv', 'object_csv_licensed.zip_Configuration3_window18_split5_typetest_projectsynapse1.0.csv', 'seek_time_csv_licensed.zip_PL2331LAGSU11J.csv', 'object_csv_licensed.zip_Introduction to Microprocessors_test_3.csv', 'id_csv_licensed.zip_boxscore_0021600049.csv', 'object_csv_licensed.zip_00023-859.csv', 'then_csv_licensed.zip_P08-1028_sweta_51.csv', 'seek_time_csv_licensed.zip_PL2331LAGRWS5J.csv', 'seek_time_csv_licensed.zip_PL1331LAGTAW8H.csv', 'quick_time_csv_licensed.zip_recipe_1176.csv', 'id_csv_licensed.zip_0021800059.csv', 'object_csv_licensed.zip_Computer ArchitectureOperating Systems_train_3_1.csv', 'thing_csv_licensed.zip_1998-09-26.csv', 'data_rate_csv_licensed.zip_J01-2004_2.csv', 'id_csv_licensed.zip_10.1038_ncomms7785_1.csv', 'id_csv_licensed.zip_dtb_delivery_fee_69.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_184.csv', 'living_thing_csv_licensed.zip_tokens_62.csv', 'seek_time_csv_licensed.zip_PL1331LAHBSBYH.csv'])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dda.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total_time': 0.6365511417388916,\n",
       " 'overlap_computation_time': 0.5155830383300781,\n",
       " 'overlaps': [('lead_time_csv_licensed.zip_VBG.csv', 0.6870644092559814),\n",
       "  ('centripetal_acceleration_csv_licensed.zip_queries-edx.csv',\n",
       "   0.6636601686477661),\n",
       "  ('escape_velocity_csv_licensed.zip_queries-edx.csv', 0.6636601090431213),\n",
       "  ('id_csv_licensed.zip_FACT_OICS_1.csv', 0.6538208723068237),\n",
       "  ('id_csv_licensed.zip_jos_menu_27.csv', 0.6504244804382324),\n",
       "  ('show_time_csv_licensed.zip_JJ.csv', 0.640790581703186),\n",
       "  ('bits_per_second_csv_licensed.zip_13-14_79.csv', 0.6378457546234131),\n",
       "  ('beats_per_minute_csv_licensed.zip_PMI_df.csv', 0.6314292550086975),\n",
       "  ('quick_time_csv_licensed.zip_PMI_df.csv', 0.6314291954040527),\n",
       "  ('parent_csv_licensed.zip_analytic_coverage_04_20_2021.csv',\n",
       "   0.6190639138221741),\n",
       "  ('real_time_csv_licensed.zip_07-08_1381.csv', 0.6113214492797852),\n",
       "  ('thing_csv_licensed.zip_07-08_2039.csv', 0.6113214492797852),\n",
       "  ('show_time_csv_licensed.zip_NN.csv', 0.6019054055213928),\n",
       "  ('seek_time_csv_licensed.zip_PL2331LAHD9LWJ.csv', 0.6012736558914185),\n",
       "  ('seek_time_csv_licensed.zip_PL2331LAHBWT7J.csv', 0.5952871441841125),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window8_split10_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948302745819092),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window2_split13_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948302745819092),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window18_split2_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948302745819092),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window7_split12_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948302745819092),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window14_split2_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948302745819092),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window13_split3_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948302149772644),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window16_split4_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948301553726196),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window14_split11_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948301553726196),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window17_split11_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948301553726196),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window6_split9_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948301553726196),\n",
       "  ('parent_csv_licensed.zip_Configuration1_window3_split12_typetypetest_projectpoi2.0.csv',\n",
       "   0.5948301553726196),\n",
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       "  ('then_csv_licensed.zip_W99-0623_swastika_54.csv', 0.42571356892585754),\n",
       "  ('show_time_csv_licensed.zip_W99-0623_swastika_129.csv',\n",
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       "  ('then_csv_licensed.zip_W99-0623_swastika_315.csv', 0.42571356892585754),\n",
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       "  ('seek_time_csv_licensed.zip_PL2331LAH9TERJ.csv', 0.42562055587768555),\n",
       "  ...]}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dda['seek_time_csv_licensed.zip_PL1331LAGRZ6NH.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "armadillo_out = dda\n",
    "arm_preds = {}\n",
    "for k in armadillo_out.keys():\n",
    "    arm_preds[k] = {}\n",
    "    for t in armadillo_out[k]['overlaps']:\n",
    "        arm_preds[k][t[0]] = t[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6870644092559814"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arm_preds['seek_time_csv_licensed.zip_PL1331LAGRZ6NH.csv']['lead_time_csv_licensed.zip_VBG.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['seek_time_csv_licensed.zip_PL1331LAGRZ6NH.csv', 'processing_time_csv_licensed.zip_J01-2004_721.csv', 'then_csv_licensed.zip_J01-2004_324.csv', 'seek_time_csv_licensed.zip_PL1331LAHDNTTH.csv', 'id_csv_licensed.zip_technician_shifts_480.csv', 'seek_time_csv_licensed.zip_PL2331LAGSURSJ.csv', 'id_csv_licensed.zip_requested_services_330.csv', 'parent_csv_licensed.zip_Configuration2_window7_split8_typetest_projectpoi1.5.csv', 'seek_time_csv_licensed.zip_PL2331LAGN44KJ.csv', 'id_csv_licensed.zip_0021900491_10.csv', 'thing_csv_licensed.zip_P05-1013_123.csv', 'show_time_csv_licensed.zip_messages_ru_73.csv', 'data_rate_csv_licensed.zip_A00-2018_akanksha_194.csv', 'escape_velocity_csv_licensed.zip_Configuration2_window15_split7_typetest_projectvelocity1.6.csv', 'seek_time_csv_licensed.zip_PL2331LAGPUYNJ.csv', 'id_csv_licensed.zip_boxscore_0021700338.csv', 'object_csv_licensed.zip_Configuration3_window1_split10_typetest_projectsynapse1.1.csv', 'seek_time_csv_licensed.zip_PL2331LAHDWZAJ.csv', 'then_csv_licensed.zip_J01-2004_aakansha_113.csv', 'object_csv_licensed.zip_Configuration3_window6_split2_typetest_projectsynapse1.0.csv', 'id_csv_licensed.zip_boxscore_0021700226.csv', 'processing_time_csv_licensed.zip_J01-2004_swastika_95.csv', 'seek_time_csv_licensed.zip_PL2331LAHD9XXJ.csv', 'processing_time_csv_licensed.zip_P05-1013_swastika_153.csv', 'seek_time_csv_licensed.zip_PL2331LAHB6W1J.csv', 'whole_csv_licensed.zip_P08-1028_3.csv', 'seek_time_csv_licensed.zip_PL1331LAGSAAZH.csv', 'id_csv_licensed.zip_test_data_records_51.csv', 'id_csv_licensed.zip_0021800295.csv', 'object_csv_licensed.zip_00005-136.csv', 'real_time_csv_licensed.zip_episode 5_6.csv', 'id_csv_licensed.zip_fm_policytc_44.csv', 'parent_csv_licensed.zip_Configuration1_window18_split11_typetypetest_projectpoi2.5.csv', 'show_time_csv_licensed.zip_J01-2004_swastika_200.csv', 'data_rate_csv_licensed.zip_A00-2018_867.csv', 'rate_of_return_csv_licensed.zip_J01-2004_31.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_23.csv', 'seek_time_csv_licensed.zip_PL2331LAGRX0NJ.csv', 'whole_csv_licensed.zip_P08-1028_swastika_188.csv', 'id_csv_licensed.zip_natures_67.csv', 'whole_csv_licensed.zip_P08-1028_563.csv', 'data_rate_csv_licensed.zip_A00-2018_sweta_21.csv', 'processing_time_csv_licensed.zip_P05-1013_834.csv', 'object_csv_licensed.zip_DolbyDecMFT.dll.csv__2351325777__.csv', 'show_time_csv_licensed.zip_J01-2004_swastika_48.csv', 'question_time_csv_licensed.zip_episode 13_2.csv', 'id_csv_licensed.zip_gul_summary_map_77.csv', 'seek_time_csv_licensed.zip_PL1331LAHBY1ZH.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_54.csv', 'then_csv_licensed.zip_J01-2004_aakansha_77.csv', 'object_csv_licensed.zip_DragDropExperienceDataExchangeDelegated.dll.csv__3433142928__.csv', 'seek_time_csv_licensed.zip_PL2331LAHD7KLJ.csv', 'object_csv_licensed.zip_PREDICTION_Object Oriented Analysis and Design_8.csv', 'object_csv_licensed.zip_00002-111.csv', 'id_csv_licensed.zip_boxscore_0021500377.csv', 'parent_csv_licensed.zip_1442305_hhRelMale.csv', 'parent_csv_licensed.zip_A00-2018_sweta_112.csv', 'seek_time_csv_licensed.zip_PL2331LAHDN50J.csv', 'lead_time_csv_licensed.zip_J01-2004_6.csv', 'half_life_csv_licensed.zip_2017-GOAT-TheWrap.csv', 'half_life_csv_licensed.zip_tokens_16.csv', 'data_rate_csv_licensed.zip_A00-2018_vardha_213.csv', 'real_time_csv_licensed.zip_0201_092942_InitializeSampleHD.csv', 'parent_csv_licensed.zip_1421110_hhRelMale.csv', 'parent_csv_licensed.zip_Configuration1_window15_split3_typetypetest_projectpoi2.5.csv', 'seek_time_csv_licensed.zip_PL1331LAHBS2ZH.csv', 'object_csv_licensed.zip_Configuration1_window16_split14_typetypetest_projectsynapse1.2.csv', 'object_csv_licensed.zip_winbiosensoradapter.dll.csv__175567660__.csv', 'object_csv_licensed.zip_00011-370.csv', 'id_csv_licensed.zip_boxscore_0021500172.csv', 'running_time_csv_licensed.zip_episode 5_3.csv', 'seek_time_csv_licensed.zip_PL1331LAGTKG9H.csv', 'parent_csv_licensed.zip_example_780.csv', 'object_csv_licensed.zip_fphc.dll.csv__1818633800__.csv', 'seek_time_csv_licensed.zip_PL2331LAHDW6YJ.csv', 'id_csv_licensed.zip_language_1045.csv', 'id_csv_licensed.zip_move_effect_changelog_prose_23.csv', 'parent_csv_licensed.zip_example_761.csv', 'in_time_csv_licensed.zip_0201_083323_InitializeSampleHD.csv', 'in_time_csv_licensed.zip_0202_052806_InitializeSampleRD.csv', 'show_time_csv_licensed.zip_J01-2004_5.csv', 'id_csv_licensed.zip_6928.csv', 'real_time_csv_licensed.zip_0224_140114_InitializeSampleCC.csv', 'whole_csv_licensed.zip_P08-1028_708.csv', 'object_csv_licensed.zip_Configuration1_window16_split3_typetypetest_projectpoi2.5.csv', 'parent_csv_licensed.zip_example_176.csv', 'seek_time_csv_licensed.zip_PL2331LAHDUP5J.csv', 'show_time_csv_licensed.zip_J01-2004_722.csv', 'id_csv_licensed.zip_thirst_data_1.csv', 'seek_time_csv_licensed.zip_PL2331LAGN188J.csv', 'processing_time_csv_licensed.zip_P05-1013_aakansha_36.csv', 'data_rate_csv_licensed.zip_W11-2123_swastika_65.csv', 'id_csv_licensed.zip_boxscore_0021500523_1.csv', 'seek_time_csv_licensed.zip_PL1331LAGSMXWH.csv', 'id_csv_licensed.zip_technician_shifts_97.csv', 'object_csv_licensed.zip_00007-241.csv', 'seek_time_csv_licensed.zip_PL2331LAGSU64J.csv', 'show_time_csv_licensed.zip_P05-1013_465.csv', 'object_csv_licensed.zip_Configuration1_window3_split11_typetest_projectsynapse1.0.csv', 'object_csv_licensed.zip_Configuration3_window2_split3_typetest_projectsynapse1.1.csv', 'running_time_csv_licensed.zip_episode 9_2.csv', 'in_time_csv_licensed.zip_0202_175612_InitializeSampleHD.csv', 'then_csv_licensed.zip_J01-2004_382.csv', 'parent_csv_licensed.zip_example_816.csv', 'seek_time_csv_licensed.zip_PL2331LAGN869J_1.csv', 'object_csv_licensed.zip_Configuration1_window16_split5_typetest_projectsynapse1.2.csv', 'object_csv_licensed.zip_00015-230.csv', 'processing_time_csv_licensed.zip_J01-2004_697.csv', 'thing_csv_licensed.zip_A00-2018_akanksha_37.csv', 'id_csv_licensed.zip_00013-234.csv', 'object_csv_licensed.zip_00010-191.csv', 'id_csv_licensed.zip_0021800830.csv', 'parent_csv_licensed.zip_example_462.csv', 'id_csv_licensed.zip_boxscore_0021700969.csv', 'escape_velocity_csv_licensed.zip_Configuration3_window9_split4_typetest_projectvelocity1.6.csv', 'id_csv_licensed.zip_boxscore_0021800491_1.csv', 'object_csv_licensed.zip_MapsMigPlugin.dll.csv__1427018721__.csv', 'real_time_csv_licensed.zip_0209_071538_InitializeSampleHD.csv', 'in_time_csv_licensed.zip_weather-2015-08-19_114.csv', 'processing_time_csv_licensed.zip_P05-1013.annv3_66.csv', 'seek_time_csv_licensed.zip_PL2331LAHBAWAJ.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_174.csv', 'then_csv_licensed.zip_P08-1102_sweta_33.csv', 'show_time_csv_licensed.zip_P05-1013_swastika_104.csv', 'object_csv_licensed.zip_00015-362.csv', 'then_csv_licensed.zip_A00-2018_akanksha_186.csv', 'parent_csv_licensed.zip_example_401.csv', 'processing_time_csv_licensed.zip_J01-2004_swastika_36.csv', 'data_rate_csv_licensed.zip_A00-2018_akanksha_14.csv', 'seek_time_csv_licensed.zip_PL1331LAGSB8LH.csv', 'thing_csv_licensed.zip_episode 5_3.csv', 'seek_time_csv_licensed.zip_PL1331LAGTW3WH.csv', 'object_csv_licensed.zip_Configuration2_window16_split15_typetest_projectsynapse1.2.csv', 'id_csv_licensed.zip_0021600826.csv', 'id_csv_licensed.zip_requested_services_190.csv', 'object_csv_licensed.zip_PREDICTION_Intermediate Software Engineering_9.csv', 'id_csv_licensed.zip_0021900194.csv', 'seek_time_csv_licensed.zip_PL1331LAGM39ZH.csv', 'object_csv_licensed.zip_Windows.ApplicationModel.Wallet.dll.csv__3335513724__.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_140.csv', 'id_csv_licensed.zip_requested_services_209.csv', 'id_csv_licensed.zip_account.tax.template_2364.csv', 'then_csv_licensed.zip_P05-1013.annv3_62.csv', 'object_csv_licensed.zip_redaktor_17.csv', 'respiratory_rate_csv_licensed.zip_f_en-cx_PH.csv', 'seek_time_csv_licensed.zip_PL1331LAHDZWUH.csv', 'seek_time_csv_licensed.zip_PL1331LAGRZKAH.csv', 'id_csv_licensed.zip_0021800003.csv', 'then_csv_licensed.zip_A00-2018_772.csv', 'processing_time_csv_licensed.zip_P05-1013_737.csv', 'object_csv_licensed.zip_00009-359.csv', 'processing_time_csv_licensed.zip_J01-2004_aakansha_114.csv', 'then_csv_licensed.zip_A00-2018_918.csv', 'id_csv_licensed.zip_ir.model.access_185.csv', 'id_csv_licensed.zip_0021700210_7.csv', 'id_csv_licensed.zip_0021700035_8.csv', 'in_time_csv_licensed.zip_weather-2012-02-16_137.csv', 'then_csv_licensed.zip_A00-2018_965.csv', 'parent_csv_licensed.zip_Configuration3_window10_split2_typetest_projectpoi1.5.csv', 'id_csv_licensed.zip_DM_Logical_Types_2.csv', 'object_csv_licensed.zip_00011-262.csv', 'object_csv_licensed.zip_00002-078.csv', 'lead_time_csv_licensed.zip_J01-2004.annv3_47.csv', 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'data_rate_csv_licensed.zip_P08-1028.csv', 'id_csv_licensed.zip_10.1038_ncomms7766.csv', 'seek_time_csv_licensed.zip_PL2331LAGSSUKJ.csv', 'object_csv_licensed.zip_Configuration4_window0_split11_typetest_projectsynapse1.0.csv', 'id_csv_licensed.zip_0021601039.csv', 'seek_time_csv_licensed.zip_PL1331LAHBV55H.csv', 'then_csv_licensed.zip_J01-2004_aakansha_8.csv', 'parent_csv_licensed.zip_A00-2018_akanksha_189.csv', 'parent_csv_licensed.zip_Configuration2_window18_split9_typetest_projectpoi1.5.csv', 'id_csv_licensed.zip_20171002152910.csv', 'object_csv_licensed.zip_Configuration1_window10_split10_typetest_projectsynapse1.1.csv', 'object_csv_licensed.zip_SettingsHandlers_Authentication.dll.csv__616412041__.csv', 'object_csv_licensed.zip_00017-145.csv', 'processing_time_csv_licensed.zip_J01-2004_swastika_168.csv', 'id_csv_licensed.zip_technician_shifts_463.csv', 'processing_time_csv_licensed.zip_Millwrights_1.csv', 'id_csv_licensed.zip_0021700479_10.csv', 'then_csv_licensed.zip_J01-2004_542.csv', 'object_csv_licensed.zip_Intermediate Software Engineering_train_1_2.csv', 'id_csv_licensed.zip_0021500878.csv', 'object_csv_licensed.zip_Configuration2_window7_split10_typetest_projectsynapse1.1.csv', 'then_csv_licensed.zip_J01-2004.annv3_46.csv', 'id_csv_licensed.zip_8554.csv', 'object_csv_licensed.zip_Configuration2_window13_split7_typetest_projectsynapse1.1.csv', 'show_time_csv_licensed.zip_J01-2004_swastika_116.csv', 'id_csv_licensed.zip_location_area_prose_16.csv', 'object_csv_licensed.zip_00002-096.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_160.csv', 'seek_time_csv_licensed.zip_PL1331LAGT82AH.csv', 'parent_csv_licensed.zip_example_731.csv', 'id_csv_licensed.zip_gul_summary_map_71.csv', 'id_csv_licensed.zip_6181.csv', 'id_csv_licensed.zip_boxscore_0021500449.csv', 'id_csv_licensed.zip_10.1038_ncomms8180.csv', 'in_time_csv_licensed.zip_0201_092533_InitializeSampleHD.csv', 'object_csv_licensed.zip_ObjectOriented Programming and Data Structures I_train_1.csv', 'id_csv_licensed.zip_language_638.csv', 'seek_time_csv_licensed.zip_PL1331LAGSLY6H.csv', 'show_time_csv_licensed.zip_example_192.csv', 'id_csv_licensed.zip_technician_shifts_518.csv', 'whole_csv_licensed.zip_P08-1028_780.csv', 'object_csv_licensed.zip_00005-071.csv', 'seek_time_csv_licensed.zip_PL1331LAGS4HRH.csv', 'seek_time_csv_licensed.zip_PL2331LAGMNHPJ.csv', 'id_csv_licensed.zip_boxscore_0021601222.csv', 'in_time_csv_licensed.zip_0202_084553_InitializeSampleRD.csv', 'in_time_csv_licensed.zip_0202_054837_InitializeSampleRD.csv', 'then_csv_licensed.zip_J01-2004_544.csv', 'id_csv_licensed.zip_technician_shifts_417.csv', 'data_rate_csv_licensed.zip_A00-2018_sweta_119.csv', 'parent_csv_licensed.zip_A00-2018_27.csv', 'seek_time_csv_licensed.zip_PL2331LAHDEV7J.csv', 'object_csv_licensed.zip_trie.dll.csv__399545289__.csv', 'parent_csv_licensed.zip_A00-2018_akanksha_10.csv', 'real_time_csv_licensed.zip_1989-06-03_1.csv', 'seek_time_csv_licensed.zip_PL1331LAGTD11H.csv', 'id_csv_licensed.zip_6920.csv', 'id_csv_licensed.zip_ir.model.access_86669.csv', 'show_time_csv_licensed.zip_J01-2004_aakansha_122.csv', 'seek_time_csv_licensed.zip_PL1331LAGT8TEH.csv', 'then_csv_licensed.zip_A00-2018_sweta_119.csv', 'id_csv_licensed.zip_10.1038_ncomms8107.csv', 'object_csv_licensed.zip_00006-133.csv', 'show_time_csv_licensed.zip_P05-1013_647.csv', 'id_csv_licensed.zip_Iris Dataset_4.csv', 'whole_csv_licensed.zip_funcs_by_address_170.csv', 'real_time_csv_licensed.zip_0224_114212_InitializeSampleCC.csv', 'object_csv_licensed.zip_00010-230.csv', 'processing_time_csv_licensed.zip_J01-2004_678.csv', 'in_time_csv_licensed.zip_weather-2014-04-14_52.csv', 'seek_time_csv_licensed.zip_PL2331LAGUMJ2J.csv', 'seek_time_csv_licensed.zip_PL2331LAGPTW0J.csv', 'seek_time_csv_licensed.zip_PL2331LAGUGNTJ.csv', 'object_csv_licensed.zip_poi-2.0_4.csv', 'seek_time_csv_licensed.zip_PL1331LAGM31PH.csv', 'id_csv_licensed.zip_0021701029.csv', 'id_csv_licensed.zip_8078_2.csv', 'object_csv_licensed.zip_Configuration3_window6_split4_typetest_projectsynapse1.2.csv', 'object_csv_licensed.zip_Total_Public_Complaints_ASR_PCF_TBL_001.csv', 'parent_csv_licensed.zip_Modules_10Oct18.csv', 'object_csv_licensed.zip_00023-535.csv', 'seek_time_csv_licensed.zip_PL2331LAHDRZ8J.csv', 'object_csv_licensed.zip_Configuration3_window9_split9_typetest_projectsynapse1.0.csv', 'seek_time_csv_licensed.zip_PL1331LAGRLKDH.csv', 'real_time_csv_licensed.zip_0210_035339_InitializeSampleRD.csv', 'object_csv_licensed.zip_00011-854.csv', 'object_csv_licensed.zip_Configuration2_window18_split1_typetest_projectsynapse.csv', 'object_csv_licensed.zip_Configuration3_window10_split9_typetest_projectsynapse1.0.csv', 'real_time_csv_licensed.zip_object_recogition.meta.csv', 'id_csv_licensed.zip_halo_85010288108_particle_ID.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_59.csv', 'id_csv_licensed.zip_boxscore_0021600349.csv', 'seek_time_csv_licensed.zip_PL2331LAHDUHYJ.csv', 'id_csv_licensed.zip_requested_services_298.csv', 'object_csv_licensed.zip_Configuration2_window5_split9_typetest_projectsynapse1.0.csv', 'id_csv_licensed.zip_7727_3.csv', 'whole_csv_licensed.zip_P08-1028.annv3_68.csv', 'seek_time_csv_licensed.zip_PL1331LAGRYS3H.csv', 'object_csv_licensed.zip_ChtAdvancedDS.dll.csv__1967600168__.csv', 'id_csv_licensed.zip_ir.model.access_3438.csv', 'object_csv_licensed.zip_00023-857.csv', 'then_csv_licensed.zip_J01-2004_aakansha_189.csv', 'then_csv_licensed.zip_J01-2004_aakansha_155.csv', 'object_csv_licensed.zip_Software Engineering Project I_train_2_1.csv', 'seek_time_csv_licensed.zip_PL1331LAH2E13H.csv', 'seek_time_csv_licensed.zip_PL1331LAGTDGZH_1.csv', 'object_csv_licensed.zip_00015-291.csv', 'lead_time_csv_licensed.zip_CNN.201501_2.csv', 'in_time_csv_licensed.zip_0202_165948_InitializeSampleHD.csv', 'escape_velocity_csv_licensed.zip_Configuration1_window19_split7_typetest_projectvelocity1.6.csv', 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'escape_velocity_csv_licensed.zip_Configuration3_window10_split16_typetest_projectvelocity1.6.csv', 'id_csv_licensed.zip_0021900304_6.csv', 'id_csv_licensed.zip_requested_services_23.csv', 'parent_csv_licensed.zip_Configuration2_window17_split13_typetest_projectpoi1.5.csv', 'id_csv_licensed.zip_fm_summary_map_25.csv', 'seek_time_csv_licensed.zip_PL2331LAHBWWLJ.csv', 'id_csv_licensed.zip_CHARTEVENTS_DM_1.csv', 'parent_csv_licensed.zip_account.tax.template_119.csv', 'id_csv_licensed.zip_10.1038_ncomms8262.csv', 'id_csv_licensed.zip_boxscore_0021400633.csv', 'processing_time_csv_licensed.zip_P05-1013_vardha_146.csv', 'id_csv_licensed.zip_0021900772_10.csv', 'seek_time_csv_licensed.zip_PL1331LAGR5VSH.csv', 'in_time_csv_licensed.zip_0130_184928_InitializeSampleGD.csv', 'show_time_csv_licensed.zip_J01-2004_aakansha_67.csv', 'show_time_csv_licensed.zip_P05-1013_4.csv', 'in_time_csv_licensed.zip_DATETIMEEVENTS_DM.csv', 'seek_time_csv_licensed.zip_PL1331LAGTGRRH.csv', 'half_life_csv_licensed.zip_tokens_58.csv', 'show_time_csv_licensed.zip_J01-2004_aakansha_158.csv', 'seek_time_csv_licensed.zip_PL2331LAGMPK8J.csv', 'object_csv_licensed.zip_ir.model.access_218.csv', 'object_csv_licensed.zip_Configuration3_window18_split5_typetest_projectsynapse1.0.csv', 'seek_time_csv_licensed.zip_PL2331LAGSU11J.csv', 'object_csv_licensed.zip_Introduction to Microprocessors_test_3.csv', 'id_csv_licensed.zip_boxscore_0021600049.csv', 'object_csv_licensed.zip_00023-859.csv', 'then_csv_licensed.zip_P08-1028_sweta_51.csv', 'seek_time_csv_licensed.zip_PL2331LAGRWS5J.csv', 'seek_time_csv_licensed.zip_PL1331LAGTAW8H.csv', 'quick_time_csv_licensed.zip_recipe_1176.csv', 'id_csv_licensed.zip_0021800059.csv', 'object_csv_licensed.zip_Computer ArchitectureOperating Systems_train_3_1.csv', 'thing_csv_licensed.zip_1998-09-26.csv', 'data_rate_csv_licensed.zip_J01-2004_2.csv', 'id_csv_licensed.zip_10.1038_ncomms7785_1.csv', 'id_csv_licensed.zip_dtb_delivery_fee_69.csv', 'show_time_csv_licensed.zip_J01-2004_sweta_184.csv', 'living_thing_csv_licensed.zip_tokens_62.csv', 'seek_time_csv_licensed.zip_PL1331LAHBSBYH.csv'])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arm_preds.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'ff':[]}\n",
    "for k in range(5):\n",
    "    d['ff'].append(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "k['luca'] = [1,2,3,4,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ff</th>\n",
       "      <th>luca</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ff  luca\n",
       "0   0     1\n",
       "1   1     2\n",
       "2   2     3\n",
       "3   3     4\n",
       "4   4     5"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dak = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/wikitables_stats.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_cols</th>\n",
       "      <th>n_rows</th>\n",
       "      <th>area</th>\n",
       "      <th>n_distinct_values</th>\n",
       "      <th>n_numerical</th>\n",
       "      <th>n_textual</th>\n",
       "      <th>n_nans</th>\n",
       "      <th>n_bools</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "      <td>2.127551e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.226195e+00</td>\n",
       "      <td>1.147382e+01</td>\n",
       "      <td>6.471986e+01</td>\n",
       "      <td>4.234295e+01</td>\n",
       "      <td>5.819784e+00</td>\n",
       "      <td>5.369081e+01</td>\n",
       "      <td>5.209242e+00</td>\n",
       "      <td>2.914149e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.063033e+00</td>\n",
       "      <td>2.732220e+01</td>\n",
       "      <td>1.826009e+02</td>\n",
       "      <td>9.867172e+01</td>\n",
       "      <td>3.800527e+01</td>\n",
       "      <td>1.535340e+02</td>\n",
       "      <td>3.797702e+01</td>\n",
       "      <td>3.716087e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>6.300000e+01</td>\n",
       "      <td>4.500000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.200000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.830000e+02</td>\n",
       "      <td>4.670000e+03</td>\n",
       "      <td>3.857000e+04</td>\n",
       "      <td>2.034700e+04</td>\n",
       "      <td>9.296000e+03</td>\n",
       "      <td>3.857000e+04</td>\n",
       "      <td>1.034600e+04</td>\n",
       "      <td>5.400000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             n_cols        n_rows          area  n_distinct_values  \\\n",
       "count  2.127551e+06  2.127551e+06  2.127551e+06       2.127551e+06   \n",
       "mean   5.226195e+00  1.147382e+01  6.471986e+01       4.234295e+01   \n",
       "std    4.063033e+00  2.732220e+01  1.826009e+02       9.867172e+01   \n",
       "min    1.000000e+00  1.000000e+00  1.000000e+00       1.000000e+00   \n",
       "25%    3.000000e+00  3.000000e+00  1.000000e+01       9.000000e+00   \n",
       "50%    4.000000e+00  6.000000e+00  2.700000e+01       2.100000e+01   \n",
       "75%    6.000000e+00  1.200000e+01  6.300000e+01       4.500000e+01   \n",
       "max    8.830000e+02  4.670000e+03  3.857000e+04       2.034700e+04   \n",
       "\n",
       "        n_numerical     n_textual        n_nans       n_bools  \n",
       "count  2.127551e+06  2.127551e+06  2.127551e+06  2.127551e+06  \n",
       "mean   5.819784e+00  5.369081e+01  5.209242e+00  2.914149e-05  \n",
       "std    3.800527e+01  1.535340e+02  3.797702e+01  3.716087e-02  \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
       "25%    0.000000e+00  8.000000e+00  0.000000e+00  0.000000e+00  \n",
       "50%    0.000000e+00  2.100000e+01  0.000000e+00  0.000000e+00  \n",
       "75%    1.000000e+00  5.200000e+01  1.000000e+00  0.000000e+00  \n",
       "max    9.296000e+03  3.857000e+04  1.034600e+04  5.400000e+01  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dak.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "dak.drop(columns=['n_bools']).describe().to_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/wikitables_stats_desc.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2127551, 9)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/wikitables_stats.csv').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2127551"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "klkl = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_querying/sloth_armadillo_predictions_table_querying.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>object_csv_licensed.zip_appidsvc.dll.csv__3865...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012150</td>\n",
       "      <td>0.272864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000001</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>then_csv_licensed.zip_FOXNEWS.201210.csv</td>\n",
       "      <td>0.064431</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.529730</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.475248</td>\n",
       "      <td>0.008166</td>\n",
       "      <td>0.352441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8000002</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>id_csv_licensed.zip_0021700946_4.csv</td>\n",
       "      <td>0.015710</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>14.486244</td>\n",
       "      <td>0.411786</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000003</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH2V01J.csv</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.612150</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.460545</td>\n",
       "      <td>0.337523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8000004</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>living_thing_csv_licensed.zip_mrswunschel.csv</td>\n",
       "      <td>0.056291</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.148936</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.091802</td>\n",
       "      <td>0.442848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9994371</th>\n",
       "      <td>6999995</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>0.101239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994372</th>\n",
       "      <td>6999996</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_metadata-info_58.csv</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>-0.054163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994373</th>\n",
       "      <td>6999997</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>speed_of_light_csv_licensed.zip_weather-2015-0...</td>\n",
       "      <td>0.103306</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.257732</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.035714</td>\n",
       "      <td>0.002311</td>\n",
       "      <td>-0.121819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994374</th>\n",
       "      <td>6999998</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_alg.exe.csv__417245484...</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.264201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994375</th>\n",
       "      <td>6999999</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>data_rate_csv_licensed.zip_14-15_32.csv</td>\n",
       "      <td>0.021798</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.058705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994376 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                           r_id  \\\n",
       "0        8000000  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "1        8000001  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "2        8000002  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "3        8000003  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "4        8000004  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "...          ...                                            ...   \n",
       "9994371  6999995   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994372  6999996   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994373  6999997   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994374  6999998   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994375  6999999   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "\n",
       "                                                      s_id      jsim  \\\n",
       "0        object_csv_licensed.zip_appidsvc.dll.csv__3865...  0.000000   \n",
       "1                 then_csv_licensed.zip_FOXNEWS.201210.csv  0.064431   \n",
       "2                     id_csv_licensed.zip_0021700946_4.csv  0.015710   \n",
       "3            seek_time_csv_licensed.zip_PL2331LAH2V01J.csv  0.086355   \n",
       "4            living_thing_csv_licensed.zip_mrswunschel.csv  0.056291   \n",
       "...                                                    ...       ...   \n",
       "9994371      seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv  0.072464   \n",
       "9994372       object_csv_licensed.zip_metadata-info_58.csv  0.000000   \n",
       "9994373  speed_of_light_csv_licensed.zip_weather-2015-0...  0.103306   \n",
       "9994374  object_csv_licensed.zip_alg.exe.csv__417245484...  0.004184   \n",
       "9994375            data_rate_csv_licensed.zip_14-15_32.csv  0.021798   \n",
       "\n",
       "         jsim_time     josie  josie_time        a%  total_time  armadillo  \n",
       "0         0.000055  0.000000    0.000004  0.000000    0.012150   0.272864  \n",
       "1         0.000051  0.529730    0.000005  0.475248    0.008166   0.352441  \n",
       "2         0.000065  0.105263    0.000006  0.078125   14.486244   0.411786  \n",
       "3         0.000067  0.612150    0.000010  0.526316    9.460545   0.337523  \n",
       "4         0.000096  0.148936    0.000023  0.091424    0.091802   0.442848  \n",
       "...            ...       ...         ...       ...         ...        ...  \n",
       "9994371   0.000006  0.196078    0.000002  0.052632    0.001652   0.101239  \n",
       "9994372   0.000010  0.000000    0.000003  0.000000    0.000860  -0.054163  \n",
       "9994373   0.000024  0.257732    0.000006  0.035714    0.002311  -0.121819  \n",
       "9994374   0.000010  0.010309    0.000003  0.100000    0.001717   0.264201  \n",
       "9994375   0.000020  0.082474    0.000005  0.100000    0.001408   0.058705  \n",
       "\n",
       "[9994376 rows x 10 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "klkl[klkl['armadillo']<0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo</th>\n",
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       "  <tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [id, r_id, s_id, jsim, jsim_time, josie, josie_time, a%, total_time, armadillo]\n",
       "Index: []"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "klkl[klkl['armadillo']<0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>id</th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>object_csv_licensed.zip_appidsvc.dll.csv__3865...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012150</td>\n",
       "      <td>0.272864</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000001</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>then_csv_licensed.zip_FOXNEWS.201210.csv</td>\n",
       "      <td>0.064431</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.529730</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.475248</td>\n",
       "      <td>0.008166</td>\n",
       "      <td>0.352441</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8000002</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>id_csv_licensed.zip_0021700946_4.csv</td>\n",
       "      <td>0.015710</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>14.486244</td>\n",
       "      <td>0.411786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000003</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH2V01J.csv</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.612150</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.460545</td>\n",
       "      <td>0.337523</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8000004</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>living_thing_csv_licensed.zip_mrswunschel.csv</td>\n",
       "      <td>0.056291</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.148936</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.091802</td>\n",
       "      <td>0.442848</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9994371</th>\n",
       "      <td>6999995</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>0.101239</td>\n",
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       "      <th>9994372</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>9994373</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>9994374</th>\n",
       "      <td>6999998</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_alg.exe.csv__417245484...</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.264201</td>\n",
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       "    <tr>\n",
       "      <th>9994375</th>\n",
       "      <td>6999999</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>data_rate_csv_licensed.zip_14-15_32.csv</td>\n",
       "      <td>0.021798</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.058705</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9994376 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "              id                                           r_id  \\\n",
       "0        8000000  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "1        8000001  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "2        8000002  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "3        8000003  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "4        8000004  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "...          ...                                            ...   \n",
       "9994371  6999995   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994372        0                                              0   \n",
       "9994373        0                                              0   \n",
       "9994374  6999998   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994375  6999999   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "\n",
       "                                                      s_id      jsim  \\\n",
       "0        object_csv_licensed.zip_appidsvc.dll.csv__3865...  0.000000   \n",
       "1                 then_csv_licensed.zip_FOXNEWS.201210.csv  0.064431   \n",
       "2                     id_csv_licensed.zip_0021700946_4.csv  0.015710   \n",
       "3            seek_time_csv_licensed.zip_PL2331LAH2V01J.csv  0.086355   \n",
       "4            living_thing_csv_licensed.zip_mrswunschel.csv  0.056291   \n",
       "...                                                    ...       ...   \n",
       "9994371      seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv  0.072464   \n",
       "9994372                                                  0  0.000000   \n",
       "9994373                                                  0  0.000000   \n",
       "9994374  object_csv_licensed.zip_alg.exe.csv__417245484...  0.004184   \n",
       "9994375            data_rate_csv_licensed.zip_14-15_32.csv  0.021798   \n",
       "\n",
       "         jsim_time     josie  josie_time        a%  total_time  armadillo  \n",
       "0         0.000055  0.000000    0.000004  0.000000    0.012150   0.272864  \n",
       "1         0.000051  0.529730    0.000005  0.475248    0.008166   0.352441  \n",
       "2         0.000065  0.105263    0.000006  0.078125   14.486244   0.411786  \n",
       "3         0.000067  0.612150    0.000010  0.526316    9.460545   0.337523  \n",
       "4         0.000096  0.148936    0.000023  0.091424    0.091802   0.442848  \n",
       "...            ...       ...         ...       ...         ...        ...  \n",
       "9994371   0.000006  0.196078    0.000002  0.052632    0.001652   0.101239  \n",
       "9994372   0.000000  0.000000    0.000000  0.000000    0.000000   0.000000  \n",
       "9994373   0.000000  0.000000    0.000000  0.000000    0.000000   0.000000  \n",
       "9994374   0.000010  0.010309    0.000003  0.100000    0.001717   0.264201  \n",
       "9994375   0.000020  0.082474    0.000005  0.100000    0.001408   0.058705  \n",
       "\n",
       "[9994376 rows x 10 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "klkl['armadillo_ae'] = abs(klkl['armadillo']-klkl['a%'])\n",
    "klkl['josie_ae'] = abs(klkl['josie']-klkl['a%'])\n",
    "klkl['jsim_ae'] = abs(klkl['jsim']-klkl['a%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
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       "      <td>9.994376e+06</td>\n",
       "      <td>9.994376e+06</td>\n",
       "      <td>9.994376e+06</td>\n",
       "      <td>9.994376e+06</td>\n",
       "      <td>9.994376e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.820405e+06</td>\n",
       "      <td>2.621375e-02</td>\n",
       "      <td>4.497941e-04</td>\n",
       "      <td>1.030476e-01</td>\n",
       "      <td>2.543786e-05</td>\n",
       "      <td>8.583473e-02</td>\n",
       "      <td>8.991432e-01</td>\n",
       "      <td>1.305030e-01</td>\n",
       "      <td>9.080177e-02</td>\n",
       "      <td>6.218761e-02</td>\n",
       "      <td>6.318188e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.288266e+06</td>\n",
       "      <td>6.326302e-02</td>\n",
       "      <td>6.111657e-03</td>\n",
       "      <td>1.593761e-01</td>\n",
       "      <td>8.262407e-04</td>\n",
       "      <td>1.195803e-01</td>\n",
       "      <td>3.340424e+00</td>\n",
       "      <td>1.255692e-01</td>\n",
       "      <td>9.960108e-02</td>\n",
       "      <td>1.062008e-01</td>\n",
       "      <td>9.326611e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.552068e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.245209e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.430511e-06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.707409e-04</td>\n",
       "      <td>8.628992e-03</td>\n",
       "      <td>4.066784e-03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.433596e+06</td>\n",
       "      <td>5.319149e-03</td>\n",
       "      <td>2.264977e-05</td>\n",
       "      <td>2.564103e-02</td>\n",
       "      <td>3.337860e-06</td>\n",
       "      <td>4.545455e-02</td>\n",
       "      <td>1.680613e-03</td>\n",
       "      <td>1.066141e-01</td>\n",
       "      <td>6.202716e-02</td>\n",
       "      <td>1.964550e-02</td>\n",
       "      <td>1.896664e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.688219e+06</td>\n",
       "      <td>2.397260e-02</td>\n",
       "      <td>6.294250e-05</td>\n",
       "      <td>1.648000e-01</td>\n",
       "      <td>5.722046e-06</td>\n",
       "      <td>1.363636e-01</td>\n",
       "      <td>1.077628e-02</td>\n",
       "      <td>2.078944e-01</td>\n",
       "      <td>1.419920e-01</td>\n",
       "      <td>8.754209e-02</td>\n",
       "      <td>9.786325e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.999999e+06</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>9.883988e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.064767e-01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>6.400581e+01</td>\n",
       "      <td>9.999973e-01</td>\n",
       "      <td>9.804897e-01</td>\n",
       "      <td>9.921916e-01</td>\n",
       "      <td>9.873863e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          jsim     jsim_time         josie    josie_time  \\\n",
       "count  9.994376e+06  9.994376e+06  9.994376e+06  9.994376e+06  9.994376e+06   \n",
       "mean   3.820405e+06  2.621375e-02  4.497941e-04  1.030476e-01  2.543786e-05   \n",
       "std    3.288266e+06  6.326302e-02  6.111657e-03  1.593761e-01  8.262407e-04   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    2.552068e+05  0.000000e+00  5.245209e-06  0.000000e+00  1.430511e-06   \n",
       "50%    3.433596e+06  5.319149e-03  2.264977e-05  2.564103e-02  3.337860e-06   \n",
       "75%    6.688219e+06  2.397260e-02  6.294250e-05  1.648000e-01  5.722046e-06   \n",
       "max    9.999999e+06  1.000000e+00  9.883988e-01  1.000000e+00  4.064767e-01   \n",
       "\n",
       "                 a%    total_time     armadillo  armadillo_ae      josie_ae  \\\n",
       "count  9.994376e+06  9.994376e+06  9.994376e+06  9.994376e+06  9.994376e+06   \n",
       "mean   8.583473e-02  8.991432e-01  1.305030e-01  9.080177e-02  6.218761e-02   \n",
       "std    1.195803e-01  3.340424e+00  1.255692e-01  9.960108e-02  1.062008e-01   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    0.000000e+00  3.707409e-04  8.628992e-03  4.066784e-03  0.000000e+00   \n",
       "50%    4.545455e-02  1.680613e-03  1.066141e-01  6.202716e-02  1.964550e-02   \n",
       "75%    1.363636e-01  1.077628e-02  2.078944e-01  1.419920e-01  8.754209e-02   \n",
       "max    1.000000e+00  6.400581e+01  9.999973e-01  9.804897e-01  9.921916e-01   \n",
       "\n",
       "            jsim_ae  \n",
       "count  9.994376e+06  \n",
       "mean   6.318188e-02  \n",
       "std    9.326611e-02  \n",
       "min    0.000000e+00  \n",
       "25%    0.000000e+00  \n",
       "50%    1.896664e-02  \n",
       "75%    9.786325e-02  \n",
       "max    9.873863e-01  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klkl.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>armadillo_ae</th>\n",
       "      <th>josie_ae</th>\n",
       "      <th>jsim_ae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>object_csv_licensed.zip_appidsvc.dll.csv__3865...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012150</td>\n",
       "      <td>0.272864</td>\n",
       "      <td>0.272864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000001</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>then_csv_licensed.zip_FOXNEWS.201210.csv</td>\n",
       "      <td>0.064431</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.529730</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.475248</td>\n",
       "      <td>0.008166</td>\n",
       "      <td>0.352441</td>\n",
       "      <td>0.122806</td>\n",
       "      <td>0.054482</td>\n",
       "      <td>0.410816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8000002</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>id_csv_licensed.zip_0021700946_4.csv</td>\n",
       "      <td>0.015710</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>14.486244</td>\n",
       "      <td>0.411786</td>\n",
       "      <td>0.333661</td>\n",
       "      <td>0.027138</td>\n",
       "      <td>0.062415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000003</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH2V01J.csv</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.612150</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.460545</td>\n",
       "      <td>0.337523</td>\n",
       "      <td>0.188793</td>\n",
       "      <td>0.085834</td>\n",
       "      <td>0.439961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8000004</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>living_thing_csv_licensed.zip_mrswunschel.csv</td>\n",
       "      <td>0.056291</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.148936</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.091802</td>\n",
       "      <td>0.442848</td>\n",
       "      <td>0.351424</td>\n",
       "      <td>0.057512</td>\n",
       "      <td>0.035133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994371</th>\n",
       "      <td>6999995</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>0.101239</td>\n",
       "      <td>0.048608</td>\n",
       "      <td>0.143447</td>\n",
       "      <td>0.019832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994372</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994373</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994374</th>\n",
       "      <td>6999998</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_alg.exe.csv__417245484...</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.264201</td>\n",
       "      <td>0.164201</td>\n",
       "      <td>0.089691</td>\n",
       "      <td>0.095816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994375</th>\n",
       "      <td>6999999</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>data_rate_csv_licensed.zip_14-15_32.csv</td>\n",
       "      <td>0.021798</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.058705</td>\n",
       "      <td>0.041295</td>\n",
       "      <td>0.017526</td>\n",
       "      <td>0.078202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994376 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                           r_id  \\\n",
       "0        8000000  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "1        8000001  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "2        8000002  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "3        8000003  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "4        8000004  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "...          ...                                            ...   \n",
       "9994371  6999995   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994372        0                                              0   \n",
       "9994373        0                                              0   \n",
       "9994374  6999998   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994375  6999999   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "\n",
       "                                                      s_id      jsim  \\\n",
       "0        object_csv_licensed.zip_appidsvc.dll.csv__3865...  0.000000   \n",
       "1                 then_csv_licensed.zip_FOXNEWS.201210.csv  0.064431   \n",
       "2                     id_csv_licensed.zip_0021700946_4.csv  0.015710   \n",
       "3            seek_time_csv_licensed.zip_PL2331LAH2V01J.csv  0.086355   \n",
       "4            living_thing_csv_licensed.zip_mrswunschel.csv  0.056291   \n",
       "...                                                    ...       ...   \n",
       "9994371      seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv  0.072464   \n",
       "9994372                                                  0  0.000000   \n",
       "9994373                                                  0  0.000000   \n",
       "9994374  object_csv_licensed.zip_alg.exe.csv__417245484...  0.004184   \n",
       "9994375            data_rate_csv_licensed.zip_14-15_32.csv  0.021798   \n",
       "\n",
       "         jsim_time     josie  josie_time        a%  total_time  armadillo  \\\n",
       "0         0.000055  0.000000    0.000004  0.000000    0.012150   0.272864   \n",
       "1         0.000051  0.529730    0.000005  0.475248    0.008166   0.352441   \n",
       "2         0.000065  0.105263    0.000006  0.078125   14.486244   0.411786   \n",
       "3         0.000067  0.612150    0.000010  0.526316    9.460545   0.337523   \n",
       "4         0.000096  0.148936    0.000023  0.091424    0.091802   0.442848   \n",
       "...            ...       ...         ...       ...         ...        ...   \n",
       "9994371   0.000006  0.196078    0.000002  0.052632    0.001652   0.101239   \n",
       "9994372   0.000000  0.000000    0.000000  0.000000    0.000000   0.000000   \n",
       "9994373   0.000000  0.000000    0.000000  0.000000    0.000000   0.000000   \n",
       "9994374   0.000010  0.010309    0.000003  0.100000    0.001717   0.264201   \n",
       "9994375   0.000020  0.082474    0.000005  0.100000    0.001408   0.058705   \n",
       "\n",
       "         armadillo_ae  josie_ae   jsim_ae  \n",
       "0            0.272864  0.000000  0.000000  \n",
       "1            0.122806  0.054482  0.410816  \n",
       "2            0.333661  0.027138  0.062415  \n",
       "3            0.188793  0.085834  0.439961  \n",
       "4            0.351424  0.057512  0.035133  \n",
       "...               ...       ...       ...  \n",
       "9994371      0.048608  0.143447  0.019832  \n",
       "9994372      0.000000  0.000000  0.000000  \n",
       "9994373      0.000000  0.000000  0.000000  \n",
       "9994374      0.164201  0.089691  0.095816  \n",
       "9994375      0.041295  0.017526  0.078202  \n",
       "\n",
       "[9994376 rows x 13 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import ndcg_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.989780896240407"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(y_true=np.array(klkl[['a%']]).reshape(1,-1), y_score=np.array(klkl[['jsim']]).reshape(1,-1), k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9655765627745595"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(y_true=np.array(klkl[['a%']]).reshape(1,-1), y_score=np.array(klkl[['josie']]).reshape(1,-1), k=10132123123123123123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndcg_score(y_true=np.array(klkl[['a%']]).reshape(1,-1), y_score=np.array(klkl[['armadillo']]).reshape(1,-1), k=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>id</th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>armadillo_ae</th>\n",
       "      <th>josie_ae</th>\n",
       "      <th>jsim_ae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000003</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH2V01J.csv</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.612150</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.460545</td>\n",
       "      <td>0.337523</td>\n",
       "      <td>0.188793</td>\n",
       "      <td>0.085834</td>\n",
       "      <td>0.439961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8000008</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGSMPGH.csv</td>\n",
       "      <td>0.309126</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.555091</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.029824</td>\n",
       "      <td>0.396680</td>\n",
       "      <td>0.129636</td>\n",
       "      <td>0.028775</td>\n",
       "      <td>0.217190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8000015</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGTPW4H.csv</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.509887</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.513765</td>\n",
       "      <td>12.692080</td>\n",
       "      <td>0.278364</td>\n",
       "      <td>0.235400</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.174479</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>8000021</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGTLYBH.csv</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.538354</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.028604</td>\n",
       "      <td>0.367011</td>\n",
       "      <td>0.159305</td>\n",
       "      <td>0.012038</td>\n",
       "      <td>0.162679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>8000052</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH9KBHJ.csv</td>\n",
       "      <td>0.017277</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.657895</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.478164</td>\n",
       "      <td>0.500360</td>\n",
       "      <td>0.025956</td>\n",
       "      <td>0.131579</td>\n",
       "      <td>0.509039</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991907</th>\n",
       "      <td>6997531</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_MSNBC.201702_1.csv</td>\n",
       "      <td>0.374194</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.597938</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000575</td>\n",
       "      <td>0.421563</td>\n",
       "      <td>0.078437</td>\n",
       "      <td>0.097938</td>\n",
       "      <td>0.125806</td>\n",
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       "    <tr>\n",
       "      <th>9992244</th>\n",
       "      <td>6997868</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>question_time_csv_licensed.zip_MSNBC.201204_2.csv</td>\n",
       "      <td>0.469945</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.886598</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000659</td>\n",
       "      <td>0.144873</td>\n",
       "      <td>0.355127</td>\n",
       "      <td>0.386598</td>\n",
       "      <td>0.030055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993481</th>\n",
       "      <td>6999105</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>running_time_csv_licensed.zip_CNN.201111_1.csv</td>\n",
       "      <td>0.326389</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.233304</td>\n",
       "      <td>0.266696</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993920</th>\n",
       "      <td>6999544</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>whole_csv_licensed.zip_52f.0.testMap_1.csv</td>\n",
       "      <td>0.432749</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.762887</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.336223</td>\n",
       "      <td>0.163777</td>\n",
       "      <td>0.262887</td>\n",
       "      <td>0.067251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994192</th>\n",
       "      <td>6999816</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_39f.4.testMap_1.csv</td>\n",
       "      <td>0.281481</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000652</td>\n",
       "      <td>0.303470</td>\n",
       "      <td>0.196530</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.218519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>256184 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                           r_id  \\\n",
       "3        8000003  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "8        8000008  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "15       8000015  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "21       8000021  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "52       8000052  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "...          ...                                            ...   \n",
       "9991907  6997531   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9992244  6997868   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9993481  6999105   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9993920  6999544   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994192  6999816   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "\n",
       "                                                      s_id      jsim  \\\n",
       "3            seek_time_csv_licensed.zip_PL2331LAH2V01J.csv  0.086355   \n",
       "8            seek_time_csv_licensed.zip_PL1331LAGSMPGH.csv  0.309126   \n",
       "15           seek_time_csv_licensed.zip_PL1331LAGTPW4H.csv  0.339286   \n",
       "21           seek_time_csv_licensed.zip_PL1331LAGTLYBH.csv  0.363636   \n",
       "52           seek_time_csv_licensed.zip_PL2331LAH9KBHJ.csv  0.017277   \n",
       "...                                                    ...       ...   \n",
       "9991907      seek_time_csv_licensed.zip_MSNBC.201702_1.csv  0.374194   \n",
       "9992244  question_time_csv_licensed.zip_MSNBC.201204_2.csv  0.469945   \n",
       "9993481     running_time_csv_licensed.zip_CNN.201111_1.csv  0.326389   \n",
       "9993920         whole_csv_licensed.zip_52f.0.testMap_1.csv  0.432749   \n",
       "9994192        object_csv_licensed.zip_39f.4.testMap_1.csv  0.281481   \n",
       "\n",
       "         jsim_time     josie  josie_time        a%  total_time  armadillo  \\\n",
       "3         0.000067  0.612150    0.000010  0.526316    9.460545   0.337523   \n",
       "8         0.000210  0.555091    0.000063  0.526316    0.029824   0.396680   \n",
       "15        0.000174  0.509887    0.000059  0.513765   12.692080   0.278364   \n",
       "21        0.000177  0.538354    0.000062  0.526316    0.028604   0.367011   \n",
       "52        0.000042  0.657895    0.000003  0.526316    9.478164   0.500360   \n",
       "...            ...       ...         ...       ...         ...        ...   \n",
       "9991907   0.000008  0.597938    0.000003  0.500000    0.000575   0.421563   \n",
       "9992244   0.000024  0.886598    0.000004  0.500000    0.000659   0.144873   \n",
       "9993481   0.000009  0.500000    0.000004  0.500000    0.000609   0.233304   \n",
       "9993920   0.000011  0.762887    0.000003  0.500000    0.000652   0.336223   \n",
       "9994192   0.000010  0.500000    0.000004  0.500000    0.000652   0.303470   \n",
       "\n",
       "         armadillo_ae  josie_ae   jsim_ae  \n",
       "3            0.188793  0.085834  0.439961  \n",
       "8            0.129636  0.028775  0.217190  \n",
       "15           0.235400  0.003878  0.174479  \n",
       "21           0.159305  0.012038  0.162679  \n",
       "52           0.025956  0.131579  0.509039  \n",
       "...               ...       ...       ...  \n",
       "9991907      0.078437  0.097938  0.125806  \n",
       "9992244      0.355127  0.386598  0.030055  \n",
       "9993481      0.266696  0.000000  0.173611  \n",
       "9993920      0.163777  0.262887  0.067251  \n",
       "9994192      0.196530  0.000000  0.218519  \n",
       "\n",
       "[256184 rows x 13 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klkl[klkl['a%']>=0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>armadillo_ae</th>\n",
       "      <th>josie_ae</th>\n",
       "      <th>jsim_ae</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8000000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>object_csv_licensed.zip_appidsvc.dll.csv__3865...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012150</td>\n",
       "      <td>0.272864</td>\n",
       "      <td>0.272864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8000001</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>then_csv_licensed.zip_FOXNEWS.201210.csv</td>\n",
       "      <td>0.064431</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.529730</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.475248</td>\n",
       "      <td>0.008166</td>\n",
       "      <td>0.352441</td>\n",
       "      <td>0.122806</td>\n",
       "      <td>0.054482</td>\n",
       "      <td>0.410816</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8000002</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>id_csv_licensed.zip_0021700946_4.csv</td>\n",
       "      <td>0.015710</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.078125</td>\n",
       "      <td>14.486244</td>\n",
       "      <td>0.411786</td>\n",
       "      <td>0.333661</td>\n",
       "      <td>0.027138</td>\n",
       "      <td>0.062415</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8000003</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAH2V01J.csv</td>\n",
       "      <td>0.086355</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.612150</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>9.460545</td>\n",
       "      <td>0.337523</td>\n",
       "      <td>0.188793</td>\n",
       "      <td>0.085834</td>\n",
       "      <td>0.439961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8000004</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv</td>\n",
       "      <td>living_thing_csv_licensed.zip_mrswunschel.csv</td>\n",
       "      <td>0.056291</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.148936</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.091424</td>\n",
       "      <td>0.091802</td>\n",
       "      <td>0.442848</td>\n",
       "      <td>0.351424</td>\n",
       "      <td>0.057512</td>\n",
       "      <td>0.035133</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994371</th>\n",
       "      <td>6999995</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv</td>\n",
       "      <td>0.072464</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.196078</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.001652</td>\n",
       "      <td>0.101239</td>\n",
       "      <td>0.048608</td>\n",
       "      <td>0.143447</td>\n",
       "      <td>0.019832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994372</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994373</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994374</th>\n",
       "      <td>6999998</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>object_csv_licensed.zip_alg.exe.csv__417245484...</td>\n",
       "      <td>0.004184</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.010309</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001717</td>\n",
       "      <td>0.264201</td>\n",
       "      <td>0.164201</td>\n",
       "      <td>0.089691</td>\n",
       "      <td>0.095816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994375</th>\n",
       "      <td>6999999</td>\n",
       "      <td>id_csv_licensed.zip_dtb_delivery_fee_105.csv</td>\n",
       "      <td>data_rate_csv_licensed.zip_14-15_32.csv</td>\n",
       "      <td>0.021798</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.082474</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.001408</td>\n",
       "      <td>0.058705</td>\n",
       "      <td>0.041295</td>\n",
       "      <td>0.017526</td>\n",
       "      <td>0.078202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9902606 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                                           r_id  \\\n",
       "0        8000000  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "1        8000001  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "2        8000002  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "3        8000003  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "4        8000004  seek_time_csv_licensed.zip_PL1331LAGT9BXH.csv   \n",
       "...          ...                                            ...   \n",
       "9994371  6999995   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994372        0                                              0   \n",
       "9994373        0                                              0   \n",
       "9994374  6999998   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "9994375  6999999   id_csv_licensed.zip_dtb_delivery_fee_105.csv   \n",
       "\n",
       "                                                      s_id      jsim  \\\n",
       "0        object_csv_licensed.zip_appidsvc.dll.csv__3865...  0.000000   \n",
       "1                 then_csv_licensed.zip_FOXNEWS.201210.csv  0.064431   \n",
       "2                     id_csv_licensed.zip_0021700946_4.csv  0.015710   \n",
       "3            seek_time_csv_licensed.zip_PL2331LAH2V01J.csv  0.086355   \n",
       "4            living_thing_csv_licensed.zip_mrswunschel.csv  0.056291   \n",
       "...                                                    ...       ...   \n",
       "9994371      seek_time_csv_licensed.zip_PL1331LAHBZYEH.csv  0.072464   \n",
       "9994372                                                  0  0.000000   \n",
       "9994373                                                  0  0.000000   \n",
       "9994374  object_csv_licensed.zip_alg.exe.csv__417245484...  0.004184   \n",
       "9994375            data_rate_csv_licensed.zip_14-15_32.csv  0.021798   \n",
       "\n",
       "         jsim_time     josie  josie_time        a%  total_time  armadillo  \\\n",
       "0         0.000055  0.000000    0.000004  0.000000    0.012150   0.272864   \n",
       "1         0.000051  0.529730    0.000005  0.475248    0.008166   0.352441   \n",
       "2         0.000065  0.105263    0.000006  0.078125   14.486244   0.411786   \n",
       "3         0.000067  0.612150    0.000010  0.526316    9.460545   0.337523   \n",
       "4         0.000096  0.148936    0.000023  0.091424    0.091802   0.442848   \n",
       "...            ...       ...         ...       ...         ...        ...   \n",
       "9994371   0.000006  0.196078    0.000002  0.052632    0.001652   0.101239   \n",
       "9994372   0.000000  0.000000    0.000000  0.000000    0.000000   0.000000   \n",
       "9994373   0.000000  0.000000    0.000000  0.000000    0.000000   0.000000   \n",
       "9994374   0.000010  0.010309    0.000003  0.100000    0.001717   0.264201   \n",
       "9994375   0.000020  0.082474    0.000005  0.100000    0.001408   0.058705   \n",
       "\n",
       "         armadillo_ae  josie_ae   jsim_ae  \n",
       "0            0.272864  0.000000  0.000000  \n",
       "1            0.122806  0.054482  0.410816  \n",
       "2            0.333661  0.027138  0.062415  \n",
       "3            0.188793  0.085834  0.439961  \n",
       "4            0.351424  0.057512  0.035133  \n",
       "...               ...       ...       ...  \n",
       "9994371      0.048608  0.143447  0.019832  \n",
       "9994372      0.000000  0.000000  0.000000  \n",
       "9994373      0.000000  0.000000  0.000000  \n",
       "9994374      0.164201  0.089691  0.095816  \n",
       "9994375      0.041295  0.017526  0.078202  \n",
       "\n",
       "[9902606 rows x 13 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klkl[klkl['armadillo']<0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "60\n",
      "70\n",
      "80\n",
      "90\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "for k in range(0, 101, 10):\n",
    "    if k == 0:\n",
    "        k=1\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>query_table</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>overlap_set_sim</th>\n",
       "      <th>jsim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>id_csv_licensed.zip_4204.csv</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175088</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>id_csv_licensed.zip_7546.csv</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.135857</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>then_csv_licensed.zip_A00-2018_sweta_119.csv</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>processing_time_csv_licensed.zip_P05-1013.annv...</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>object_csv_licensed.zip_actualtWalkTwo2020_12_...</td>\n",
       "      <td>0.062271</td>\n",
       "      <td>0.178842</td>\n",
       "      <td>0.385524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11995</th>\n",
       "      <td>10000</td>\n",
       "      <td>seek_time_csv_licensed.zip_WD-WX41D94RNF01.csv</td>\n",
       "      <td>0.916310</td>\n",
       "      <td>0.967505</td>\n",
       "      <td>0.960500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>10000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL1331LAGSMJAH.csv</td>\n",
       "      <td>0.920862</td>\n",
       "      <td>0.967649</td>\n",
       "      <td>0.956487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>10000</td>\n",
       "      <td>parent_csv_licensed.zip_Configuration1_window8...</td>\n",
       "      <td>0.863645</td>\n",
       "      <td>0.946851</td>\n",
       "      <td>0.916729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>10000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAHDEV7J.csv</td>\n",
       "      <td>0.949996</td>\n",
       "      <td>0.967608</td>\n",
       "      <td>0.948832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>10000</td>\n",
       "      <td>seek_time_csv_licensed.zip_PL2331LAHBED0J.csv</td>\n",
       "      <td>0.946681</td>\n",
       "      <td>0.968036</td>\n",
       "      <td>0.946385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           k                                        query_table  armadillo  \\\n",
       "0          1                       id_csv_licensed.zip_4204.csv   0.000000   \n",
       "1          1                       id_csv_licensed.zip_7546.csv   0.250000   \n",
       "2          1       then_csv_licensed.zip_A00-2018_sweta_119.csv   1.000000   \n",
       "3          1  processing_time_csv_licensed.zip_P05-1013.annv...   0.727273   \n",
       "4          1  object_csv_licensed.zip_actualtWalkTwo2020_12_...   0.062271   \n",
       "...      ...                                                ...        ...   \n",
       "11995  10000     seek_time_csv_licensed.zip_WD-WX41D94RNF01.csv   0.916310   \n",
       "11996  10000      seek_time_csv_licensed.zip_PL1331LAGSMJAH.csv   0.920862   \n",
       "11997  10000  parent_csv_licensed.zip_Configuration1_window8...   0.863645   \n",
       "11998  10000      seek_time_csv_licensed.zip_PL2331LAHDEV7J.csv   0.949996   \n",
       "11999  10000      seek_time_csv_licensed.zip_PL2331LAHBED0J.csv   0.946681   \n",
       "\n",
       "       overlap_set_sim      jsim  \n",
       "0             0.175088  0.500000  \n",
       "1             0.135857  0.500000  \n",
       "2             1.000000  1.000000  \n",
       "3             0.380585  1.000000  \n",
       "4             0.178842  0.385524  \n",
       "...                ...       ...  \n",
       "11995         0.967505  0.960500  \n",
       "11996         0.967649  0.956487  \n",
       "11997         0.946851  0.916729  \n",
       "11998         0.967608  0.948832  \n",
       "11999         0.968036  0.946385  \n",
       "\n",
       "[12000 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "klk = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_querying/ndcg_data.csv')\n",
    "klk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>k</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>overlap_set_sim</th>\n",
       "      <th>jsim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.866460</td>\n",
       "      <td>0.938426</td>\n",
       "      <td>0.932232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080687</td>\n",
       "      <td>0.053472</td>\n",
       "      <td>0.066544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.833468</td>\n",
       "      <td>0.925382</td>\n",
       "      <td>0.913890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.889358</td>\n",
       "      <td>0.955955</td>\n",
       "      <td>0.953223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.916705</td>\n",
       "      <td>0.965844</td>\n",
       "      <td>0.975378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10000.0</td>\n",
       "      <td>0.987765</td>\n",
       "      <td>0.999997</td>\n",
       "      <td>0.999895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             k    armadillo  overlap_set_sim         jsim\n",
       "count   1000.0  1000.000000      1000.000000  1000.000000\n",
       "mean   10000.0     0.866460         0.938426     0.932232\n",
       "std        0.0     0.080687         0.053472     0.066544\n",
       "min    10000.0     0.000000         0.000000     0.000000\n",
       "25%    10000.0     0.833468         0.925382     0.913890\n",
       "50%    10000.0     0.889358         0.955955     0.953223\n",
       "75%    10000.0     0.916705         0.965844     0.975378\n",
       "max    10000.0     0.987765         0.999997     0.999895"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "klk[klk['k']==10000].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>198.7650.csv</td>\n",
       "      <td>198.97756.csv</td>\n",
       "      <td>88</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000004</td>\n",
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       "      <td>3.205166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>309.87528.csv</td>\n",
       "      <td>309.87529.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.35588.csv</td>\n",
       "      <td>574.6628.csv</td>\n",
       "      <td>70</td>\n",
       "      <td>0.439716</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.105769</td>\n",
       "      <td>3.087128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225.64426.csv</td>\n",
       "      <td>401.116864.csv</td>\n",
       "      <td>17</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>441.155903.csv</td>\n",
       "      <td>618.72349.csv</td>\n",
       "      <td>110</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>3.848327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>245.13162.csv</td>\n",
       "      <td>377.69517.csv</td>\n",
       "      <td>842</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.058065</td>\n",
       "      <td>11.562310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>392.26789.csv</td>\n",
       "      <td>447.114168.csv</td>\n",
       "      <td>84</td>\n",
       "      <td>0.491071</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.126973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>382.160.csv</td>\n",
       "      <td>440.84900.csv</td>\n",
       "      <td>12</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>322.68550.csv</td>\n",
       "      <td>488.146192.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>422.109201.csv</td>\n",
       "      <td>422.141642.csv</td>\n",
       "      <td>21</td>\n",
       "      <td>0.359551</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.581818</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.005207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 r_id            s_id  seeds      jsim  jsim_time     josie  \\\n",
       "0        198.7650.csv   198.97756.csv     88  0.583333   0.000004  0.736842   \n",
       "1       309.87528.csv   309.87529.csv      4  0.280000   0.000003  0.437500   \n",
       "2       124.35588.csv    574.6628.csv     70  0.439716   0.000014  0.639175   \n",
       "3       225.64426.csv  401.116864.csv     17  0.692308   0.000010  0.818182   \n",
       "4      441.155903.csv   618.72349.csv    110  0.487179   0.000007  0.791667   \n",
       "...               ...             ...    ...       ...        ...       ...   \n",
       "59995   245.13162.csv   377.69517.csv    842  0.875000   0.000009  0.933333   \n",
       "59996   392.26789.csv  447.114168.csv     84  0.491071   0.000014  0.687500   \n",
       "59997     382.160.csv   440.84900.csv     12  0.300000   0.000005  0.500000   \n",
       "59998   322.68550.csv  488.146192.csv      4  0.428571   0.000003  0.600000   \n",
       "59999  422.109201.csv  422.141642.csv     21  0.359551   0.000009  0.581818   \n",
       "\n",
       "       josie_time        a%  total_time  \n",
       "0        0.000001  0.215385    3.205166  \n",
       "1        0.000001  0.250000    0.000202  \n",
       "2        0.000004  0.105769    3.087128  \n",
       "3        0.000005  0.800000    0.000886  \n",
       "4        0.000003  0.281250    3.848327  \n",
       "...           ...       ...         ...  \n",
       "59995    0.000003  0.058065   11.562310  \n",
       "59996    0.000005  0.200000    3.126973  \n",
       "59997    0.000002  0.333333    0.000358  \n",
       "59998    0.000002  0.400000    0.000246  \n",
       "59999    0.000003  0.111111    0.005207  \n",
       "\n",
       "[60000 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo_wikitables</th>\n",
       "      <th>armadillo_wikitables_AE</th>\n",
       "      <th>armadillo_gittables</th>\n",
       "      <th>armadillo_gittables_AE</th>\n",
       "      <th>o_set_sim_AE</th>\n",
       "      <th>jsim_AE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>198.7650.csv</td>\n",
       "      <td>198.97756.csv</td>\n",
       "      <td>88</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>3.205166</td>\n",
       "      <td>0.162806</td>\n",
       "      <td>0.052578</td>\n",
       "      <td>0.175342</td>\n",
       "      <td>0.040043</td>\n",
       "      <td>0.521457</td>\n",
       "      <td>0.367949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>309.87528.csv</td>\n",
       "      <td>309.87529.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.359683</td>\n",
       "      <td>0.109683</td>\n",
       "      <td>0.668466</td>\n",
       "      <td>0.418466</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.35588.csv</td>\n",
       "      <td>574.6628.csv</td>\n",
       "      <td>70</td>\n",
       "      <td>0.439716</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.105769</td>\n",
       "      <td>3.087128</td>\n",
       "      <td>0.357759</td>\n",
       "      <td>0.251990</td>\n",
       "      <td>0.128538</td>\n",
       "      <td>0.022769</td>\n",
       "      <td>0.533406</td>\n",
       "      <td>0.333947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225.64426.csv</td>\n",
       "      <td>401.116864.csv</td>\n",
       "      <td>17</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>0.839514</td>\n",
       "      <td>0.039514</td>\n",
       "      <td>0.953886</td>\n",
       "      <td>0.153886</td>\n",
       "      <td>0.018182</td>\n",
       "      <td>0.107692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>441.155903.csv</td>\n",
       "      <td>618.72349.csv</td>\n",
       "      <td>110</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>3.848327</td>\n",
       "      <td>0.191752</td>\n",
       "      <td>0.089498</td>\n",
       "      <td>0.333093</td>\n",
       "      <td>0.051843</td>\n",
       "      <td>0.510417</td>\n",
       "      <td>0.205929</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>245.13162.csv</td>\n",
       "      <td>377.69517.csv</td>\n",
       "      <td>842</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.058065</td>\n",
       "      <td>11.562310</td>\n",
       "      <td>0.695972</td>\n",
       "      <td>0.637908</td>\n",
       "      <td>0.964013</td>\n",
       "      <td>0.905948</td>\n",
       "      <td>0.875269</td>\n",
       "      <td>0.816935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>392.26789.csv</td>\n",
       "      <td>447.114168.csv</td>\n",
       "      <td>84</td>\n",
       "      <td>0.491071</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.126973</td>\n",
       "      <td>0.565953</td>\n",
       "      <td>0.365953</td>\n",
       "      <td>0.340119</td>\n",
       "      <td>0.140119</td>\n",
       "      <td>0.487500</td>\n",
       "      <td>0.291071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>382.160.csv</td>\n",
       "      <td>440.84900.csv</td>\n",
       "      <td>12</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.502552</td>\n",
       "      <td>0.169219</td>\n",
       "      <td>0.671056</td>\n",
       "      <td>0.337723</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>322.68550.csv</td>\n",
       "      <td>488.146192.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.515062</td>\n",
       "      <td>0.115062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.028571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>422.109201.csv</td>\n",
       "      <td>422.141642.csv</td>\n",
       "      <td>21</td>\n",
       "      <td>0.359551</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.581818</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.005207</td>\n",
       "      <td>0.053041</td>\n",
       "      <td>0.058070</td>\n",
       "      <td>0.190264</td>\n",
       "      <td>0.079153</td>\n",
       "      <td>0.470707</td>\n",
       "      <td>0.248439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 r_id            s_id  seeds      jsim  jsim_time     josie  \\\n",
       "0        198.7650.csv   198.97756.csv     88  0.583333   0.000004  0.736842   \n",
       "1       309.87528.csv   309.87529.csv      4  0.280000   0.000003  0.437500   \n",
       "2       124.35588.csv    574.6628.csv     70  0.439716   0.000014  0.639175   \n",
       "3       225.64426.csv  401.116864.csv     17  0.692308   0.000010  0.818182   \n",
       "4      441.155903.csv   618.72349.csv    110  0.487179   0.000007  0.791667   \n",
       "...               ...             ...    ...       ...        ...       ...   \n",
       "59995   245.13162.csv   377.69517.csv    842  0.875000   0.000009  0.933333   \n",
       "59996   392.26789.csv  447.114168.csv     84  0.491071   0.000014  0.687500   \n",
       "59997     382.160.csv   440.84900.csv     12  0.300000   0.000005  0.500000   \n",
       "59998   322.68550.csv  488.146192.csv      4  0.428571   0.000003  0.600000   \n",
       "59999  422.109201.csv  422.141642.csv     21  0.359551   0.000009  0.581818   \n",
       "\n",
       "       josie_time        a%  total_time  armadillo_wikitables  \\\n",
       "0        0.000001  0.215385    3.205166              0.162806   \n",
       "1        0.000001  0.250000    0.000202              0.359683   \n",
       "2        0.000004  0.105769    3.087128              0.357759   \n",
       "3        0.000005  0.800000    0.000886              0.839514   \n",
       "4        0.000003  0.281250    3.848327              0.191752   \n",
       "...           ...       ...         ...                   ...   \n",
       "59995    0.000003  0.058065   11.562310              0.695972   \n",
       "59996    0.000005  0.200000    3.126973              0.565953   \n",
       "59997    0.000002  0.333333    0.000358              0.502552   \n",
       "59998    0.000002  0.400000    0.000246              0.515062   \n",
       "59999    0.000003  0.111111    0.005207              0.053041   \n",
       "\n",
       "       armadillo_wikitables_AE  armadillo_gittables  armadillo_gittables_AE  \\\n",
       "0                     0.052578             0.175342                0.040043   \n",
       "1                     0.109683             0.668466                0.418466   \n",
       "2                     0.251990             0.128538                0.022769   \n",
       "3                     0.039514             0.953886                0.153886   \n",
       "4                     0.089498             0.333093                0.051843   \n",
       "...                        ...                  ...                     ...   \n",
       "59995                 0.637908             0.964013                0.905948   \n",
       "59996                 0.365953             0.340119                0.140119   \n",
       "59997                 0.169219             0.671056                0.337723   \n",
       "59998                 0.115062             0.000000                0.400000   \n",
       "59999                 0.058070             0.190264                0.079153   \n",
       "\n",
       "       o_set_sim_AE   jsim_AE  \n",
       "0          0.521457  0.367949  \n",
       "1          0.187500  0.030000  \n",
       "2          0.533406  0.333947  \n",
       "3          0.018182  0.107692  \n",
       "4          0.510417  0.205929  \n",
       "...             ...       ...  \n",
       "59995      0.875269  0.816935  \n",
       "59996      0.487500  0.291071  \n",
       "59997      0.166667  0.033333  \n",
       "59998      0.200000  0.028571  \n",
       "59999      0.470707  0.248439  \n",
       "\n",
       "[60000 rows x 15 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dd = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/effectiveness_evaluation.csv')\n",
    "dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd['o_set_sim_AE'] = abs(dd['josie']-dd['a%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dd['jsim_AE'] = abs(dd['jsim']-dd['a%'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>armadillo_wikitables</th>\n",
       "      <th>armadillo_wikitables_AE</th>\n",
       "      <th>armadillo_gittables</th>\n",
       "      <th>armadillo_gittables_AE</th>\n",
       "      <th>o_set_sim_AE</th>\n",
       "      <th>jsim_AE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.941967</td>\n",
       "      <td>0.647238</td>\n",
       "      <td>5.192626e-06</td>\n",
       "      <td>0.820302</td>\n",
       "      <td>1.992679e-06</td>\n",
       "      <td>0.496343</td>\n",
       "      <td>0.914817</td>\n",
       "      <td>0.562836</td>\n",
       "      <td>0.119146</td>\n",
       "      <td>0.526013</td>\n",
       "      <td>0.223159</td>\n",
       "      <td>0.325091</td>\n",
       "      <td>0.219903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>160.590663</td>\n",
       "      <td>0.203651</td>\n",
       "      <td>3.881562e-06</td>\n",
       "      <td>0.143001</td>\n",
       "      <td>1.404466e-06</td>\n",
       "      <td>0.291760</td>\n",
       "      <td>2.065447</td>\n",
       "      <td>0.266788</td>\n",
       "      <td>0.142798</td>\n",
       "      <td>0.348036</td>\n",
       "      <td>0.191419</td>\n",
       "      <td>0.252332</td>\n",
       "      <td>0.192537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>0.189655</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>3.099442e-06</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.369021</td>\n",
       "      <td>0.019978</td>\n",
       "      <td>0.199018</td>\n",
       "      <td>0.060371</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>4.291534e-06</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>1.668930e-06</td>\n",
       "      <td>0.498705</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.563093</td>\n",
       "      <td>0.075787</td>\n",
       "      <td>0.521265</td>\n",
       "      <td>0.178949</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.169312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>5.960464e-06</td>\n",
       "      <td>0.945205</td>\n",
       "      <td>2.384186e-06</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.174530</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.163311</td>\n",
       "      <td>0.879076</td>\n",
       "      <td>0.340711</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1428.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.418591e-04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.029274e-05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.229370</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.984375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              seeds          jsim     jsim_time         josie    josie_time  \\\n",
       "count  60000.000000  60000.000000  6.000000e+04  60000.000000  6.000000e+04   \n",
       "mean      67.941967      0.647238  5.192626e-06      0.820302  1.992679e-06   \n",
       "std      160.590663      0.203651  3.881562e-06      0.143001  1.404466e-06   \n",
       "min        1.000000      0.068627  2.384186e-07      0.189655  4.768372e-07   \n",
       "25%        9.000000      0.488372  3.099442e-06      0.720000  1.192093e-06   \n",
       "50%       22.000000      0.634615  4.291534e-06      0.842105  1.668930e-06   \n",
       "75%       41.000000      0.794118  5.960464e-06      0.945205  2.384186e-06   \n",
       "max     1428.000000      1.000000  1.418591e-04      1.000000  4.029274e-05   \n",
       "\n",
       "                 a%    total_time  armadillo_wikitables  \\\n",
       "count  60000.000000  60000.000000          60000.000000   \n",
       "mean       0.496343      0.914817              0.562836   \n",
       "std        0.291760      2.065447              0.266788   \n",
       "min        0.011494      0.000091              0.000000   \n",
       "25%        0.250000      0.000334              0.369021   \n",
       "50%        0.498705      0.000616              0.563093   \n",
       "75%        0.750000      0.174530              0.769231   \n",
       "max        1.000000     37.229370              1.000000   \n",
       "\n",
       "       armadillo_wikitables_AE  armadillo_gittables  armadillo_gittables_AE  \\\n",
       "count             60000.000000         60000.000000            60000.000000   \n",
       "mean                  0.119146             0.526013                0.223159   \n",
       "std                   0.142798             0.348036                0.191419   \n",
       "min                   0.000000             0.000000                0.000000   \n",
       "25%                   0.019978             0.199018                0.060371   \n",
       "50%                   0.075787             0.521265                0.178949   \n",
       "75%                   0.163311             0.879076                0.340711   \n",
       "max                   1.000000             1.000000                1.000000   \n",
       "\n",
       "       o_set_sim_AE       jsim_AE  \n",
       "count  60000.000000  60000.000000  \n",
       "mean       0.325091      0.219903  \n",
       "std        0.252332      0.192537  \n",
       "min        0.000000      0.000000  \n",
       "25%        0.100000      0.083333  \n",
       "50%        0.333333      0.169312  \n",
       "75%        0.500000      0.300000  \n",
       "max        0.984375      0.984375  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as  pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "      <th>graphs_generation_time</th>\n",
       "      <th>embeddings_generation_time</th>\n",
       "      <th>cos_sim_time_armadillo</th>\n",
       "      <th>armadillo_total_time</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>AE_armadillo</th>\n",
       "      <th>overlap_computations_repeated_armadillo</th>\n",
       "      <th>overlap_computations_no_read_armadillo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>6.000000e+04</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "      <td>60000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>67.941967</td>\n",
       "      <td>0.647238</td>\n",
       "      <td>5.192626e-06</td>\n",
       "      <td>0.820302</td>\n",
       "      <td>1.992679e-06</td>\n",
       "      <td>0.496343</td>\n",
       "      <td>0.914817</td>\n",
       "      <td>0.006343</td>\n",
       "      <td>0.001689</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.008180</td>\n",
       "      <td>0.562836</td>\n",
       "      <td>0.119146</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>0.000135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>160.590663</td>\n",
       "      <td>0.203651</td>\n",
       "      <td>3.881562e-06</td>\n",
       "      <td>0.143001</td>\n",
       "      <td>1.404466e-06</td>\n",
       "      <td>0.291760</td>\n",
       "      <td>2.065447</td>\n",
       "      <td>0.008727</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.008813</td>\n",
       "      <td>0.266788</td>\n",
       "      <td>0.142798</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068627</td>\n",
       "      <td>2.384186e-07</td>\n",
       "      <td>0.189655</td>\n",
       "      <td>4.768372e-07</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>0.001019</td>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.000141</td>\n",
       "      <td>0.002794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.000126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.488372</td>\n",
       "      <td>3.099442e-06</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>1.192093e-06</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.002236</td>\n",
       "      <td>0.001637</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>0.004048</td>\n",
       "      <td>0.369021</td>\n",
       "      <td>0.019978</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>4.291534e-06</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>1.668930e-06</td>\n",
       "      <td>0.498705</td>\n",
       "      <td>0.000616</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.001658</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.005319</td>\n",
       "      <td>0.563093</td>\n",
       "      <td>0.075787</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.794118</td>\n",
       "      <td>5.960464e-06</td>\n",
       "      <td>0.945205</td>\n",
       "      <td>2.384186e-06</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.174530</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>0.001691</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.007007</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.163311</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1428.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.418591e-04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.029274e-05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.229370</td>\n",
       "      <td>0.092574</td>\n",
       "      <td>0.012824</td>\n",
       "      <td>0.004153</td>\n",
       "      <td>0.095008</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020289</td>\n",
       "      <td>0.019195</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              seeds          jsim     jsim_time         josie    josie_time  \\\n",
       "count  60000.000000  60000.000000  6.000000e+04  60000.000000  6.000000e+04   \n",
       "mean      67.941967      0.647238  5.192626e-06      0.820302  1.992679e-06   \n",
       "std      160.590663      0.203651  3.881562e-06      0.143001  1.404466e-06   \n",
       "min        1.000000      0.068627  2.384186e-07      0.189655  4.768372e-07   \n",
       "25%        9.000000      0.488372  3.099442e-06      0.720000  1.192093e-06   \n",
       "50%       22.000000      0.634615  4.291534e-06      0.842105  1.668930e-06   \n",
       "75%       41.000000      0.794118  5.960464e-06      0.945205  2.384186e-06   \n",
       "max     1428.000000      1.000000  1.418591e-04      1.000000  4.029274e-05   \n",
       "\n",
       "                 a%    total_time  graphs_generation_time  \\\n",
       "count  60000.000000  60000.000000            60000.000000   \n",
       "mean       0.496343      0.914817                0.006343   \n",
       "std        0.291760      2.065447                0.008727   \n",
       "min        0.011494      0.000091                0.001019   \n",
       "25%        0.250000      0.000334                0.002236   \n",
       "50%        0.498705      0.000616                0.003515   \n",
       "75%        0.750000      0.174530                0.005172   \n",
       "max        1.000000     37.229370                0.092574   \n",
       "\n",
       "       embeddings_generation_time  cos_sim_time_armadillo  \\\n",
       "count                60000.000000            60000.000000   \n",
       "mean                     0.001689                0.000146   \n",
       "std                      0.000295                0.000021   \n",
       "min                      0.001604                0.000141   \n",
       "25%                      0.001637                0.000144   \n",
       "50%                      0.001658                0.000145   \n",
       "75%                      0.001691                0.000147   \n",
       "max                      0.012824                0.004153   \n",
       "\n",
       "       armadillo_total_time     armadillo  AE_armadillo  \\\n",
       "count          60000.000000  60000.000000  60000.000000   \n",
       "mean               0.008180      0.562836      0.119146   \n",
       "std                0.008813      0.266788      0.142798   \n",
       "min                0.002794      0.000000      0.000000   \n",
       "25%                0.004048      0.369021      0.019978   \n",
       "50%                0.005319      0.563093      0.075787   \n",
       "75%                0.007007      0.769231      0.163311   \n",
       "max                0.095008      1.000000      1.000000   \n",
       "\n",
       "       overlap_computations_repeated_armadillo  \\\n",
       "count                             60000.000000   \n",
       "mean                                  0.000227   \n",
       "std                                   0.000181   \n",
       "min                                   0.000215   \n",
       "25%                                   0.000219   \n",
       "50%                                   0.000220   \n",
       "75%                                   0.000221   \n",
       "max                                   0.020289   \n",
       "\n",
       "       overlap_computations_no_read_armadillo  \n",
       "count                            60000.000000  \n",
       "mean                                 0.000135  \n",
       "std                                  0.000179  \n",
       "min                                  0.000126  \n",
       "25%                                  0.000128  \n",
       "50%                                  0.000129  \n",
       "75%                                  0.000129  \n",
       "max                                  0.019195  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/efficiency/efficiency.csv').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>r_id</th>\n",
       "      <th>s_id</th>\n",
       "      <th>seeds</th>\n",
       "      <th>jsim</th>\n",
       "      <th>jsim_time</th>\n",
       "      <th>josie</th>\n",
       "      <th>josie_time</th>\n",
       "      <th>a%</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>198.7650.csv</td>\n",
       "      <td>198.97756.csv</td>\n",
       "      <td>88</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.215385</td>\n",
       "      <td>3.205166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>309.87528.csv</td>\n",
       "      <td>309.87529.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>124.35588.csv</td>\n",
       "      <td>574.6628.csv</td>\n",
       "      <td>70</td>\n",
       "      <td>0.439716</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.105769</td>\n",
       "      <td>3.087128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>225.64426.csv</td>\n",
       "      <td>401.116864.csv</td>\n",
       "      <td>17</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.000886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>441.155903.csv</td>\n",
       "      <td>618.72349.csv</td>\n",
       "      <td>110</td>\n",
       "      <td>0.487179</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.281250</td>\n",
       "      <td>3.848327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59995</th>\n",
       "      <td>245.13162.csv</td>\n",
       "      <td>377.69517.csv</td>\n",
       "      <td>842</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.058065</td>\n",
       "      <td>11.562310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59996</th>\n",
       "      <td>392.26789.csv</td>\n",
       "      <td>447.114168.csv</td>\n",
       "      <td>84</td>\n",
       "      <td>0.491071</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3.126973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59997</th>\n",
       "      <td>382.160.csv</td>\n",
       "      <td>440.84900.csv</td>\n",
       "      <td>12</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59998</th>\n",
       "      <td>322.68550.csv</td>\n",
       "      <td>488.146192.csv</td>\n",
       "      <td>4</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59999</th>\n",
       "      <td>422.109201.csv</td>\n",
       "      <td>422.141642.csv</td>\n",
       "      <td>21</td>\n",
       "      <td>0.359551</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.581818</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.005207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 r_id            s_id  seeds      jsim  jsim_time     josie  \\\n",
       "0        198.7650.csv   198.97756.csv     88  0.583333   0.000004  0.736842   \n",
       "1       309.87528.csv   309.87529.csv      4  0.280000   0.000003  0.437500   \n",
       "2       124.35588.csv    574.6628.csv     70  0.439716   0.000014  0.639175   \n",
       "3       225.64426.csv  401.116864.csv     17  0.692308   0.000010  0.818182   \n",
       "4      441.155903.csv   618.72349.csv    110  0.487179   0.000007  0.791667   \n",
       "...               ...             ...    ...       ...        ...       ...   \n",
       "59995   245.13162.csv   377.69517.csv    842  0.875000   0.000009  0.933333   \n",
       "59996   392.26789.csv  447.114168.csv     84  0.491071   0.000014  0.687500   \n",
       "59997     382.160.csv   440.84900.csv     12  0.300000   0.000005  0.500000   \n",
       "59998   322.68550.csv  488.146192.csv      4  0.428571   0.000003  0.600000   \n",
       "59999  422.109201.csv  422.141642.csv     21  0.359551   0.000009  0.581818   \n",
       "\n",
       "       josie_time        a%  total_time  \n",
       "0        0.000001  0.215385    3.205166  \n",
       "1        0.000001  0.250000    0.000202  \n",
       "2        0.000004  0.105769    3.087128  \n",
       "3        0.000005  0.800000    0.000886  \n",
       "4        0.000003  0.281250    3.848327  \n",
       "...           ...       ...         ...  \n",
       "59995    0.000003  0.058065   11.562310  \n",
       "59996    0.000005  0.200000    3.126973  \n",
       "59997    0.000002  0.333333    0.000358  \n",
       "59998    0.000002  0.400000    0.000246  \n",
       "59999    0.000003  0.111111    0.005207  \n",
       "\n",
       "[60000 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/2_WikiTables/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib as plt\n",
    "\n",
    "def compare_models_ndcg(data: pd.DataFrame | str, bin_criterion: str='k', bins_name: str='Correct Label', out_pdf: str=None, font_scale: float=0.7) -> pd.DataFrame:\n",
    "    \"\"\"Function to plot an histogram to compare performances of different models depending on their range of error \n",
    "\n",
    "    Args:\n",
    "        data (pd.DataFrame | str): data frame containing the results\n",
    "        bin_criterion (str, optional): parameter to generate the 10 bins, must be with values in [0,1]. Defaults to 'a%'.\n",
    "        bins_name (str, optional): name of the bins. Defaults to 'AE'.\n",
    "    \"\"\"\n",
    "    if isinstance(data, str):\n",
    "        data = pd.read_csv(data)\n",
    "    new_data = {\n",
    "        'k':[],\n",
    "        'Approach':[],\n",
    "        'NDCG_Score':[]\n",
    "    }\n",
    "    for k in [1, 10, 20, 30, 40, 50, 60, 70,80,90,100,10_000]:\n",
    "        t = data[data[bin_criterion] == k]\n",
    "        \n",
    "        new_data['Approach'].append('Armadillo')\n",
    "        new_data['k'].append(k)\n",
    "        new_data['NDCG_Score'].append(round(np.mean(t['armadillo']),2))\n",
    "\n",
    "        new_data['Approach'].append('Overlap Set Similarity')\n",
    "        new_data['k'].append(k)\n",
    "        new_data['NDCG_Score'].append(round(np.mean(t['overlap_set_sim']),2))\n",
    "        \n",
    "        new_data['Approach'].append('Jaccard Similarity')\n",
    "        new_data['k'].append(k)\n",
    "        new_data['NDCG_Score'].append(round(np.mean(t['jsim']),2))\n",
    "        \n",
    "    \n",
    "    df = pd.DataFrame(new_data)\n",
    "    sns.set_theme(font_scale=font_scale, style=\"whitegrid\")\n",
    "    sns.barplot(data=df, x='k', y='NDCG_Score', hue='Approach')\n",
    "\n",
    "    if isinstance(out_pdf, str):\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        plt.savefig(out_pdf, format=\"pdf\", bbox_inches=\"tight\")\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = compare_models_ndcg('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_querying/ndcg_data.csv', font_scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.read_csv('/home/francesco.pugnaloni/GNNTE/Datasets/1_Gittables/table_querying/ndcg_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 10000}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(results['k'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>k</th>\n",
       "      <th>armadillo</th>\n",
       "      <th>overlap_set_sim</th>\n",
       "      <th>jsim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.319812</td>\n",
       "      <td>0.555453</td>\n",
       "      <td>0.788573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.345676</td>\n",
       "      <td>0.371927</td>\n",
       "      <td>0.300834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.063916</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.706767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.557625</td>\n",
       "      <td>0.935673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.966013</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            k    armadillo  overlap_set_sim         jsim\n",
       "count  1000.0  1000.000000      1000.000000  1000.000000\n",
       "mean      1.0     0.319812         0.555453     0.788573\n",
       "std       0.0     0.345676         0.371927     0.300834\n",
       "min       1.0     0.000000         0.000000     0.000000\n",
       "25%       1.0     0.063916         0.166667     0.706767\n",
       "50%       1.0     0.181818         0.557625     0.935673\n",
       "75%       1.0     0.500000         0.966013     1.000000\n",
       "max       1.0     1.000000         1.000000     1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[results['k']==1].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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