resources/openpredict-pyrdf2vec-embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate RDF Knowledge graph from spreadsheet\n",
    "\n",
    "1. Original input file on Google Docs: https://docs.google.com/spreadsheets/d/11SElScyLIs0RANYcT2MpWa7QWf-gIB13fjNvV9b5ZBE/export?format=xlsx&id=11SElScyLIs0RANYcT2MpWa7QWf-gIB13fjNvV9b5ZBE \n",
    "\n",
    "2. We use a GitHub Actions workflow to generate the NeuroDKG: https://github.com/MaastrichtU-IDS/neuro_dkg/blob/master/.github/workflows/generate-rdf.yml\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use this notebook\n",
    "\n",
    "To run this notebook, start a `jupyter/all-spark-notebook` container using `docker-compose up` from the `docs/` folder\n",
    "\n",
    "```\n",
    "cd docs\n",
    "docker-compose up\n",
    "```\n",
    "\n",
    "Access on http://localhost:8888"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyRDF2vec in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 1)) (0.1.1)\n",
      "Requirement already satisfied: gensim in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (3.8.3)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 3)) (1.19.4)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 4)) (1.1.4)\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 5)) (3.3.2)\n",
      "Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 6)) (0.23.2)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 7)) (1.5.3)\n",
      "Requirement already satisfied: findspark in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 8)) (1.3.0)\n",
      "Requirement already satisfied: pyspark in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 9)) (3.0.1)\n",
      "Requirement already satisfied: rdflib in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 10)) (5.0.0)\n",
      "Requirement already satisfied: SPARQLWrapper<2.0.0,>=1.8.5 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (1.8.5)\n",
      "Requirement already satisfied: attrs<21.0.0,>=20.1.0 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (20.2.0)\n",
      "Requirement already satisfied: python-louvain<0.15,>=0.14 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (0.14)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.48.2 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (4.51.0)\n",
      "Requirement already satisfied: networkx<3.0,>=2.5 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (2.5)\n",
      "Requirement already satisfied: mimeparse<0.2.0,>=0.1.3 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (0.1.3)\n",
      "Requirement already satisfied: tomlkit<0.8.0,>=0.7.0 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (0.7.0)\n",
      "Requirement already satisfied: flask<2.0.0,>=1.1.2 in /opt/conda/lib/python3.8/site-packages (from pyRDF2vec->-r requirements.txt (line 1)) (1.1.2)\n",
      "Requirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.8/site-packages (from gensim->-r requirements.txt (line 2)) (1.15.0)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /opt/conda/lib/python3.8/site-packages (from gensim->-r requirements.txt (line 2)) (4.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.8/site-packages (from pandas->-r requirements.txt (line 4)) (2.8.1)\n",
      "Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.8/site-packages (from pandas->-r requirements.txt (line 4)) (2020.4)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 5)) (2020.6.20)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 5)) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 5)) (8.0.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 5)) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 5)) (2.4.7)\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->-r requirements.txt (line 6)) (0.17.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->-r requirements.txt (line 6)) (2.1.0)\n",
      "Requirement already satisfied: py4j==0.10.9 in /opt/conda/lib/python3.8/site-packages (from pyspark->-r requirements.txt (line 9)) (0.10.9)\n",
      "Requirement already satisfied: isodate in /opt/conda/lib/python3.8/site-packages (from rdflib->-r requirements.txt (line 10)) (0.6.0)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /opt/conda/lib/python3.8/site-packages (from networkx<3.0,>=2.5->pyRDF2vec->-r requirements.txt (line 1)) (4.4.2)\n",
      "Requirement already satisfied: itsdangerous>=0.24 in /opt/conda/lib/python3.8/site-packages (from flask<2.0.0,>=1.1.2->pyRDF2vec->-r requirements.txt (line 1)) (1.1.0)\n",
      "Requirement already satisfied: click>=5.1 in /opt/conda/lib/python3.8/site-packages (from flask<2.0.0,>=1.1.2->pyRDF2vec->-r requirements.txt (line 1)) (7.1.2)\n",
      "Requirement already satisfied: Jinja2>=2.10.1 in /opt/conda/lib/python3.8/site-packages (from flask<2.0.0,>=1.1.2->pyRDF2vec->-r requirements.txt (line 1)) (2.11.2)\n",
      "Requirement already satisfied: Werkzeug>=0.15 in /opt/conda/lib/python3.8/site-packages (from flask<2.0.0,>=1.1.2->pyRDF2vec->-r requirements.txt (line 1)) (1.0.1)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/lib/python3.8/site-packages (from Jinja2>=2.10.1->flask<2.0.0,>=1.1.2->pyRDF2vec->-r requirements.txt (line 1)) (1.1.1)\n"
     ]
    }
   ],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying out the official pyRDF2Vec\n",
    "\n",
    "Documentation: https://pyrdf2vec.readthedocs.io/en/latest/readme.html#create-a-knowledge-graph-object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from pyrdf2vec.graphs import KG\n",
    "\n",
    "# Download the NeuroDKG turtle file if not present\n",
    "os.system(\"wget -N https://raw.githubusercontent.com/MaastrichtU-IDS/neuro_dkg/6dc99ccc112b1cfcb1ae12494fe474fc7f910ee1/data/rdf/neurodkg.ttl\")\n",
    "label_predicates = [\"http://www.w3.org/1999/02/22-rdf-syntax-ns#type\"]\n",
    "kg = KG(location=\"neurodkg.ttl\", label_predicates=label_predicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'http://www.w3id.org/neurodkg/Instances/Treatment1_TG95', 'http://www.w3id.org/neurodkg/Instances/context68', 'http://www.w3id.org/neurodkg/Instances/TargetGroup75', 'http://www.w3id.org/neurodkg/Instances/context99', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG77', 'http://www.w3id.org/neurodkg/Instances/context166', 'http://www.w3id.org/neurodkg/Instances/context19', 'http://www.w3id.org/neurodkg/Instances/TargetGroup156', 'http://www.w3id.org/neurodkg/Instances/context86', 'http://www.w3id.org/neurodkg/Instances/TargetGroup49', 'http://www.w3id.org/neurodkg/Instances/context109', 'http://www.w3id.org/neurodkg/Instances/context8', 'http://www.w3id.org/neurodkg/Instances/context159', 'http://www.w3id.org/neurodkg/Instances/TargetGroup128', 'http://www.w3id.org/neurodkg/Instances/TargetGroup65', 'http://www.w3id.org/neurodkg/Instances/context134', 'http://www.w3id.org/neurodkg/Instances/TargetGroup114', 'http://www.w3id.org/neurodkg/Instances/context26', 'http://www.w3id.org/neurodkg/Instances/TargetGroup140', 'http://www.w3id.org/neurodkg/Instances/TargetGroup152', 'http://www.w3id.org/neurodkg/Instances/context66', 'http://www.w3id.org/neurodkg/Instances/context70', 'http://www.w3id.org/neurodkg/Instances/TargetGroup27', 'http://www.w3id.org/neurodkg/Instances/TargetGroup2B', 'http://www.w3id.org/neurodkg/Instances/TargetGroup116', 'http://www.w3id.org/neurodkg/Instances/context141', 'http://www.w3id.org/neurodkg/Instances/TargetGroup134', 'http://www.w3id.org/neurodkg/Instances/TargetGroup86', 'http://www.w3id.org/neurodkg/Instances/TargetGroup161', 'http://www.w3id.org/neurodkg/Instances/TargetGroup70', 'http://www.w3id.org/neurodkg/Instances/context171', 'http://www.w3id.org/neurodkg/Instances/context7', 'http://www.w3id.org/neurodkg/Instances/TargetGroup11', 'http://www.w3id.org/neurodkg/Instances/TargetGroup38', 'http://www.w3id.org/neurodkg/Instances/TargetGroup124A', 'http://www.w3id.org/neurodkg/Instances/TargetGroup56', 'http://www.w3id.org/neurodkg/Instances/TargetGroup46B', 'http://www.w3id.org/neurodkg/Instances/TargetGroup21', 'http://www.w3id.org/neurodkg/Instances/TargetGroup156A', 'http://www.w3id.org/neurodkg/Instances/context84', 'http://www.w3id.org/neurodkg/Instances/context87', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG56', 'http://www.w3id.org/neurodkg/Instances/TargetGroup8', 'http://www.w3id.org/neurodkg/Instances/TargetGroup92', 'http://www.w3id.org/neurodkg/Instances/context129', 'http://www.w3id.org/neurodkg/Instances/TargetGroup119', 'http://www.w3id.org/neurodkg/Instances/context120', 'http://www.w3id.org/neurodkg/Instances/context42', 'http://www.w3id.org/neurodkg/Instances/context69', 'http://www.w3id.org/neurodkg/Instances/TargetGroup110', 'http://www.w3id.org/neurodkg/Instances/context59', 'http://www.w3id.org/neurodkg/Instances/context48', 'http://www.w3id.org/neurodkg/Instances/TargetGroup136B', 'http://www.w3id.org/neurodkg/Instances/TargetGroup36A', 'http://www.w3id.org/neurodkg/Instances/context160', 'http://www.w3id.org/neurodkg/Instances/TargetGroup42', 'http://www.w3id.org/neurodkg/Instances/TargetGroup10', 'http://www.w3id.org/neurodkg/Instances/TargetGroup3', 'http://www.w3id.org/neurodkg/Instances/TargetGroup36B', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG74', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG156A', 'http://www.w3id.org/neurodkg/Instances/context32', 'http://www.w3id.org/neurodkg/Instances/context150', 'http://www.w3id.org/neurodkg/Instances/context151', 'http://www.w3id.org/neurodkg/Instances/context103', 'http://www.w3id.org/neurodkg/Instances/context108', 'http://www.w3id.org/neurodkg/Instances/TargetGroup156C', 'http://www.w3id.org/neurodkg/Instances/context77', 'http://www.w3id.org/neurodkg/Instances/context95', 'http://www.w3id.org/neurodkg/Instances/context21', 'http://www.w3id.org/neurodkg/Instances/TargetGroup132', 'http://www.w3id.org/neurodkg/Instances/context140', 'http://www.w3id.org/neurodkg/Instances/context51', 'http://www.w3id.org/neurodkg/Instances/TargetGroup9', 'http://www.w3id.org/neurodkg/Instances/TargetGroup144', 'http://www.w3id.org/neurodkg/Instances/context52', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG1', 'http://www.w3id.org/neurodkg/Instances/TargetGroup143', 'http://www.w3id.org/neurodkg/Instances/context9', 'http://www.w3id.org/neurodkg/Instances/context142', 'http://www.w3id.org/neurodkg/Instances/context169', 'http://www.w3id.org/neurodkg/Instances/context73', 'http://www.w3id.org/neurodkg/Instances/context123', 'http://www.w3id.org/neurodkg/Instances/context79', 'http://www.w3id.org/neurodkg/Instances/context139', 'http://www.w3id.org/neurodkg/Instances/TargetGroup33', 'http://www.w3id.org/neurodkg/Instances/context161', 'http://www.w3id.org/neurodkg/Instances/context102', 'http://www.w3id.org/neurodkg/Instances/TargetGroup46A', 'http://www.w3id.org/neurodkg/Instances/TargetGroup50', 'http://www.w3id.org/neurodkg/Instances/context58', 'http://www.w3id.org/neurodkg/Instances/TargetGroup165', 'http://www.w3id.org/neurodkg/Instances/context115', 'http://www.w3id.org/neurodkg/Instances/TargetGroup131', 'http://www.w3id.org/neurodkg/Instances/TargetGroup133', 'http://www.w3id.org/neurodkg/Instances/context43', 'http://www.w3id.org/neurodkg/Instances/context144', 'http://www.w3id.org/neurodkg/Instances/context88', 'http://www.w3id.org/neurodkg/Instances/context148', 'http://www.w3id.org/neurodkg/Instances/context65', 'http://www.w3id.org/neurodkg/Instances/TargetGroup5', 'http://www.w3id.org/neurodkg/Instances/TargetGroup40', 'http://www.w3id.org/neurodkg/Instances/context170', 'http://www.w3id.org/neurodkg/Instances/TargetGroup52', 'http://www.w3id.org/neurodkg/Instances/context137', 'http://www.w3id.org/neurodkg/Instances/context25', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG94', 'http://www.w3id.org/neurodkg/Instances/context5', 'http://www.w3id.org/neurodkg/Instances/context153', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG139', 'http://www.w3id.org/neurodkg/Instances/TargetGroup2C', 'http://www.w3id.org/neurodkg/Instances/TargetGroup125', 'http://www.w3id.org/neurodkg/Instances/TargetGroup12A', 'http://www.w3id.org/neurodkg/Instances/context113', 'http://www.w3id.org/neurodkg/Instances/TargetGroup95', 'http://www.w3id.org/neurodkg/Instances/TargetGroup93', 'http://www.w3id.org/neurodkg/Instances/context31', 'http://www.w3id.org/neurodkg/Instances/context158', 'http://www.w3id.org/neurodkg/Instances/context49', 'http://www.w3id.org/neurodkg/Instances/TargetGroup83B', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG3', 'http://www.w3id.org/neurodkg/Instances/context118', 'http://www.w3id.org/neurodkg/Instances/TargetGroup31', 'http://www.w3id.org/neurodkg/Instances/TargetGroup43', 'http://www.w3id.org/neurodkg/Instances/context62', 'http://www.w3id.org/neurodkg/Instances/TargetGroup28', 'http://www.w3id.org/neurodkg/Instances/context154', 'http://www.w3id.org/neurodkg/Instances/context54', 'http://www.w3id.org/neurodkg/Instances/TargetGroup14', 'http://www.w3id.org/neurodkg/Instances/Treatment1_TG28', 'http://www.w3id.org/neurodkg/Instances/TargetGroup72', 'http://www.w3id.org/neurodkg/Instances/context136', 'http://www.w3id.org/neurodkg/Instances/TargetGroup1', 'http://www.w3id.org/neurodkg/Instances/TargetGroup23', 'http://www.w3id.org/neurodkg/Instances/context81', 'http://www.w3id.org/neurodkg/Instances/context35', 'http://www.w3id.org/neurodkg/Instances/TargetGroup96B', 'http://www.w3id.org/neurodkg/Instances/context157', 'http://www.w3id.org/neurodkg/Instances/context104', 'http://www.w3id.org/neurodkg/Instances/TargetGroup58', 'http://www.w3id.org/neurodkg/Instances/context162', 'http://www.w3id.org/neurodkg/Instances/context145', 'http://www.w3id.org/neurodkg/Instances/TargetGroup7', 'http://www.w3id.org/neurodkg/Instances/TargetGroup37', 'http://www.w3id.org/neurodkg/Instances/context75', 'http://www.w3id.org/neurodkg/Instances/context117']\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'extract'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-db8ede750414>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0mtransformer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRDF2VecTransformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwalkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwalkers\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# \"Entities should be a list of URIs that can be found in the Knowledge Graph\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransformer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pyrdf2vec/rdf2vec.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, kg, entities)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \"\"\"\n\u001b[0;32m--> 117\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/pyrdf2vec/rdf2vec.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, kg, entities, verbose)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mwalker\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalkers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalks_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwalker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mentities\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m         \u001b[0mcorpus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwalks_\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'extract'"
     ]
    }
   ],
   "source": [
    "from pyrdf2vec.samplers import UniformSampler\n",
    "from pyrdf2vec.walkers import RandomWalker\n",
    "from pyrdf2vec import RDF2VecTransformer\n",
    "\n",
    "walkers = [RandomWalker(4, 5, UniformSampler())]\n",
    "\n",
    "# Get entities from the KG\n",
    "entities = set([])\n",
    "for v in kg._entities:\n",
    "    if v.name.startswith('http://www.w3id.org/neurodkg/Instances'):\n",
    "        entities.add(v.name)\n",
    "entities = list(entities)\n",
    "print(entities)\n",
    "\n",
    "transformer = RDF2VecTransformer(walkers=[walkers])\n",
    "# \"Entities should be a list of URIs that can be found in the Knowledge Graph\"\n",
    "embeddings = transformer.fit_transform(kg, entities)\n",
    "\n",
    "print(embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Remzi's pyRDF2Vec\n",
    "\n",
    "At https://github.com/MaastrichtU-IDS/pyRDF2Vec/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import os\n",
    "import requests\n",
    "import functools\n",
    "import numpy as np\n",
    "import rdflib\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import shutil\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "from rdf2vec.converters import rdflib_to_kg\n",
    "from rdf2vec.walkers import RandomWalker\n",
    "from rdf2vec import RDF2VecTransformer\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the rdf file (ttl, nt, all other supported by rdflib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/MaastrichtU-IDS/neuro_dkg/master/data/output/neuro_dkg.ttl'\n",
    "rdf_file ='neurodkg.ttl'\n",
    "# rdf_file = url.split('/')[-1]\n",
    "#rdf_file = 'input/covid19-literature-knowledge-graph/sample_kg.nt'\n",
    "#fileext = '.nq.gz'\n",
    "\n",
    "# Download the RDF file\n",
    "with requests.get(url, stream=True) as r:\n",
    "    with open(rdf_file, 'wb') as f:\n",
    "        r.raw.read = functools.partial(r.raw.read, decode_content=True)\n",
    "        shutil.copyfileobj(r.raw, f)\n",
    "\n",
    "#predicates for Random Walker to follow\n",
    "label_predicates = ['<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1421/1421 [00:00<00:00, 24554.67it/s]\n"
     ]
    }
   ],
   "source": [
    "kg = rdflib_to_kg(rdf_file, filetype='turtle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<SparkContext master=local[10] appName=pyspark-shell>\n"
     ]
    }
   ],
   "source": [
    "# We'll all possible walks of depth 2\n",
    "random_walker = RandomWalker(2, 4)\n",
    "\n",
    "# Create embeddings with random walks\n",
    "transformer = RDF2VecTransformer(walkers=[random_walker], sg=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_entities = kg.get_all_entities()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['http://www.w3id.org/neurodkg/Instances/context169',\n",
       " 'http://www.w3id.org/neurodkg/Instances/context8',\n",
       " 'http://purl.bioontology.org/ontology/MEDDRA/10048013',\n",
       " 'http://www.w3id.org/neurodkg/Instances/TargetGroup74',\n",
       " 'Lithium',\n",
       " 'http://www.w3id.org/neurodkg/Instances/context141',\n",
       " 'http://www.w3id.org/neurodkg/Instances/TargetGroup54',\n",
       " 'generalized seizures',\n",
       " 'http://www.w3id.org/drugbank:DB00252',\n",
       " 'http://www.w3id.org/neurodkg/Instances/context24']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_entities[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./walks/randwalks_n4_depth2_pagerank_uniform.txt\n",
      "Time elapsed to generate features: 00:00:05\n",
      "Extracted 0 walks for 967 instances!\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n",
      "Processing  uniform\n"
     ]
    }
   ],
   "source": [
    "walk_embeddings = transformer.fit_transform(kg, all_entities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([ 1.23020168e-02, -4.67225090e-02, -6.15784526e-02, -2.88769137e-02,\n",
       "         4.28201519e-02,  3.36477044e-03, -5.46489842e-02,  1.97499562e-02,\n",
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       "       dtype=float32),\n",
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       "         2.96208105e-04,  6.90666586e-02,  3.24263945e-02, -6.00500740e-02,\n",
       "        -4.72745486e-02,  5.59838042e-02, -5.56321368e-02,  7.09747970e-02,\n",
       "         3.33050545e-03,  3.18746492e-02, -7.32763186e-02, -4.00800779e-02,\n",
       "        -4.38502580e-02,  2.40485463e-02,  6.59321696e-02,  2.87684724e-02,\n",
       "        -8.04984793e-02, -1.60414483e-02,  1.32762473e-02, -1.11004384e-02,\n",
       "         9.00485665e-02, -2.45736502e-02, -2.16662791e-02,  4.70624631e-03,\n",
       "         7.86756910e-03, -6.86218496e-03, -1.04243496e-04,  1.69826075e-02,\n",
       "        -6.14554211e-02,  3.39617878e-02,  7.99183324e-02,  1.71396800e-03],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walk_embeddings[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "967"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_entities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "967"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(walk_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating a dataframe for entity embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =pd.DataFrame(zip(all_entities, walk_embeddings), columns=['entity', 'embedding'])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a function for converting entity names\n",
    "# if you need to provide entity names with CURIE format (e.g. DRUGBANK:DB00012)\n",
    "def replace_prefix(entity):\n",
    "    if entity.startswith('http://www.w3id.org/drugbank:'):\n",
    "        return entity.replace('http://www.w3id.org/drugbank:', 'DRUGBANK:')\n",
    "    else:\n",
    "        return entity\n",
    "\n",
    "df.entity = df.entity.apply(replace_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert dataframe embeddings to JSON\n",
    "\n",
    "And store the dataframe in a JSON file, to be imported in the OpenPredict API!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json('neurodkg_embedding.json',orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df =pd.read_json('neurodkg_embedding.json',orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>entity</th>\n",
       "      <th>embedding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://www.w3id.org/neurodkg/Instances/context169</td>\n",
       "      <td>[0.012302016800000001, -0.046722509, -0.061578...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://www.w3id.org/neurodkg/Instances/context8</td>\n",
       "      <td>[0.0116026448, -0.0446706526, -0.0590210706, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://purl.bioontology.org/ontology/MEDDRA/10...</td>\n",
       "      <td>[0.0079595456, -0.029062213400000002, -0.03673...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://www.w3id.org/neurodkg/Instances/TargetG...</td>\n",
       "      <td>[0.0154349497, -0.0612307899, -0.0794833302000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lithium</td>\n",
       "      <td>[-0.0002488188, -8.322100000000001e-05, 0.0003...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              entity  \\\n",
       "0  http://www.w3id.org/neurodkg/Instances/context169   \n",
       "1    http://www.w3id.org/neurodkg/Instances/context8   \n",
       "2  http://purl.bioontology.org/ontology/MEDDRA/10...   \n",
       "3  http://www.w3id.org/neurodkg/Instances/TargetG...   \n",
       "4                                            Lithium   \n",
       "\n",
       "                                           embedding  \n",
       "0  [0.012302016800000001, -0.046722509, -0.061578...  \n",
       "1  [0.0116026448, -0.0446706526, -0.0590210706, -...  \n",
       "2  [0.0079595456, -0.029062213400000002, -0.03673...  \n",
       "3  [0.0154349497, -0.0612307899, -0.0794833302000...  \n",
       "4  [-0.0002488188, -8.322100000000001e-05, 0.0003...  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      http://www.w3id.org/neurodkg/Instances/context169\n",
      "1        http://www.w3id.org/neurodkg/Instances/context8\n",
      "2      http://purl.bioontology.org/ontology/MEDDRA/10...\n",
      "3      http://www.w3id.org/neurodkg/Instances/TargetG...\n",
      "4                                                Lithium\n",
      "                             ...                        \n",
      "962                                     DRUGBANK:DB00193\n",
      "963                                     DRUGBANK:DB01086\n",
      "964                                           ziconotide\n",
      "965    http://www.w3id.org/neurodkg/Instances/TargetG...\n",
      "966    http://www.w3id.org/neurodkg/Instances/TargetG...\n",
      "Name: entity, Length: 967, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df['entity'])\n",
    "# np.array(df['embedding'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_mat =[]\n",
    "for i, row in df.iterrows():\n",
    "    emb=row['embedding']\n",
    "    embedding_mat.append(emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "entities = df.entity.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### alternatively you can store as csv with X columns (X is the dimension of the emebdding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_emb =pd.DataFrame( embedding_mat, columns= ['feature'+str(i) for i in range(len(emb))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_emb['entity'] = entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_emb.to_csv('neurodkg_embedding.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>feature0</th>\n",
       "      <th>feature1</th>\n",
       "      <th>feature2</th>\n",
       "      <th>feature3</th>\n",
       "      <th>feature4</th>\n",
       "      <th>feature5</th>\n",
       "      <th>feature6</th>\n",
       "      <th>feature7</th>\n",
       "      <th>feature8</th>\n",
       "      <th>feature9</th>\n",
       "      <th>...</th>\n",
       "      <th>feature491</th>\n",
       "      <th>feature492</th>\n",
       "      <th>feature493</th>\n",
       "      <th>feature494</th>\n",
       "      <th>feature495</th>\n",
       "      <th>feature496</th>\n",
       "      <th>feature497</th>\n",
       "      <th>feature498</th>\n",
       "      <th>feature499</th>\n",
       "      <th>entity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.012302</td>\n",
       "      <td>-0.046723</td>\n",
       "      <td>-0.061578</td>\n",
       "      <td>-0.028877</td>\n",
       "      <td>0.042820</td>\n",
       "      <td>0.003365</td>\n",
       "      <td>-0.054649</td>\n",
       "      <td>0.019750</td>\n",
       "      <td>0.022355</td>\n",
       "      <td>0.023803</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003829</td>\n",
       "      <td>0.007926</td>\n",
       "      <td>-0.005846</td>\n",
       "      <td>-0.000503</td>\n",
       "      <td>0.016634</td>\n",
       "      <td>-0.061863</td>\n",
       "      <td>0.035371</td>\n",
       "      <td>0.081102</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>http://www.w3id.org/neurodkg/Instances/context169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.011603</td>\n",
       "      <td>-0.044671</td>\n",
       "      <td>-0.059021</td>\n",
       "      <td>-0.026219</td>\n",
       "      <td>0.042432</td>\n",
       "      <td>0.002738</td>\n",
       "      <td>-0.053726</td>\n",
       "      <td>0.017745</td>\n",
       "      <td>0.022102</td>\n",
       "      <td>0.023573</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004447</td>\n",
       "      <td>0.007249</td>\n",
       "      <td>-0.005877</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.016213</td>\n",
       "      <td>-0.060918</td>\n",
       "      <td>0.033026</td>\n",
       "      <td>0.078968</td>\n",
       "      <td>0.001730</td>\n",
       "      <td>http://www.w3id.org/neurodkg/Instances/context8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007960</td>\n",
       "      <td>-0.029062</td>\n",
       "      <td>-0.036739</td>\n",
       "      <td>-0.017166</td>\n",
       "      <td>0.026013</td>\n",
       "      <td>0.003133</td>\n",
       "      <td>-0.033342</td>\n",
       "      <td>0.011258</td>\n",
       "      <td>0.014057</td>\n",
       "      <td>0.014073</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002516</td>\n",
       "      <td>0.004570</td>\n",
       "      <td>-0.004586</td>\n",
       "      <td>-0.000352</td>\n",
       "      <td>0.009320</td>\n",
       "      <td>-0.039472</td>\n",
       "      <td>0.022204</td>\n",
       "      <td>0.050009</td>\n",
       "      <td>0.000899</td>\n",
       "      <td>http://purl.bioontology.org/ontology/MEDDRA/10...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.015435</td>\n",
       "      <td>-0.061231</td>\n",
       "      <td>-0.079483</td>\n",
       "      <td>-0.035929</td>\n",
       "      <td>0.055599</td>\n",
       "      <td>0.004625</td>\n",
       "      <td>-0.072390</td>\n",
       "      <td>0.025209</td>\n",
       "      <td>0.029083</td>\n",
       "      <td>0.030716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.010459</td>\n",
       "      <td>-0.007804</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.020397</td>\n",
       "      <td>-0.082394</td>\n",
       "      <td>0.045698</td>\n",
       "      <td>0.104531</td>\n",
       "      <td>0.002276</td>\n",
       "      <td>http://www.w3id.org/neurodkg/Instances/TargetG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.000249</td>\n",
       "      <td>-0.000083</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000773</td>\n",
       "      <td>0.000971</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>-0.000630</td>\n",
       "      <td>-0.000779</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000348</td>\n",
       "      <td>-0.000773</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>-0.000130</td>\n",
       "      <td>-0.000326</td>\n",
       "      <td>-0.000675</td>\n",
       "      <td>-0.000712</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>-0.000928</td>\n",
       "      <td>Lithium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature0  feature1  feature2  feature3  feature4  feature5  feature6  \\\n",
       "0  0.012302 -0.046723 -0.061578 -0.028877  0.042820  0.003365 -0.054649   \n",
       "1  0.011603 -0.044671 -0.059021 -0.026219  0.042432  0.002738 -0.053726   \n",
       "2  0.007960 -0.029062 -0.036739 -0.017166  0.026013  0.003133 -0.033342   \n",
       "3  0.015435 -0.061231 -0.079483 -0.035929  0.055599  0.004625 -0.072390   \n",
       "4 -0.000249 -0.000083  0.000321  0.000216  0.000773  0.000971  0.000285   \n",
       "\n",
       "   feature7  feature8  feature9  ...  feature491  feature492  feature493  \\\n",
       "0  0.019750  0.022355  0.023803  ...    0.003829    0.007926   -0.005846   \n",
       "1  0.017745  0.022102  0.023573  ...    0.004447    0.007249   -0.005877   \n",
       "2  0.011258  0.014057  0.014073  ...    0.002516    0.004570   -0.004586   \n",
       "3  0.025209  0.029083  0.030716  ...    0.005269    0.010459   -0.007804   \n",
       "4  0.000631 -0.000630 -0.000779  ...   -0.000348   -0.000773    0.000499   \n",
       "\n",
       "   feature494  feature495  feature496  feature497  feature498  feature499  \\\n",
       "0   -0.000503    0.016634   -0.061863    0.035371    0.081102    0.001887   \n",
       "1    0.000358    0.016213   -0.060918    0.033026    0.078968    0.001730   \n",
       "2   -0.000352    0.009320   -0.039472    0.022204    0.050009    0.000899   \n",
       "3    0.000284    0.020397   -0.082394    0.045698    0.104531    0.002276   \n",
       "4   -0.000130   -0.000326   -0.000675   -0.000712    0.000320   -0.000928   \n",
       "\n",
       "                                              entity  \n",
       "0  http://www.w3id.org/neurodkg/Instances/context169  \n",
       "1    http://www.w3id.org/neurodkg/Instances/context8  \n",
       "2  http://purl.bioontology.org/ontology/MEDDRA/10...  \n",
       "3  http://www.w3id.org/neurodkg/Instances/TargetG...  \n",
       "4                                            Lithium  \n",
       "\n",
       "[5 rows x 501 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_emb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "              "
   ]
  }
 ],
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