KGNET_v0.0_LP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "09df9421",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hussein/anaconda3/envs/ogb/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.25.2\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "/home/hussein/anaconda3/envs/ogb/lib/python3.9/site-packages/tqdm/auto.py:22: 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",
      "WARNING:root:The OGB package is out of date. Your version is 1.3.5, while the latest version is 1.3.6.\n"
     ]
    }
   ],
   "source": [
    "from KGNET import KGNET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbc44719",
   "metadata": {},
   "outputs": [],
   "source": [
    "kgnet=KGNET(KG_endpointUrl='http://206.12.98.118:8890/sparql',KG_NamedGraph_IRI='https://dblp2022.org',KG_Prefix='dblp2022')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d68e814f",
   "metadata": {},
   "source": [
    "## visualize graph schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c5c1fd0f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "nx.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"1000px\"\n",
       "            src=\"nx.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fb1fc882100>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types_df=kgnet.getKGNodeEdgeTypes()\n",
    "g=kgnet.visualizeKG(types_df,Notebook=True,width=\"100%\",height=\"1000px\")\n",
    "g.show('nx.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7493282",
   "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>subject</th>\n",
       "      <th>predicate</th>\n",
       "      <th>object</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Book</td>\n",
       "      <td>thesisAcceptedBySchool</td>\n",
       "      <td>thesisAcceptedBySchool_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Book</td>\n",
       "      <td>publicationNote</td>\n",
       "      <td>publicationNote_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Class</td>\n",
       "      <td>equivalentClass</td>\n",
       "      <td>equivalentClass_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Class</td>\n",
       "      <td>comment</td>\n",
       "      <td>comment_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Class</td>\n",
       "      <td>subClassOf</td>\n",
       "      <td>subClassOf_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Creator</td>\n",
       "      <td>wikidata</td>\n",
       "      <td>wikidata_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Creator</td>\n",
       "      <td>creatorNote</td>\n",
       "      <td>creatorNote_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Creator</td>\n",
       "      <td>awardWebpage</td>\n",
       "      <td>awardWebpage_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Creator</td>\n",
       "      <td>archivedWebpage</td>\n",
       "      <td>archivedWebpage_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Creator</td>\n",
       "      <td>primaryFullCreatorName</td>\n",
       "      <td>primaryFullCreatorName_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Creator</td>\n",
       "      <td>primaryAffiliation</td>\n",
       "      <td>primaryAffiliation_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Creator</td>\n",
       "      <td>orcid</td>\n",
       "      <td>orcid_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Creator</td>\n",
       "      <td>Affaliation_Country</td>\n",
       "      <td>Affaliation_Country_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Creator</td>\n",
       "      <td>webpage</td>\n",
       "      <td>webpage_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Creator</td>\n",
       "      <td>label</td>\n",
       "      <td>label_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Creator</td>\n",
       "      <td>differentFrom</td>\n",
       "      <td>differentFrom_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Creator</td>\n",
       "      <td>wikipedia</td>\n",
       "      <td>wikipedia_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Creator</td>\n",
       "      <td>primaryHomepage</td>\n",
       "      <td>primaryHomepage_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Creator</td>\n",
       "      <td>otherFullCreatorName</td>\n",
       "      <td>otherFullCreatorName_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Creator</td>\n",
       "      <td>otherHomepage</td>\n",
       "      <td>otherHomepage_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Creator</td>\n",
       "      <td>otherAffiliation</td>\n",
       "      <td>otherAffiliation_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Editorship</td>\n",
       "      <td>editedBy</td>\n",
       "      <td>Creator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Publication</td>\n",
       "      <td>type</td>\n",
       "      <td>type_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedInVolume</td>\n",
       "      <td>publishedInVolume_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedAsPartOf</td>\n",
       "      <td>Editorship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedInSeriesVolume</td>\n",
       "      <td>publishedInSeriesVolume_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedInSeries</td>\n",
       "      <td>publishedInSeries_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedIn</td>\n",
       "      <td>publishedIn_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Publication</td>\n",
       "      <td>yearOfPublication</td>\n",
       "      <td>yearOfPublication_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Publication</td>\n",
       "      <td>title</td>\n",
       "      <td>title_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Publication</td>\n",
       "      <td>listedOnTocPage</td>\n",
       "      <td>listedOnTocPage_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Publication</td>\n",
       "      <td>doi</td>\n",
       "      <td>doi_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Publication</td>\n",
       "      <td>primaryElectronicEdition</td>\n",
       "      <td>primaryElectronicEdition_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Publication</td>\n",
       "      <td>pagination</td>\n",
       "      <td>pagination_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Publication</td>\n",
       "      <td>numberOfCreators</td>\n",
       "      <td>numberOfCreators_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Publication</td>\n",
       "      <td>monthOfPublication</td>\n",
       "      <td>monthOfPublication_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Publication</td>\n",
       "      <td>isbn</td>\n",
       "      <td>isbn_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedInVolumeIssue</td>\n",
       "      <td>publishedInVolumeIssue_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Publication</td>\n",
       "      <td>bibtexType</td>\n",
       "      <td>bibtexType_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Publication</td>\n",
       "      <td>authoredBy</td>\n",
       "      <td>Creator</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Publication</td>\n",
       "      <td>otherElectronicEdition</td>\n",
       "      <td>otherElectronicEdition_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Publication</td>\n",
       "      <td>yearOfEvent</td>\n",
       "      <td>yearOfEvent_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Publication</td>\n",
       "      <td>sameAs</td>\n",
       "      <td>sameAs_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Publication</td>\n",
       "      <td>publishedBy</td>\n",
       "      <td>publishedBy_Obj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity</td>\n",
       "      <td>type</td>\n",
       "      <td>type</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        subject                 predicate                        object\n",
       "30         Book    thesisAcceptedBySchool    thesisAcceptedBySchool_Obj\n",
       "28         Book           publicationNote           publicationNote_Obj\n",
       "1         Class           equivalentClass           equivalentClass_Obj\n",
       "3         Class                   comment                   comment_Obj\n",
       "6         Class                subClassOf                subClassOf_Obj\n",
       "32      Creator                  wikidata                  wikidata_Obj\n",
       "40      Creator               creatorNote               creatorNote_Obj\n",
       "17      Creator              awardWebpage              awardWebpage_Obj\n",
       "16      Creator           archivedWebpage           archivedWebpage_Obj\n",
       "14      Creator    primaryFullCreatorName    primaryFullCreatorName_Obj\n",
       "13      Creator        primaryAffiliation        primaryAffiliation_Obj\n",
       "12      Creator                     orcid                     orcid_Obj\n",
       "44      Creator       Affaliation_Country       Affaliation_Country_Obj\n",
       "10      Creator                   webpage                   webpage_Obj\n",
       "2       Creator                     label                     label_Obj\n",
       "4       Creator             differentFrom             differentFrom_Obj\n",
       "11      Creator                 wikipedia                 wikipedia_Obj\n",
       "15      Creator           primaryHomepage           primaryHomepage_Obj\n",
       "8       Creator      otherFullCreatorName      otherFullCreatorName_Obj\n",
       "9       Creator             otherHomepage             otherHomepage_Obj\n",
       "7       Creator          otherAffiliation          otherAffiliation_Obj\n",
       "37   Editorship                  editedBy                       Creator\n",
       "41  Publication                      type                      type_Obj\n",
       "42  Publication         publishedInVolume         publishedInVolume_Obj\n",
       "39  Publication         publishedAsPartOf                    Editorship\n",
       "36  Publication   publishedInSeriesVolume   publishedInSeriesVolume_Obj\n",
       "35  Publication         publishedInSeries         publishedInSeries_Obj\n",
       "34  Publication               publishedIn               publishedIn_Obj\n",
       "33  Publication         yearOfPublication         yearOfPublication_Obj\n",
       "31  Publication                     title                     title_Obj\n",
       "38  Publication           listedOnTocPage           listedOnTocPage_Obj\n",
       "22  Publication                       doi                       doi_Obj\n",
       "27  Publication  primaryElectronicEdition  primaryElectronicEdition_Obj\n",
       "26  Publication                pagination                pagination_Obj\n",
       "25  Publication          numberOfCreators          numberOfCreators_Obj\n",
       "24  Publication        monthOfPublication        monthOfPublication_Obj\n",
       "23  Publication                      isbn                      isbn_Obj\n",
       "43  Publication    publishedInVolumeIssue    publishedInVolumeIssue_Obj\n",
       "21  Publication                bibtexType                bibtexType_Obj\n",
       "20  Publication                authoredBy                       Creator\n",
       "19  Publication    otherElectronicEdition    otherElectronicEdition_Obj\n",
       "18  Publication               yearOfEvent               yearOfEvent_Obj\n",
       "5   Publication                    sameAs                    sameAs_Obj\n",
       "29  Publication               publishedBy               publishedBy_Obj\n",
       "0        entity                      type                          type"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00704c2c",
   "metadata": {},
   "source": [
    "## Training Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "303a0277",
   "metadata": {},
   "outputs": [],
   "source": [
    "TargetEdge=\"https://dblp.org/rdf/schema#authoredBy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "374fe9df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "targetEdge= https://dblp.org/rdf/schema#authoredBy\n",
      "sparql_ml_insert_query=  prefix dblp2022:<https://dblp2022.org> \n",
      " prefix kgnet:<https://www.kgnet.com/>\n",
      "           Insert into <kgnet>\n",
      "           where{\n",
      "               select * from kgnet.TrainGML(\n",
      "               {\n",
      "\"name\":\"LinkPredictor>dblp2022>schema#authoredBy>MorsE\",\n",
      "\"GMLTask\":{\"taskType\":\"kgnet:LinkPredictor\",\n",
      "\"namedGraphURI\":\"https://dblp2022.org\",\n",
      "\"namedGraphPrefix\":\"dblp2022\",\n",
      "\"targetEdge\":\"https://dblp.org/rdf/schema#authoredBy\",\"GNNMethod\":\"MorsE\",\n",
      "\"datasetTypesFilePath\":\"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/dblp2022_Types.csv\",\n",
      "\"TOSG\":\"d2h1\"}\n",
      "})}\n",
      "################# TOSG Sampling ###########################\n",
      "KG' path= //media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/mid-0000100.tsv\n",
      "rows_count= 1732918 Query Time= 0:00:10.532241  sec.\n",
      "query idx= 0 batches_count= 9\n",
      "Query idx:  1.0 / 9  records count= 24821  time= 0:00:00.698847  sec.\n",
      "Query idx:  2.0 / 9  records count= 24845  time= 0:00:00.765834  sec.\n",
      "Query idx:  3.0 / 9  records count= 26277  time= 0:00:00.815558  sec.\n",
      "Query idx:  4.0 / 9  records count= 24781  time= 0:00:00.997926  sec.\n",
      "Query idx:  6.0 / 9  records count= 24749  time= 0:00:01.149427  sec.\n",
      "Query idx:  9.0 / 9  records count= 20715  time= 0:00:01.160636  sec.\n",
      "Query idx:  5.0 / 9  records count= 25521  time= 0:00:01.489172  sec.\n",
      "Query idx:  8.0 / 9  records count= 25882  time= 0:00:01.720766  sec.\n",
      "Query idx:  7.0 / 9  records count= 25057  time= 0:00:01.915491  sec.\n",
      "16  threads finish at  0:00:01.944372  sec.\n",
      "query idx= 1 batches_count= 9\n",
      "Query idx:  1.0 / 9  records count= 73776  time= 0:00:01.263650  sec.\n",
      "Query idx:  2.0 / 9  records count= 69994  time= 0:00:01.449329  sec.\n",
      "Query idx:  3.0 / 9  records count= 73538  time= 0:00:01.557664  sec.\n",
      "Query idx:  7.0 / 9  records count= 52557  time= 0:00:02.121699  sec.\n",
      "Query idx:  6.0 / 9  records count= 61380  time= 0:00:02.183732  sec.\n",
      "Query idx:  5.0 / 9  records count= 71701  time= 0:00:02.241834  sec.\n",
      "Query idx:  9.0 / 9  records count= 71711  time= 0:00:02.547873  sec.\n",
      "Query idx:  8.0 / 9  records count= 62077  time= 0:00:02.778538  sec.\n",
      "Query idx:  4.0 / 9  records count= 75190  time= 0:00:03.271378  sec.\n",
      "16  threads finish at  0:00:05.234922  sec.\n",
      "total time  0:00:05.235025  sec.\n",
      "################# Start GNN Task Training  ###########################\n",
      "################# Start GMLaaS Pipline ###########################\n",
      "######### Start PYG Dataset Transformation ##########\n",
      "Directory '//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/mid-0000100' created\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-24 14:46:46 | mid-0000100 | df_test_g_train len=24805\n",
      "INFO:mid-0000100:df_test_g_train len=24805\n",
      "2023-08-24 14:46:47 | mid-0000100 | df_train_g_test len=112888\n",
      "INFO:mid-0000100:df_train_g_test len=112888\n",
      "2023-08-24 14:46:47 | mid-0000100 | df_train_g_train len=693611\n",
      "INFO:mid-0000100:df_train_g_train len=693611\n",
      "2023-08-24 14:46:47 | mid-0000100 | df_train_g_valid len=646\n",
      "INFO:mid-0000100:df_train_g_valid len=646\n",
      "2023-08-24 14:46:47 | mid-0000100 | df_test_g_valid len=443\n",
      "INFO:mid-0000100:df_test_g_valid len=443\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'https://dblp.org/pid/280/9427'\n",
      "'https://dblp.org/pid/95/5358'\n",
      "'https://dblp.org/pid/219/6363'\n",
      "'https://dblp.org/pid/40/5636'\n",
      "'https://dblp.org/pid/70/1042'\n",
      "'https://dblp.org/pid/182/7374'\n",
      "'https://dblp.org/pid/291/9034'\n",
      "'https://dblp.org/pid/256/6922'\n",
      "'https://dblp.org/pid/42/653'\n",
      "'https://dblp.org/pid/303/1793'\n",
      "'https://dblp.org/pid/190/3728'\n",
      "'https://dblp.org/pid/13/6059'\n",
      "'https://dblp.org/pid/33/6018'\n",
      "'https://dblp.org/pid/239/4637'\n",
      "'https://dblp.org/pid/249/0535'\n",
      "'https://dblp.org/pid/160/9138'\n",
      "'https://dblp.org/pid/224/7265'\n",
      "'https://dblp.org/pid/49/8374'\n",
      "'https://dblp.org/pid/75/971'\n",
      "'https://dblp.org/pid/03/2006'\n",
      "'https://dblp.org/pid/257/3355'\n",
      "'https://dblp.org/pid/11/4798'\n",
      "'https://dblp.org/rec/conf/ttc/Beurer-KellnerP21'\n",
      "'https://dblp.org/pid/88/11337-2'\n",
      "'https://dblp.org/pid/97/3274'\n",
      "'https://dblp.org/pid/54/3227'\n",
      "'https://dblp.org/pid/149/7762'\n",
      "'https://dblp.org/pid/31/697'\n",
      "'https://dblp.org/pid/309/0654'\n",
      "'https://dblp.org/pid/158/8944'\n",
      "'https://dblp.org/pid/130/5073'\n",
      "'https://dblp.org/pid/65/5151'\n",
      "'https://dblp.org/pid/25/2550'\n",
      "'https://dblp.org/pid/78/3405'\n",
      "'https://dblp.org/pid/269/5667'\n",
      "'https://dblp.org/pid/62/5537'\n",
      "'https://dblp.org/pid/281/8415'\n",
      "'https://dblp.org/pid/91/8376'\n",
      "'https://dblp.org/pid/298/8414'\n",
      "'https://dblp.org/pid/202/2125'\n",
      "'https://dblp.org/pid/268/8163'\n",
      "'https://dblp.org/pid/01/7049'\n",
      "'https://dblp.org/pid/129/1008'\n",
      "'https://dblp.org/pid/312/9730'\n",
      "'https://dblp.org/pid/304/4674'\n",
      "'https://dblp.org/pid/181/2695-26'\n",
      "'https://dblp.org/pid/311/3806'\n",
      "'https://dblp.org/pid/01/1455'\n",
      "'https://dblp.org/pid/40/3396'\n",
      "'https://dblp.org/pid/44/3928'\n",
      "'https://dblp.org/pid/146/1252'\n",
      "'https://dblp.org/pid/156/9734'\n",
      "'https://dblp.org/pid/259/8416'\n",
      "'https://dblp.org/pid/184/4060'\n",
      "'https://dblp.org/pid/26/1194-3'\n",
      "'https://dblp.org/pid/290/3509'\n",
      "'https://dblp.org/pid/269/8518'\n",
      "'https://dblp.org/pid/l/XiangYangLi'\n",
      "'https://dblp.org/pid/45/2664'\n",
      "'https://dblp.org/rec/conf/ccnc/Marquez-BarjaNM22'\n",
      "'https://dblp.org/pid/200/7611'\n",
      "'https://dblp.org/pid/46/10011'\n",
      "'https://dblp.org/pid/26/11046'\n",
      "'https://dblp.org/pid/h/TiejunHuang'\n",
      "'https://dblp.org/pid/87/11301'\n",
      "'https://dblp.org/pid/21/6430'\n",
      "'https://dblp.org/pid/312/5843'\n",
      "'https://dblp.org/pid/283/3537'\n",
      "'https://dblp.org/pid/311/3808'\n",
      "'https://dblp.org/pid/59/2703'\n",
      "'https://dblp.org/pid/246/4915'\n",
      "'https://dblp.org/pid/89/2593'\n",
      "'https://dblp.org/pid/180/9365'\n",
      "'https://dblp.org/pid/96/4283'\n",
      "'https://dblp.org/pid/313/1568'\n",
      "'https://dblp.org/pid/69/8915'\n",
      "'https://dblp.org/pid/15/5471'\n",
      "'https://dblp.org/pid/90/5355'\n",
      "'https://dblp.org/pid/311/3151'\n",
      "'https://dblp.org/pid/88/1924'\n",
      "'https://dblp.org/pid/77/6837'\n",
      "'https://dblp.org/pid/304/1140'\n",
      "'https://dblp.org/pid/309/0891'\n",
      "'https://dblp.org/pid/99/4318'\n",
      "'https://dblp.org/pid/252/8753'\n",
      "'https://dblp.org/pid/18/2020'\n",
      "'https://dblp.org/pid/311/4125'\n",
      "'https://dblp.org/pid/225/6353'\n",
      "'https://dblp.org/pid/243/1520'\n",
      "'https://dblp.org/pid/172/3626'\n",
      "'https://dblp.org/pid/43/5290'\n",
      "'https://dblp.org/pid/21/8110'\n",
      "'https://dblp.org/pid/58/8743'\n",
      "'https://dblp.org/pid/191/0255'\n",
      "'https://dblp.org/pid/06/1313'\n",
      "'https://dblp.org/pid/37/179'\n",
      "'https://dblp.org/pid/10/1642'\n",
      "'https://dblp.org/pid/73/7783-1'\n",
      "'https://dblp.org/pid/69/5902'\n",
      "'https://dblp.org/rec/journals/corr/abs-2202-03651'\n",
      "'https://dblp.org/pid/10/4661-127'\n",
      "'https://dblp.org/pid/00/4859'\n",
      "'https://dblp.org/pid/311/4202'\n",
      "'https://dblp.org/pid/45/1438'\n",
      "'https://dblp.org/pid/60/10075'\n",
      "'https://dblp.org/rec/conf/wacv/BammeyNGGCM22'\n",
      "'https://dblp.org/pid/48/7757'\n",
      "'https://dblp.org/pid/95/494-1'\n",
      "'https://dblp.org/pid/314/2550'\n",
      "'https://dblp.org/pid/26/11029'\n",
      "'https://dblp.org/pid/95/6026'\n",
      "'https://dblp.org/pid/44/429'\n",
      "'https://dblp.org/pid/176/8023'\n",
      "'https://dblp.org/pid/23/1788-2'\n",
      "'https://dblp.org/pid/313/0436'\n",
      "'https://dblp.org/pid/47/7711'\n",
      "'https://dblp.org/pid/136/9111'\n",
      "'https://dblp.org/pid/83/6095'\n",
      "'https://dblp.org/pid/92/7397'\n",
      "'https://dblp.org/pid/32/6839'\n",
      "'https://dblp.org/pid/02/7138'\n",
      "'https://dblp.org/pid/311/1405'\n",
      "'https://dblp.org/pid/00/3214'\n",
      "'https://dblp.org/pid/r/MathieuRoche'\n",
      "'https://dblp.org/pid/35/7908'\n",
      "'https://dblp.org/pid/313/8922'\n",
      "'https://dblp.org/pid/313/9721'\n",
      "'https://dblp.org/pid/302/8037'\n",
      "'https://dblp.org/rec/conf/ace/Finnie-AnsleyDB22'\n",
      "'https://dblp.org/pid/201/2308'\n",
      "'https://dblp.org/pid/313/8937'\n",
      "'https://dblp.org/pid/313/1066'\n",
      "'https://dblp.org/pid/116/3548'\n",
      "'https://dblp.org/pid/311/4771'\n",
      "'https://dblp.org/pid/29/9985'\n",
      "'https://dblp.org/pid/310/1532'\n",
      "'https://dblp.org/pid/65/6750'\n",
      "'https://dblp.org/pid/278/0906'\n",
      "'https://dblp.org/pid/68/1822'\n",
      "'https://dblp.org/pid/305/3596'\n",
      "'https://dblp.org/pid/179/6837'\n",
      "'https://dblp.org/pid/311/2350'\n",
      "'https://dblp.org/pid/256/7143'\n",
      "'https://dblp.org/pid/05/6396'\n",
      "'https://dblp.org/pid/259/2875'\n",
      "'https://dblp.org/pid/37/5192'\n",
      "'https://dblp.org/pid/93/730'\n",
      "'https://dblp.org/pid/01/10806'\n",
      "'https://dblp.org/pid/63/10335'\n",
      "'https://dblp.org/pid/246/3097'\n",
      "'https://dblp.org/pid/69/5528'\n",
      "'https://dblp.org/pid/137/6161'\n",
      "'https://dblp.org/pid/45/7703'\n",
      "'https://dblp.org/pid/208/8618'\n",
      "'https://dblp.org/pid/247/4848'\n",
      "'https://dblp.org/pid/79/492'\n",
      "'https://dblp.org/pid/311/4733'\n",
      "'https://dblp.org/pid/14/4673'\n",
      "'https://dblp.org/pid/60/8758'\n",
      "'https://dblp.org/pid/242/0080'\n",
      "'https://dblp.org/pid/72/10609'\n",
      "'https://dblp.org/pid/190/9620'\n",
      "'https://dblp.org/pid/66/8942'\n",
      "'https://dblp.org/pid/41/6155'\n",
      "'https://dblp.org/pid/75/533'\n",
      "'https://dblp.org/pid/312/7386'\n",
      "'https://dblp.org/pid/70/10621'\n",
      "'https://dblp.org/pid/128/6972'\n",
      "'https://dblp.org/pid/212/5296'\n",
      "'https://dblp.org/pid/313/1798'\n",
      "'https://dblp.org/pid/45/2357'\n",
      "'https://dblp.org/pid/69/476'\n",
      "'https://dblp.org/pid/194/0977'\n",
      "'https://dblp.org/pid/59/1007'\n",
      "'https://dblp.org/pid/276/8652'\n",
      "'https://dblp.org/pid/222/2820'\n",
      "'https://dblp.org/pid/168/8514'\n",
      "'https://dblp.org/pid/279/7911'\n",
      "'https://dblp.org/pid/310/4066'\n",
      "'https://dblp.org/pid/16/10349'\n",
      "'https://dblp.org/pid/87/9571'\n",
      "'https://dblp.org/rec/journals/corr/abs-2202-01952'\n",
      "'https://dblp.org/pid/47/5523-2'\n",
      "'https://dblp.org/pid/222/2018'\n",
      "'https://dblp.org/pid/302/1093'\n",
      "'https://dblp.org/pid/76/2452-3'\n",
      "'https://dblp.org/pid/95/4213'\n",
      "'https://dblp.org/pid/310/1661'\n",
      "'https://dblp.org/pid/80/7133'\n",
      "'https://dblp.org/pid/56/6786'\n",
      "'https://dblp.org/pid/80/1641'\n",
      "'https://dblp.org/pid/91/5004'\n",
      "'https://dblp.org/pid/169/6774'\n",
      "'https://dblp.org/pid/40/1762'\n",
      "'https://dblp.org/pid/220/4065'\n",
      "'https://dblp.org/pid/202/5982'\n",
      "'https://dblp.org/pid/29/6497'\n",
      "'https://dblp.org/pid/144/9417'\n",
      "'https://dblp.org/pid/313/5961'\n",
      "'https://dblp.org/pid/97/6483-5'\n",
      "'https://dblp.org/pid/07/1951-20'\n",
      "'https://dblp.org/pid/231/6771'\n",
      "'https://dblp.org/pid/08/5760'\n",
      "'https://dblp.org/pid/231/4753'\n",
      "'https://dblp.org/pid/78/6661'\n",
      "'https://dblp.org/pid/312/9704'\n",
      "'https://dblp.org/pid/199/2228'\n",
      "'https://dblp.org/pid/263/4773'\n",
      "'https://dblp.org/pid/207/4885'\n",
      "'https://dblp.org/rec/conf/nafips/UrendaK20'\n",
      "'https://dblp.org/pid/00/4449'\n",
      "'https://dblp.org/pid/43/5134-1'\n",
      "'https://dblp.org/pid/168/9493'\n",
      "'https://dblp.org/pid/120/0921'\n",
      "'https://dblp.org/pid/150/9124'\n",
      "'https://dblp.org/pid/81/6959'\n",
      "'https://dblp.org/pid/216/3189'\n",
      "'https://dblp.org/pid/53/1753'\n",
      "'https://dblp.org/pid/77/1274'\n",
      "'https://dblp.org/pid/08/2429'\n",
      "'https://dblp.org/pid/01/3482'\n",
      "'https://dblp.org/pid/76/3463'\n",
      "'https://dblp.org/pid/245/0223'\n",
      "'https://dblp.org/pid/303/2038'\n",
      "'https://dblp.org/pid/91/3295'\n",
      "'https://dblp.org/pid/123/7641'\n",
      "'https://dblp.org/pid/312/6578'\n",
      "'https://dblp.org/pid/314/2527'\n",
      "'https://dblp.org/pid/63/1514'\n",
      "'https://dblp.org/pid/134/0947'\n",
      "'https://dblp.org/pid/262/1702'\n",
      "'https://dblp.org/pid/144/7376'\n",
      "'https://dblp.org/pid/21/2715-6'\n",
      "'https://dblp.org/rec/conf/nafips/CostaB21'\n",
      "'https://dblp.org/pid/06/10323'\n",
      "'https://dblp.org/pid/26/4253'\n",
      "'https://dblp.org/pid/221/9620'\n",
      "'https://dblp.org/pid/37/4921'\n",
      "'https://dblp.org/pid/49/3209'\n",
      "'https://dblp.org/pid/233/9195-2'\n",
      "'https://dblp.org/pid/280/0918'\n",
      "'https://dblp.org/pid/303/1620'\n",
      "'https://dblp.org/pid/54/2140'\n",
      "'https://dblp.org/rec/conf/clawar/SotoDG21'\n",
      "'https://dblp.org/pid/305/7414'\n",
      "'https://dblp.org/pid/251/8836'\n",
      "'https://dblp.org/pid/s/AmbujKSingh'\n",
      "'https://dblp.org/pid/258/4298'\n",
      "'https://dblp.org/pid/07/10127'\n",
      "'https://dblp.org/pid/41/1658'\n",
      "'https://dblp.org/pid/13/3656'\n",
      "'https://dblp.org/pid/24/10573'\n",
      "'https://dblp.org/pid/74/6484'\n",
      "'https://dblp.org/pid/195/1464'\n",
      "'https://dblp.org/pid/09/3554'\n",
      "'https://dblp.org/pid/146/4547'\n",
      "'https://dblp.org/pid/55/8495'\n",
      "'https://dblp.org/pid/66/3984-8'\n",
      "'https://dblp.org/pid/00/3261'\n",
      "'https://dblp.org/pid/22/4616'\n",
      "'https://dblp.org/pid/85/4391'\n",
      "'https://dblp.org/pid/00/4922'\n",
      "'https://dblp.org/pid/158/4120'\n",
      "'https://dblp.org/pid/311/3580'\n",
      "'https://dblp.org/pid/44/9456'\n",
      "'https://dblp.org/pid/180/5665'\n",
      "'https://dblp.org/pid/135/0037'\n",
      "'https://dblp.org/pid/21/3810'\n",
      "'https://dblp.org/pid/304/8669'\n",
      "'https://dblp.org/pid/245/8590'\n",
      "'https://dblp.org/pid/237/7416'\n",
      "'https://dblp.org/pid/04/2024-3'\n",
      "'https://dblp.org/pid/314/3396'\n",
      "'https://dblp.org/pid/07/8497'\n",
      "'https://dblp.org/pid/13/2913-1'\n",
      "'https://dblp.org/pid/24/6560'\n",
      "'https://dblp.org/pid/310/5492'\n",
      "'https://dblp.org/rec/journals/bspc/ZhangLHLW22'\n",
      "'https://dblp.org/pid/70/3304'\n",
      "'https://dblp.org/pid/37/5336'\n",
      "'https://dblp.org/pid/54/1072'\n",
      "'https://dblp.org/pid/234/3058'\n",
      "'https://dblp.org/pid/172/1443'\n",
      "'https://dblp.org/pid/15/6040-1'\n",
      "'https://dblp.org/pid/310/6263'\n",
      "'https://dblp.org/pid/302/8178'\n",
      "'https://dblp.org/pid/314/4683'\n",
      "'https://dblp.org/pid/85/3156'\n",
      "'https://dblp.org/pid/83/2265'\n",
      "'https://dblp.org/pid/04/5616'\n",
      "'https://dblp.org/pid/20/3215'\n",
      "'https://dblp.org/pid/241/5884'\n",
      "'https://dblp.org/pid/49/7488'\n",
      "'https://dblp.org/pid/210/6243'\n",
      "'https://dblp.org/pid/39/9'\n",
      "'https://dblp.org/pid/286/5650'\n",
      "'https://dblp.org/pid/170/4080'\n",
      "'https://dblp.org/pid/275/0838'\n",
      "'https://dblp.org/pid/59/11040'\n",
      "'https://dblp.org/pid/190/8188'\n",
      "'https://dblp.org/pid/148/2880'\n",
      "'https://dblp.org/pid/42/4324'\n",
      "'https://dblp.org/pid/06/1313'\n",
      "'https://dblp.org/pid/194/9121'\n",
      "'https://dblp.org/pid/221/1730'\n",
      "'https://dblp.org/pid/41/8398'\n",
      "'https://dblp.org/pid/v/RMVoyles'\n",
      "'https://dblp.org/pid/49/2335'\n",
      "'https://dblp.org/pid/214/8118'\n",
      "'https://dblp.org/pid/06/7035'\n",
      "'https://dblp.org/pid/30/2371'\n",
      "'https://dblp.org/pid/281/0860'\n",
      "'https://dblp.org/pid/165/0280'\n",
      "'https://dblp.org/pid/34/8744'\n",
      "'https://dblp.org/pid/279/2784'\n",
      "'https://dblp.org/pid/08/9370'\n",
      "'https://dblp.org/pid/129/1008'\n",
      "'https://dblp.org/pid/254/0340'\n",
      "'https://dblp.org/pid/312/8116'\n",
      "'https://dblp.org/pid/52/4225'\n",
      "'https://dblp.org/pid/137/8589'\n",
      "'https://dblp.org/pid/312/7380'\n",
      "'https://dblp.org/pid/91/8857'\n",
      "'https://dblp.org/pid/90/10342'\n",
      "'https://dblp.org/pid/310/6201'\n",
      "'https://dblp.org/pid/143/5718'\n",
      "'https://dblp.org/pid/116/7852'\n",
      "'https://dblp.org/pid/245/9759'\n",
      "'https://dblp.org/pid/50/5725'\n",
      "'https://dblp.org/pid/218/5637'\n",
      "'https://dblp.org/pid/313/0494'\n",
      "'https://dblp.org/pid/213/9442'\n",
      "'https://dblp.org/pid/145/6593'\n",
      "'https://dblp.org/pid/147/6907'\n",
      "'https://dblp.org/pid/232/0322'\n",
      "'https://dblp.org/pid/279/2880'\n",
      "'https://dblp.org/pid/195/8738'\n",
      "'https://dblp.org/pid/51/208'\n",
      "'https://dblp.org/pid/164/1411'\n",
      "'https://dblp.org/pid/278/9250'\n",
      "'https://dblp.org/pid/259/8571'\n",
      "'https://dblp.org/pid/08/4340'\n",
      "'https://dblp.org/pid/180/4444'\n",
      "'https://dblp.org/pid/47/91-2'\n",
      "'https://dblp.org/pid/25/512'\n",
      "'https://dblp.org/pid/303/5565'\n",
      "'https://dblp.org/pid/303/1557'\n",
      "'https://dblp.org/pid/175/6718'\n",
      "'https://dblp.org/pid/310/4627'\n",
      "'https://dblp.org/pid/309/6183'\n",
      "'https://dblp.org/pid/129/1161'\n",
      "'https://dblp.org/rec/conf/nafips/PickeringC20'\n",
      "'https://dblp.org/pid/149/0396'\n",
      "'https://dblp.org/pid/35/6717'\n",
      "'https://dblp.org/rec/conf/nafips/PickeringC21'\n",
      "'https://dblp.org/pid/21/5505'\n",
      "'https://dblp.org/pid/314/4036'\n",
      "'https://dblp.org/pid/43/8501'\n",
      "'https://dblp.org/pid/207/7584'\n",
      "'https://dblp.org/pid/149/0238'\n",
      "'https://dblp.org/pid/97/6505-1'\n",
      "'https://dblp.org/pid/243/3203'\n",
      "'https://dblp.org/pid/230/3666'\n",
      "'https://dblp.org/rec/journals/corr/abs-2201-02866'\n",
      "'https://dblp.org/pid/98/3765'\n",
      "'https://dblp.org/pid/171/8350'\n",
      "'https://dblp.org/pid/228/5381'\n",
      "'https://dblp.org/pid/254/0860'\n",
      "'https://dblp.org/pid/80/8724-1'\n",
      "'https://dblp.org/pid/302/8258'\n",
      "'https://dblp.org/pid/303/1919'\n",
      "'https://dblp.org/pid/82/104-2'\n",
      "'https://dblp.org/pid/220/9907'\n",
      "'https://dblp.org/pid/20/625'\n",
      "'https://dblp.org/pid/80/7562'\n",
      "'https://dblp.org/pid/282/7181'\n",
      "'https://dblp.org/pid/132/5158'\n",
      "'https://dblp.org/pid/38/2606-1'\n",
      "'https://dblp.org/pid/260/0079'\n",
      "'https://dblp.org/pid/311/0438'\n",
      "'https://dblp.org/pid/209/4848'\n",
      "'https://dblp.org/rec/journals/tii/Zamudio-Ramirez22a'\n",
      "'https://dblp.org/pid/180/5513'\n",
      "'https://dblp.org/pid/20/5393'\n",
      "'https://dblp.org/pid/314/4852'\n",
      "'https://dblp.org/pid/70/3621'\n",
      "'https://dblp.org/pid/50/567'\n",
      "'https://dblp.org/pid/52/331'\n",
      "'https://dblp.org/pid/40/2499'\n",
      "'https://dblp.org/pid/17/1352-1'\n",
      "'https://dblp.org/pid/279/4236'\n",
      "'https://dblp.org/pid/96/4282-54'\n",
      "'https://dblp.org/pid/72/6108'\n",
      "'https://dblp.org/pid/313/1246'\n",
      "'https://dblp.org/pid/16/1153'\n",
      "'https://dblp.org/pid/90/2367'\n",
      "'https://dblp.org/pid/309/0580'\n",
      "'https://dblp.org/pid/255/3928'\n",
      "'https://dblp.org/pid/176/2707'\n",
      "'https://dblp.org/pid/135/7091'\n",
      "'https://dblp.org/pid/219/2662'\n",
      "'https://dblp.org/pid/40/5755'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-24 14:46:50 | mid-0000100 | Sampling Time Sec=5.431976\n",
      "INFO:mid-0000100:Sampling Time Sec=5.431976\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meta_train\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-24 14:46:51.513997: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-08-24 14:46:51.805166: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-08-24 14:46:51.806881: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-08-24 14:46:52.659767: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "2023-08-24 14:46:53 | mid-0000100 | {\"dataset_name\": \"mid-0000100\", \"name\": \"mid-0000100\", \"root_path\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/\", \"step\": \"meta_train\", \"metatrain_state\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/./state/WikiKG2_LP/WikiKG2_LP_transe.best\", \"state_dir\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/trained_models/\", \"log_dir\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/logs/\", \"tb_log_dir\": \"./tb_log\", \"num_train_subgraph\": 100, \"num_valid_subgraph\": 50, \"num_sample_for_estimate_size\": 50, \"rw_0\": 10, \"rw_1\": 10, \"rw_2\": 5, \"num_sample_cand\": 5, \"metatrain_num_neg\": 32, \"metatrain_num_epoch\": 5, \"metatrain_bs\": 32, \"metatrain_lr\": 0.01, \"metatrain_check_per_step\": 4, \"indtest_eval_bs\": 512, \"posttrain_num_neg\": 32, \"posttrain_bs\": 64, \"posttrain_lr\": 0.001, \"posttrain_num_epoch\": 2, \"posttrain_check_per_epoch\": 1, \"num_layers\": 3, \"num_bases\": 4, \"emb_dim\": 128, \"kge\": \"TransE\", \"gamma\": 10, \"adv_temp\": 1, \"gpu\": \"cpu\", \"seed\": 1234, \"f\": \"/home/hussein/.local/share/jupyter/runtime/kernel-f12c50c1-eb6f-463b-a2e4-70d0dcb91283.json\", \"ent_dim\": 128, \"rel_dim\": 128, \"data_path\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/mid-0000100/mid-0000100.pkl\", \"db_path\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/mid-0000100_subgraph\", \"num_rel\": 16}\n",
      "INFO:mid-0000100:{\"dataset_name\": \"mid-0000100\", \"name\": \"mid-0000100\", \"root_path\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/\", \"step\": \"meta_train\", \"metatrain_state\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/./state/WikiKG2_LP/WikiKG2_LP_transe.best\", \"state_dir\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/trained_models/\", \"log_dir\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/logs/\", \"tb_log_dir\": \"./tb_log\", \"num_train_subgraph\": 100, \"num_valid_subgraph\": 50, \"num_sample_for_estimate_size\": 50, \"rw_0\": 10, \"rw_1\": 10, \"rw_2\": 5, \"num_sample_cand\": 5, \"metatrain_num_neg\": 32, \"metatrain_num_epoch\": 5, \"metatrain_bs\": 32, \"metatrain_lr\": 0.01, \"metatrain_check_per_step\": 4, \"indtest_eval_bs\": 512, \"posttrain_num_neg\": 32, \"posttrain_bs\": 64, \"posttrain_lr\": 0.001, \"posttrain_num_epoch\": 2, \"posttrain_check_per_epoch\": 1, \"num_layers\": 3, \"num_bases\": 4, \"emb_dim\": 128, \"kge\": \"TransE\", \"gamma\": 10, \"adv_temp\": 1, \"gpu\": \"cpu\", \"seed\": 1234, \"f\": \"/home/hussein/.local/share/jupyter/runtime/kernel-f12c50c1-eb6f-463b-a2e4-70d0dcb91283.json\", \"ent_dim\": 128, \"rel_dim\": 128, \"data_path\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/mid-0000100/mid-0000100.pkl\", \"db_path\": \"//media/hussein/UbuntuData/GithubRepos/KGNET/Datasets/mid-0000100_subgraph\", \"num_rel\": 16}\n",
      "/media/hussein/UbuntuData/GithubRepos////KGNET/GMLaaS/models/MorsE/utils.py:19: UserWarning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matricesor `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755883846/work/aten/src/ATen/native/TensorShape.cpp:2318.)\n",
      "  g = dgl.graph((torch.cat([triples[:, 0].T, triples[:, 2].T]),\n",
      "2023-08-24 14:46:54 | mid-0000100 | start meta-training\n",
      "INFO:mid-0000100:start meta-training\n",
      "2023-08-24 14:46:54 | mid-0000100 | resource.struct_rusage(ru_utime=16.955367, ru_stime=2.370062, ru_maxrss=1093832, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=507455, ru_majflt=4252, ru_nswap=0, ru_inblock=941448, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=121547, ru_nivcsw=3469)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=16.955367, ru_stime=2.370062, ru_maxrss=1093832, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=507455, ru_majflt=4252, ru_nswap=0, ru_inblock=941448, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=121547, ru_nivcsw=3469)\n",
      "/media/hussein/UbuntuData/GithubRepos////KGNET/GMLaaS/models/MorsE/datasets.py:35: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755883846/work/torch/csrc/utils/tensor_new.cpp:210.)\n",
      "  torch.tensor(que_neg_tail_ent), torch.tensor(que_neg_head_ent)\n",
      "/home/hussein/anaconda3/envs/ogb/lib/python3.9/site-packages/torch/autocast_mode.py:162: UserWarning: User provided device_type of 'cuda', but CUDA is not available. Disabling\n",
      "  warnings.warn('User provided device_type of \\'cuda\\', but CUDA is not available. Disabling')\n",
      "2023-08-24 14:46:57 | mid-0000100 | epoch:0 | step: 1 | loss: 2.1412\n",
      "INFO:mid-0000100:epoch:0 | step: 1 | loss: 2.1412\n",
      "2023-08-24 14:46:59 | mid-0000100 | epoch:0 | step: 2 | loss: 17.7684\n",
      "INFO:mid-0000100:epoch:0 | step: 2 | loss: 17.7684\n",
      "2023-08-24 14:47:01 | mid-0000100 | epoch:0 | step: 3 | loss: 31.1768\n",
      "INFO:mid-0000100:epoch:0 | step: 3 | loss: 31.1768\n",
      "2023-08-24 14:47:02 | mid-0000100 | epoch:0 | step: 4 | loss: 1.7919\n",
      "INFO:mid-0000100:epoch:0 | step: 4 | loss: 1.7919\n",
      "/media/hussein/UbuntuData/GithubRepos////KGNET/GMLaaS/models/MorsE/trainer.py:114: UserWarning: where received a uint8 condition tensor. This behavior is deprecated and will be removed in a future version of PyTorch. Use a boolean condition instead. (Triggered internally at  /opt/conda/conda-bld/pytorch_1646755883846/work/aten/src/ATen/native/TensorCompare.cpp:333.)\n",
      "  pred = torch.where(tail_label.byte(), -torch.ones_like(pred) * 10000000, pred)\n",
      "2023-08-24 14:47:03 | mid-0000100 | valid on valid subgraphs\n",
      "INFO:mid-0000100:valid on valid subgraphs\n",
      "2023-08-24 14:47:03 | mid-0000100 |  mrr: 0.0171, hits@1: 0.0006, hits@5: 0.0037, hits@10: 0.0104\n",
      "INFO:mid-0000100: mrr: 0.0171, hits@1: 0.0006, hits@5: 0.0037, hits@10: 0.0104\n",
      "2023-08-24 14:47:03 | mid-0000100 | resource.struct_rusage(ru_utime=50.664687, ru_stime=11.948388, ru_maxrss=5894604, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=6781108, ru_majflt=4374, ru_nswap=0, ru_inblock=952048, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=122344, ru_nivcsw=4183)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=50.664687, ru_stime=11.948388, ru_maxrss=5894604, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=6781108, ru_majflt=4374, ru_nswap=0, ru_inblock=952048, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=122344, ru_nivcsw=4183)\n",
      "2023-08-24 14:47:03 | mid-0000100 | Train Time Sec=8.679978\n",
      "INFO:mid-0000100:Train Time Sec=8.679978\n",
      "2023-08-24 14:47:03 | mid-0000100 | best model | mrr 0.0171\n",
      "INFO:mid-0000100:best model | mrr 0.0171\n",
      "2023-08-24 14:47:03 | mid-0000100 | resource.struct_rusage(ru_utime=50.719206, ru_stime=11.951813, ru_maxrss=5894604, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=6781108, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=122861, ru_nivcsw=4184)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=50.719206, ru_stime=11.951813, ru_maxrss=5894604, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=6781108, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=122861, ru_nivcsw=4184)\n",
      "2023-08-24 14:47:05 | mid-0000100 | epoch:1 | step: 1 | loss: 5.8227\n",
      "INFO:mid-0000100:epoch:1 | step: 1 | loss: 5.8227\n",
      "2023-08-24 14:47:08 | mid-0000100 | epoch:1 | step: 2 | loss: 1.0813\n",
      "INFO:mid-0000100:epoch:1 | step: 2 | loss: 1.0813\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-24 14:47:10 | mid-0000100 | epoch:1 | step: 3 | loss: 0.7524\n",
      "INFO:mid-0000100:epoch:1 | step: 3 | loss: 0.7524\n",
      "2023-08-24 14:47:11 | mid-0000100 | epoch:1 | step: 4 | loss: 1.1251\n",
      "INFO:mid-0000100:epoch:1 | step: 4 | loss: 1.1251\n",
      "2023-08-24 14:47:12 | mid-0000100 | valid on valid subgraphs\n",
      "INFO:mid-0000100:valid on valid subgraphs\n",
      "2023-08-24 14:47:12 | mid-0000100 |  mrr: 0.5787, hits@1: 0.3913, hits@5: 0.8377, hits@10: 0.8849\n",
      "INFO:mid-0000100: mrr: 0.5787, hits@1: 0.3913, hits@5: 0.8377, hits@10: 0.8849\n",
      "2023-08-24 14:47:12 | mid-0000100 | resource.struct_rusage(ru_utime=85.971466, ru_stime=21.868282999999998, ru_maxrss=6137544, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=12959612, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=123475, ru_nivcsw=4885)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=85.971466, ru_stime=21.868282999999998, ru_maxrss=6137544, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=12959612, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=123475, ru_nivcsw=4885)\n",
      "2023-08-24 14:47:12 | mid-0000100 | Train Time Sec=17.766842\n",
      "INFO:mid-0000100:Train Time Sec=17.766842\n",
      "2023-08-24 14:47:12 | mid-0000100 | best model | mrr 0.5787\n",
      "INFO:mid-0000100:best model | mrr 0.5787\n",
      "2023-08-24 14:47:12 | mid-0000100 | resource.struct_rusage(ru_utime=86.024972, ru_stime=21.869669, ru_maxrss=6137544, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=12959612, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=123932, ru_nivcsw=4930)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=86.024972, ru_stime=21.869669, ru_maxrss=6137544, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=12959612, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=123932, ru_nivcsw=4930)\n",
      "2023-08-24 14:47:15 | mid-0000100 | epoch:2 | step: 1 | loss: 0.6507\n",
      "INFO:mid-0000100:epoch:2 | step: 1 | loss: 0.6507\n",
      "2023-08-24 14:47:17 | mid-0000100 | epoch:2 | step: 2 | loss: 0.6567\n",
      "INFO:mid-0000100:epoch:2 | step: 2 | loss: 0.6567\n",
      "2023-08-24 14:47:19 | mid-0000100 | epoch:2 | step: 3 | loss: 0.4841\n",
      "INFO:mid-0000100:epoch:2 | step: 3 | loss: 0.4841\n",
      "2023-08-24 14:47:19 | mid-0000100 | epoch:2 | step: 4 | loss: 0.5526\n",
      "INFO:mid-0000100:epoch:2 | step: 4 | loss: 0.5526\n",
      "2023-08-24 14:47:20 | mid-0000100 | valid on valid subgraphs\n",
      "INFO:mid-0000100:valid on valid subgraphs\n",
      "2023-08-24 14:47:20 | mid-0000100 |  mrr: 0.6333, hits@1: 0.4647, hits@5: 0.8416, hits@10: 0.8998\n",
      "INFO:mid-0000100: mrr: 0.6333, hits@1: 0.4647, hits@5: 0.8416, hits@10: 0.8998\n",
      "2023-08-24 14:47:20 | mid-0000100 | resource.struct_rusage(ru_utime=118.872292, ru_stime=31.588217, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=19165790, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=124587, ru_nivcsw=5666)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=118.872292, ru_stime=31.588217, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=19165790, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=124587, ru_nivcsw=5666)\n",
      "2023-08-24 14:47:20 | mid-0000100 | Train Time Sec=26.388921\n",
      "INFO:mid-0000100:Train Time Sec=26.388921\n",
      "2023-08-24 14:47:20 | mid-0000100 | best model | mrr 0.6333\n",
      "INFO:mid-0000100:best model | mrr 0.6333\n",
      "2023-08-24 14:47:20 | mid-0000100 | resource.struct_rusage(ru_utime=118.928489, ru_stime=31.596513, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=19165790, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=125089, ru_nivcsw=5674)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=118.928489, ru_stime=31.596513, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=19165790, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=125089, ru_nivcsw=5674)\n",
      "2023-08-24 14:47:23 | mid-0000100 | epoch:3 | step: 1 | loss: 0.4933\n",
      "INFO:mid-0000100:epoch:3 | step: 1 | loss: 0.4933\n",
      "2023-08-24 14:47:25 | mid-0000100 | epoch:3 | step: 2 | loss: 0.4322\n",
      "INFO:mid-0000100:epoch:3 | step: 2 | loss: 0.4322\n",
      "2023-08-24 14:47:28 | mid-0000100 | epoch:3 | step: 3 | loss: 0.4418\n",
      "INFO:mid-0000100:epoch:3 | step: 3 | loss: 0.4418\n",
      "2023-08-24 14:47:28 | mid-0000100 | epoch:3 | step: 4 | loss: 0.5068\n",
      "INFO:mid-0000100:epoch:3 | step: 4 | loss: 0.5068\n",
      "2023-08-24 14:47:29 | mid-0000100 | valid on valid subgraphs\n",
      "INFO:mid-0000100:valid on valid subgraphs\n",
      "2023-08-24 14:47:29 | mid-0000100 |  mrr: 0.6904, hits@1: 0.5261, hits@5: 0.9145, hits@10: 0.9407\n",
      "INFO:mid-0000100: mrr: 0.6904, hits@1: 0.5261, hits@5: 0.9145, hits@10: 0.9407\n",
      "2023-08-24 14:47:29 | mid-0000100 | resource.struct_rusage(ru_utime=152.668739, ru_stime=41.146243, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=25385773, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=125750, ru_nivcsw=6268)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=152.668739, ru_stime=41.146243, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=25385773, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=125750, ru_nivcsw=6268)\n",
      "2023-08-24 14:47:29 | mid-0000100 | Train Time Sec=35.156643\n",
      "INFO:mid-0000100:Train Time Sec=35.156643\n",
      "2023-08-24 14:47:29 | mid-0000100 | best model | mrr 0.6904\n",
      "INFO:mid-0000100:best model | mrr 0.6904\n",
      "2023-08-24 14:47:29 | mid-0000100 | resource.struct_rusage(ru_utime=152.728546, ru_stime=41.15049, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=25385773, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=126260, ru_nivcsw=6269)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=152.728546, ru_stime=41.15049, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=25385773, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=126260, ru_nivcsw=6269)\n",
      "2023-08-24 14:47:32 | mid-0000100 | epoch:4 | step: 1 | loss: 0.4333\n",
      "INFO:mid-0000100:epoch:4 | step: 1 | loss: 0.4333\n",
      "2023-08-24 14:47:34 | mid-0000100 | epoch:4 | step: 2 | loss: 0.4414\n",
      "INFO:mid-0000100:epoch:4 | step: 2 | loss: 0.4414\n",
      "2023-08-24 14:47:37 | mid-0000100 | epoch:4 | step: 3 | loss: 0.4358\n",
      "INFO:mid-0000100:epoch:4 | step: 3 | loss: 0.4358\n",
      "2023-08-24 14:47:37 | mid-0000100 | epoch:4 | step: 4 | loss: 0.3919\n",
      "INFO:mid-0000100:epoch:4 | step: 4 | loss: 0.3919\n",
      "2023-08-24 14:47:38 | mid-0000100 | valid on valid subgraphs\n",
      "INFO:mid-0000100:valid on valid subgraphs\n",
      "2023-08-24 14:47:38 | mid-0000100 |  mrr: 0.6817, hits@1: 0.5090, hits@5: 0.9130, hits@10: 0.9421\n",
      "INFO:mid-0000100: mrr: 0.6817, hits@1: 0.5090, hits@5: 0.9130, hits@10: 0.9421\n",
      "2023-08-24 14:47:38 | mid-0000100 | resource.struct_rusage(ru_utime=186.207327, ru_stime=50.842383, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=31572876, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=126887, ru_nivcsw=6905)\n",
      "INFO:mid-0000100:resource.struct_rusage(ru_utime=186.207327, ru_stime=50.842383, ru_maxrss=6219024, ru_ixrss=0, ru_idrss=0, ru_isrss=0, ru_minflt=31572876, ru_majflt=4376, ru_nswap=0, ru_inblock=952080, ru_oublock=472, ru_msgsnd=0, ru_msgrcv=0, ru_nsignals=0, ru_nvcsw=126887, ru_nivcsw=6905)\n",
      "2023-08-24 14:47:38 | mid-0000100 | Train Time Sec=43.89268\n",
      "INFO:mid-0000100:Train Time Sec=43.89268\n",
      "2023-08-24 14:47:38 | mid-0000100 | best model is at step 4, mrr 0.6904, bad count 1\n",
      "INFO:mid-0000100:best model is at step 4, mrr 0.6904, bad count 1\n",
      "2023-08-24 14:47:38 | mid-0000100 | finish meta-training\n",
      "INFO:mid-0000100:finish meta-training\n",
      "2023-08-24 14:47:38 | mid-0000100 | save best model\n",
      "INFO:mid-0000100:save best model\n",
      "2023-08-24 14:47:38 | mid-0000100 | best validation | epoch:4, mrr: 0.6904, hits@1: 0.5261, hits@5: 0.9145, hits@10: 0.9407\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:mid-0000100:best validation | epoch:4, mrr: 0.6904, hits@1: 0.5261, hits@5: 0.9145, hits@10: 0.9407\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "call before_test_load\n",
      "call evaluate_indtest_test_triples\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-08-24 14:47:42 | mid-0000100 | test on ind-test-graph, sample 50\n",
      "INFO:mid-0000100:test on ind-test-graph, sample 50\n",
      "2023-08-24 14:47:42 | mid-0000100 | mrr: 0.4432, hits@1: 0.2271, hits@5: 0.7717, hits@10: 0.9708\n",
      "INFO:mid-0000100:mrr: 0.4432, hits@1: 0.2271, hits@5: 0.7717, hits@10: 0.9708\n",
      "2023-08-24 14:47:42 | mid-0000100 | Train Time Sec=51.531812\n",
      "INFO:mid-0000100:Train Time Sec=51.531812\n",
      "2023-08-24 14:47:42 | mid-0000100 | Total Time Sec=57.961665\n",
      "INFO:mid-0000100:Total Time Sec=57.961665\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_eval_rst defaultdict(<class 'int'>, {'mr': 3.8443545574635913, 'mrr': 0.6904077535921556, 'hits@1': 0.5260619832455447, 'hits@5': 0.9144747582456557, 'hits@10': 0.9406877170858623}) type : <class 'collections.defaultdict'>\n",
      "Done\n",
      "Uploading dataset to:  http://206.12.100.114:64648/uploaddataset/\n",
      "Model uploaded to cloud successfully!\n",
      "task_uri= kgnet:GMLTask/tid-0000026\n",
      "model_uri= kgnet:GMLModel/mid-0000100\n"
     ]
    }
   ],
   "source": [
    "\n",
    "taskId,modelId,trainMetadata=kgnet.train_GML( operatorType=KGNET.GML_Operator_Types.LinkPrediction,\n",
    "                                                       targetEdge=TargetEdge,\n",
    "                                                       GNNMethod=KGNET.GNN_Methods.MorsE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc3509b",
   "metadata": {},
   "source": [
    "### Query Task Info from KGMeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a1336b6",
   "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>property</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Task ID</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Task Name</td>\n",
       "      <td>LinkPredictor&gt;dblp2022&gt;schema#authoredBy&gt;RGCN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Task Type</td>\n",
       "      <td>kgnet:type/linkPrediction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KG name</td>\n",
       "      <td>dblp2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KG description</td>\n",
       "      <td>Subgraph of dblp 2022-03 KG that contains only...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Target Edge</td>\n",
       "      <td>https://dblp.org/rdf/schema#authoredBy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Date Created</td>\n",
       "      <td>19/08/2023, 16:30:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td># Trained Models</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           property                                              Value\n",
       "0           Task ID                                                 26\n",
       "1         Task Name      LinkPredictor>dblp2022>schema#authoredBy>RGCN\n",
       "2         Task Type                          kgnet:type/linkPrediction\n",
       "3           KG name                                           dblp2022\n",
       "4    KG description  Subgraph of dblp 2022-03 KG that contains only...\n",
       "5       Target Edge             https://dblp.org/rdf/schema#authoredBy\n",
       "6      Date Created                               19/08/2023, 16:30:56\n",
       "7  # Trained Models                                                  7"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kgnet.KGMeta_Governer.getGMLTaskBasicInfoByID(taskId)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50eb88b8",
   "metadata": {},
   "source": [
    "### Query Model Info from KGMeta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a52a8fe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "}\n",
       "#T_f1b7d_row5_col0, #T_f1b7d_row5_col1, #T_f1b7d_row5_col2, #T_f1b7d_row5_col3, #T_f1b7d_row5_col4, #T_f1b7d_row5_col5, #T_f1b7d_row5_col6, #T_f1b7d_row5_col8 {\n",
       "  background-color: #ffbd2c;\n",
       "  font-weight: bold;\n",
       "  color: ;\n",
       "}\n",
       "#T_f1b7d_row5_col7 {\n",
       "  background-color: #A3EBB1;\n",
       "  background-color: #ffbd2c;\n",
       "  font-weight: bold;\n",
       "  color: ;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f1b7d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f1b7d_level0_col0\" class=\"col_heading level0 col0\" >Property</th>\n",
       "      <th id=\"T_f1b7d_level0_col1\" class=\"col_heading level0 col1\" >Model ID</th>\n",
       "      <th id=\"T_f1b7d_level0_col2\" class=\"col_heading level0 col2\" >GNN Method</th>\n",
       "      <th id=\"T_f1b7d_level0_col3\" class=\"col_heading level0 col3\" >Training Memory GB.</th>\n",
       "      <th id=\"T_f1b7d_level0_col4\" class=\"col_heading level0 col4\" >Training Time Sec.</th>\n",
       "      <th id=\"T_f1b7d_level0_col5\" class=\"col_heading level0 col5\" >Test MRR Score</th>\n",
       "      <th id=\"T_f1b7d_level0_col6\" class=\"col_heading level0 col6\" >Test Hits@10 Score</th>\n",
       "      <th id=\"T_f1b7d_level0_col7\" class=\"col_heading level0 col7\" >Inference Time</th>\n",
       "      <th id=\"T_f1b7d_level0_col8\" class=\"col_heading level0 col8\" >Date Created</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f1b7d_row0_col0\" class=\"data row0 col0\" >Model #1</td>\n",
       "      <td id=\"T_f1b7d_row0_col1\" class=\"data row0 col1\" >93</td>\n",
       "      <td id=\"T_f1b7d_row0_col2\" class=\"data row0 col2\" >MorsE</td>\n",
       "      <td id=\"T_f1b7d_row0_col3\" class=\"data row0 col3\" >1.005764</td>\n",
       "      <td id=\"T_f1b7d_row0_col4\" class=\"data row0 col4\" >50.050000</td>\n",
       "      <td id=\"T_f1b7d_row0_col5\" class=\"data row0 col5\" >0.690000</td>\n",
       "      <td id=\"T_f1b7d_row0_col6\" class=\"data row0 col6\" >0.920000</td>\n",
       "      <td id=\"T_f1b7d_row0_col7\" class=\"data row0 col7\" >13</td>\n",
       "      <td id=\"T_f1b7d_row0_col8\" class=\"data row0 col8\" >23/08/2023, 10:13:05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f1b7d_row1_col0\" class=\"data row1 col0\" >Model #2</td>\n",
       "      <td id=\"T_f1b7d_row1_col1\" class=\"data row1 col1\" >108</td>\n",
       "      <td id=\"T_f1b7d_row1_col2\" class=\"data row1 col2\" >RGCN</td>\n",
       "      <td id=\"T_f1b7d_row1_col3\" class=\"data row1 col3\" >0.881691</td>\n",
       "      <td id=\"T_f1b7d_row1_col4\" class=\"data row1 col4\" >10618.430000</td>\n",
       "      <td id=\"T_f1b7d_row1_col5\" class=\"data row1 col5\" >0.000000</td>\n",
       "      <td id=\"T_f1b7d_row1_col6\" class=\"data row1 col6\" >0.000000</td>\n",
       "      <td id=\"T_f1b7d_row1_col7\" class=\"data row1 col7\" >51</td>\n",
       "      <td id=\"T_f1b7d_row1_col8\" class=\"data row1 col8\" >25/08/2023, 19:44:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_f1b7d_row2_col0\" class=\"data row2 col0\" >Model #3</td>\n",
       "      <td id=\"T_f1b7d_row2_col1\" class=\"data row2 col1\" >112</td>\n",
       "      <td id=\"T_f1b7d_row2_col2\" class=\"data row2 col2\" >MorsE</td>\n",
       "      <td id=\"T_f1b7d_row2_col3\" class=\"data row2 col3\" >1.094242</td>\n",
       "      <td id=\"T_f1b7d_row2_col4\" class=\"data row2 col4\" >48.850000</td>\n",
       "      <td id=\"T_f1b7d_row2_col5\" class=\"data row2 col5\" >0.790000</td>\n",
       "      <td id=\"T_f1b7d_row2_col6\" class=\"data row2 col6\" >0.950000</td>\n",
       "      <td id=\"T_f1b7d_row2_col7\" class=\"data row2 col7\" >101</td>\n",
       "      <td id=\"T_f1b7d_row2_col8\" class=\"data row2 col8\" >28/08/2023, 22:27:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_f1b7d_row3_col0\" class=\"data row3 col0\" >Model #4</td>\n",
       "      <td id=\"T_f1b7d_row3_col1\" class=\"data row3 col1\" >79</td>\n",
       "      <td id=\"T_f1b7d_row3_col2\" class=\"data row3 col2\" >MorsE</td>\n",
       "      <td id=\"T_f1b7d_row3_col3\" class=\"data row3 col3\" >1.016010</td>\n",
       "      <td id=\"T_f1b7d_row3_col4\" class=\"data row3 col4\" >52.220000</td>\n",
       "      <td id=\"T_f1b7d_row3_col5\" class=\"data row3 col5\" >0.730000</td>\n",
       "      <td id=\"T_f1b7d_row3_col6\" class=\"data row3 col6\" >0.930000</td>\n",
       "      <td id=\"T_f1b7d_row3_col7\" class=\"data row3 col7\" >14</td>\n",
       "      <td id=\"T_f1b7d_row3_col8\" class=\"data row3 col8\" >19/08/2023, 17:01:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_f1b7d_row4_col0\" class=\"data row4 col0\" >Model #5</td>\n",
       "      <td id=\"T_f1b7d_row4_col1\" class=\"data row4 col1\" >100</td>\n",
       "      <td id=\"T_f1b7d_row4_col2\" class=\"data row4 col2\" >MorsE</td>\n",
       "      <td id=\"T_f1b7d_row4_col3\" class=\"data row4 col3\" >1.043159</td>\n",
       "      <td id=\"T_f1b7d_row4_col4\" class=\"data row4 col4\" >51.530000</td>\n",
       "      <td id=\"T_f1b7d_row4_col5\" class=\"data row4 col5\" >0.690000</td>\n",
       "      <td id=\"T_f1b7d_row4_col6\" class=\"data row4 col6\" >0.940000</td>\n",
       "      <td id=\"T_f1b7d_row4_col7\" class=\"data row4 col7\" >12</td>\n",
       "      <td id=\"T_f1b7d_row4_col8\" class=\"data row4 col8\" >24/08/2023, 14:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_f1b7d_row5_col0\" class=\"data row5 col0\" >Model #6</td>\n",
       "      <td id=\"T_f1b7d_row5_col1\" class=\"data row5 col1\" >92</td>\n",
       "      <td id=\"T_f1b7d_row5_col2\" class=\"data row5 col2\" >MorsE</td>\n",
       "      <td id=\"T_f1b7d_row5_col3\" class=\"data row5 col3\" >1.081074</td>\n",
       "      <td id=\"T_f1b7d_row5_col4\" class=\"data row5 col4\" >53.760000</td>\n",
       "      <td id=\"T_f1b7d_row5_col5\" class=\"data row5 col5\" >0.720000</td>\n",
       "      <td id=\"T_f1b7d_row5_col6\" class=\"data row5 col6\" >0.930000</td>\n",
       "      <td id=\"T_f1b7d_row5_col7\" class=\"data row5 col7\" >8</td>\n",
       "      <td id=\"T_f1b7d_row5_col8\" class=\"data row5 col8\" >23/08/2023, 09:42:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f1b7d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_f1b7d_row6_col0\" class=\"data row6 col0\" >Model #7</td>\n",
       "      <td id=\"T_f1b7d_row6_col1\" class=\"data row6 col1\" >80</td>\n",
       "      <td id=\"T_f1b7d_row6_col2\" class=\"data row6 col2\" >MorsE</td>\n",
       "      <td id=\"T_f1b7d_row6_col3\" class=\"data row6 col3\" >0.984329</td>\n",
       "      <td id=\"T_f1b7d_row6_col4\" class=\"data row6 col4\" >56.380000</td>\n",
       "      <td id=\"T_f1b7d_row6_col5\" class=\"data row6 col5\" >0.740000</td>\n",
       "      <td id=\"T_f1b7d_row6_col6\" class=\"data row6 col6\" >0.930000</td>\n",
       "      <td id=\"T_f1b7d_row6_col7\" class=\"data row6 col7\" >17</td>\n",
       "      <td id=\"T_f1b7d_row6_col8\" class=\"data row6 col8\" >19/08/2023, 17:09:02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fb1fc807700>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df,styler_df=kgnet.KGMeta_Governer.getGMLTaskModelsBasicInfoByID(taskId)\n",
    "styler_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfed117e",
   "metadata": {},
   "source": [
    "## Inference Pipeline\n",
    "write the SPARQL-ML inference query then execute it. KGNet will choose the model that match your task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "514548ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "                    prefix dblp2022:<https://dblp.org/rdf/schema#>\n",
    "                    prefix kgnet:<https://kgnet/>\n",
    "                    select ?publication ?Title ?pred_author                    \n",
    "                    from <https://dblp2022.org>\n",
    "                    where {\n",
    "                    ?publication a dblp2022:Publication.\n",
    "                    ?publication dblp2022:title ?Title .\n",
    "                    ?publication ?LinkPredictor ?pred_author .\n",
    "                    ?LinkPredictor  a <kgnet:types/LinkPredictor>.\n",
    "                    ?LinkPredictor  <kgnet:targetEdge> \\\"\"\"\"+TargetEdge+\"\"\"\\\" .\n",
    "                    ?LinkPredictor <kgnet:GNNMethod> \"MorsE\" .\n",
    "                    ?LinkPredictor <kgnet:topK> 1 .\n",
    "                    }\n",
    "                    limit 300\n",
    "                    offset 0\n",
    "        \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb30d958",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_df,inferenceQueryMetadata=kgnet.executeSPARQLMLInferenceQuery(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "96ec4248",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>publication</th>\n",
       "      <th>Title</th>\n",
       "      <th>pred_author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"https://dblp.org/rec/conf/ctrsa/Bultel22\"</td>\n",
       "      <td>\"CCA Secure A Posteriori Openable Encryption i...</td>\n",
       "      <td>\"https://dblp.org/pid/179/7523\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"https://dblp.org/rec/conf/ctrsa/CaoSCCFW22\"</td>\n",
       "      <td>\"Lattice-Based Fault Attacks on Deterministic ...</td>\n",
       "      <td>\"https://dblp.org/pid/91/5830\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"https://dblp.org/rec/conf/ctrsa/Chevallier-Ma...</td>\n",
       "      <td>\"A Pairing-Free Signature Scheme from Correlat...</td>\n",
       "      <td>\"https://dblp.org/pid/50/3410\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"https://dblp.org/rec/conf/ctrsa/Chi-Dominguez...</td>\n",
       "      <td>\"Fully Projective Radical Isogenies in Constan...</td>\n",
       "      <td>\"https://dblp.org/pid/289/1061\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"https://dblp.org/rec/conf/ctrsa/CidIR22\"</td>\n",
       "      <td>\"FASTA - A Stream Cipher for Fast FHE Evaluati...</td>\n",
       "      <td>\"https://dblp.org/pid/297/6133\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>\"https://dblp.org/rec/conf/wacv/CaoBTAL0SE22\"</td>\n",
       "      <td>\"Fair and accurate age prediction using distri...</td>\n",
       "      <td>\"https://dblp.org/pid/75/6343\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>\"https://dblp.org/rec/conf/wacv/CardaceLRSS22\"</td>\n",
       "      <td>\"Plugging Self-Supervised Monocular Depth into...</td>\n",
       "      <td>\"https://dblp.org/pid/31/495\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>\"https://dblp.org/rec/conf/wacv/CardaceRSS22\"</td>\n",
       "      <td>\"Shallow Features Guide Unsupervised Domain Ad...</td>\n",
       "      <td>\"https://dblp.org/pid/31/495\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>\"https://dblp.org/rec/conf/wacv/ChenGM22\"</td>\n",
       "      <td>\"Multi-level Attentive Adversarial Learning wi...</td>\n",
       "      <td>\"https://dblp.org/pid/76/2452\"</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>\"https://dblp.org/rec/conf/wacv/ChenLTP22\"</td>\n",
       "      <td>\"Mixed-dual-head Meets Box Priors: A Robust Fr...</td>\n",
       "      <td>\"https://dblp.org/pid/43/656\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           publication  \\\n",
       "0           \"https://dblp.org/rec/conf/ctrsa/Bultel22\"   \n",
       "1         \"https://dblp.org/rec/conf/ctrsa/CaoSCCFW22\"   \n",
       "2    \"https://dblp.org/rec/conf/ctrsa/Chevallier-Ma...   \n",
       "3    \"https://dblp.org/rec/conf/ctrsa/Chi-Dominguez...   \n",
       "4            \"https://dblp.org/rec/conf/ctrsa/CidIR22\"   \n",
       "..                                                 ...   \n",
       "295      \"https://dblp.org/rec/conf/wacv/CaoBTAL0SE22\"   \n",
       "296     \"https://dblp.org/rec/conf/wacv/CardaceLRSS22\"   \n",
       "297      \"https://dblp.org/rec/conf/wacv/CardaceRSS22\"   \n",
       "298          \"https://dblp.org/rec/conf/wacv/ChenGM22\"   \n",
       "299         \"https://dblp.org/rec/conf/wacv/ChenLTP22\"   \n",
       "\n",
       "                                                 Title  \\\n",
       "0    \"CCA Secure A Posteriori Openable Encryption i...   \n",
       "1    \"Lattice-Based Fault Attacks on Deterministic ...   \n",
       "2    \"A Pairing-Free Signature Scheme from Correlat...   \n",
       "3    \"Fully Projective Radical Isogenies in Constan...   \n",
       "4    \"FASTA - A Stream Cipher for Fast FHE Evaluati...   \n",
       "..                                                 ...   \n",
       "295  \"Fair and accurate age prediction using distri...   \n",
       "296  \"Plugging Self-Supervised Monocular Depth into...   \n",
       "297  \"Shallow Features Guide Unsupervised Domain Ad...   \n",
       "298  \"Multi-level Attentive Adversarial Learning wi...   \n",
       "299  \"Mixed-dual-head Meets Box Priors: A Robust Fr...   \n",
       "\n",
       "                         pred_author  \n",
       "0    \"https://dblp.org/pid/179/7523\"  \n",
       "1     \"https://dblp.org/pid/91/5830\"  \n",
       "2     \"https://dblp.org/pid/50/3410\"  \n",
       "3    \"https://dblp.org/pid/289/1061\"  \n",
       "4    \"https://dblp.org/pid/297/6133\"  \n",
       "..                               ...  \n",
       "295   \"https://dblp.org/pid/75/6343\"  \n",
       "296    \"https://dblp.org/pid/31/495\"  \n",
       "297    \"https://dblp.org/pid/31/495\"  \n",
       "298   \"https://dblp.org/pid/76/2452\"  \n",
       "299    \"https://dblp.org/pid/43/656\"  \n",
       "\n",
       "[300 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85766bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for key in inferenceQueryMetadata:\n",
    "#    print(\"####################\"+key+\"#######################\")\n",
    "#    print(inferenceQueryMetadata[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "904da75a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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