link_prediction_hypergraph.ipynb

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  {
   "cell_type": "code",
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   "outputs": [],
   "source": [
    "# coding: utf-8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# parse arguments ([ConfigArgParse](https://github.com/bw2/ConfigArgParse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import config\n",
    "args = config.parse()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load dataset (hyperlinks and candidates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import data\n",
    "dataset = data.load(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get the graph (dual), features, and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = data.preProcess(dataset, args)\n",
    "# dataset is a dictionary with graph, hyperlinks, etc. as keys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# get train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import split\n",
    "trainData, testData = split.TrainTest(dataset, args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"        \n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(args.gpu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# initialise NHP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import model\n",
    "nhp, dataset, trainData, testData = model.initialiseNormalise(dataset, trainData, testData, args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train and test NHP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhp = model.train(nhp, dataset, trainData, args)\n",
    "results = model.test(nhp, dataset, testData, args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  'r': [run all cells](https://stackoverflow.com/questions/33143753/jupyter-ipython-notebooks-shortcut-for-run-all)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_ipython().run_cell_magic('javascript', '', \"\\nJupyter.keyboard_manager.command_shortcuts.add_shortcut('r', {\\n    help : 'run all cells',\\n    help_index : 'zz',\\n    handler : function (event) {\\n        IPython.notebook.execute_all_cells();\\n        return false;\\n    }}\\n);\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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