graphkit-learn/notebooks/test_networkx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, {}), (2, {}), (3, {})]\n",
      "[(1, 2, {}), (2, 1, {}), (3, 1, {})]\n",
      "{2: {}}\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "dg = nx.DiGraph()\n",
    "dg.add_nodes_from([1, 2, 3])\n",
    "dg.add_edges_from([(1, 2), (2, 1), (3, 1)])\n",
    "print(dg.nodes(data=True))\n",
    "print(dg.edges(data=True))\n",
    "print(dg[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataset(filename):\n",
    "    from os.path import dirname, splitext\n",
    "\n",
    "    dirname_dataset = dirname(filename)\n",
    "    extension = splitext(filename)[1][1:]\n",
    "    data = []\n",
    "    y = []\n",
    "    if(extension == \"ds\"):\n",
    "        content = open(filename).read().splitlines()\n",
    "        for i in range(0, len(content)):\n",
    "            tmp = content[i].split(' ')\n",
    "            data.append(loadCT(dirname_dataset + '/' + tmp[0]))\n",
    "            y.append(float(tmp[1]))\n",
    "    elif(extension == \"cxl\"):\n",
    "        import xml.etree.ElementTree as ET\n",
    "\n",
    "        tree = ET.parse(filename)\n",
    "        root = tree.getroot()\n",
    "        data = []\n",
    "        y = []\n",
    "        for graph in root.iter('print'):\n",
    "            mol_filename = graph.attrib['file']\n",
    "            mol_class = graph.attrib['class']\n",
    "            data.append(loadGXL(dirname_dataset + '/' + mol_filename))\n",
    "            y.append(mol_class)\n",
    "\n",
    "    return data, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-7ebb4e3a81de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-48607ff671f8>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0my\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      8\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\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     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds'"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-fe7b61e2fb4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-48607ff671f8>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0my\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      8\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\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     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-fe7b61e2fb4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-48607ff671f8>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0my\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      8\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\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     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-90ad1e087423>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-48607ff671f8>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0my\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      8\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\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     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '~/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-388fbbf72f9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"~/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-48607ff671f8>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0my\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      8\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\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     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '~/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds'"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"~/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "vars() argument must have __dict__ attribute",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-061ad4023bd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/home/ljia/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-15-e4695123992f>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     12\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;31mTypeError\u001b[0m: vars() argument must have __dict__ attribute"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"/home/ljia/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'loadCT' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-d5015a571752>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloadDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../../datasets/acyclic/Acyclic/dataset_bps.ds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-2-48607ff671f8>\u001b[0m in \u001b[0;36mloadDataset\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\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     11\u001b[0m             \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontent\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\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[0;32m---> 12\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloadCT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdirname_dataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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     13\u001b[0m             \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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     14\u001b[0m     \u001b[0;32melif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cxl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'loadCT' is not defined"
     ]
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"../../datasets/acyclic/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadDataset(filename):\n",
    "    from os.path import dirname, splitext\n",
    "\n",
    "    dirname_dataset = dirname(filename)\n",
    "    extension = splitext(filename)[1][1:]\n",
    "    data = []\n",
    "    y = []\n",
    "    if(extension == \"ds\"):\n",
    "        content = open(filename).read().splitlines()\n",
    "        for i in range(0, len(content)):\n",
    "            tmp = content[i].split(' ')\n",
    "            data.append(loadCT(dirname_dataset + '/' + tmp[0].replace('#', '', 1)))\n",
    "            y.append(float(tmp[1]))\n",
    "    elif(extension == \"cxl\"):\n",
    "        import xml.etree.ElementTree as ET\n",
    "\n",
    "        tree = ET.parse(filename)\n",
    "        root = tree.getroot()\n",
    "        data = []\n",
    "        y = []\n",
    "        for graph in root.iter('print'):\n",
    "            mol_filename = graph.attrib['file']\n",
    "            mol_class = graph.attrib['class']\n",
    "            data.append(loadGXL(dirname_dataset + '/' + mol_filename))\n",
    "            y.append(mol_class)\n",
    "\n",
    "    return data, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadCT(filename):\n",
    "    content = open(filename).read().splitlines()\n",
    "    G = nx.Graph(name=str(content[0]))\n",
    "    tmp = content[1].split(\" \")\n",
    "    if tmp[0] == '':\n",
    "        nb_nodes = int(tmp[1])\n",
    "        nb_edges = int(tmp[2])\n",
    "    else:\n",
    "        nb_nodes = int(tmp[0])\n",
    "        nb_edges = int(tmp[1])\n",
    "\n",
    "    for i in range(0, nb_nodes):\n",
    "        tmp = content[i + 2].split(\" \")\n",
    "        tmp = [x for x in tmp if x != '']\n",
    "        G.add_node(i, label=tmp[3])\n",
    "\n",
    "    for i in range(0, nb_edges):\n",
    "        tmp = content[i+G.number_of_nodes()+2].split(\" \")\n",
    "        tmp = [x for x in tmp if x != '']\n",
    "        G.add_edge(int(tmp[0]) - 1, int(tmp[1]) - 1, label=int(tmp[3]))\n",
    "    return G\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def getSPLengths(G1):\n",
    "    sp = nx.shortest_path(G1)\n",
    "    distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))\n",
    "    for i in np.keys():\n",
    "        for j in np[i].keys():\n",
    "            distances[i, j] = len(sp[i][j])-1\n",
    "\n",
    "def getSPGraph(G):\n",
    "    \"\"\"Transform graph G to its corresponding shortest-paths graph.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    G : NetworkX graph\n",
    "        The graph to be tramsformed.\n",
    "        \n",
    "    Return\n",
    "    ------\n",
    "    S : NetworkX graph\n",
    "        The shortest-paths graph corresponding to G.\n",
    "        \n",
    "    Notes\n",
    "    ------\n",
    "    For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.\n",
    "    \n",
    "    References\n",
    "    ----------\n",
    "    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.\n",
    "    \"\"\"\n",
    "    return floydTransformation(G)\n",
    "            \n",
    "def floydTransformation(G):\n",
    "    \"\"\"Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    G : NetworkX graph\n",
    "        The graph to be tramsformed.\n",
    "        \n",
    "    Return\n",
    "    ------\n",
    "    S : NetworkX graph\n",
    "        The shortest-paths graph corresponding to G.\n",
    "        \n",
    "    References\n",
    "    ----------\n",
    "    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.\n",
    "    \"\"\"\n",
    "    spMatrix = nx.floyd_warshall_numpy(G) # @todo weigth label not considered\n",
    "    S = nx.Graph()\n",
    "    S.add_nodes_from(G)\n",
    "    for i in range(0, G.number_of_nodes()):\n",
    "        for j in range(0, G.number_of_nodes()):\n",
    "            print(spMatrix[i, j])\n",
    "            S.add_edge(i, j, cost = spMatrix[i, j])\n",
    "    return S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  2.  3.  1.  2.]]\n",
      "{0: {0: [0], 1: [0, 3, 1], 2: [0, 3, 4, 2], 3: [0, 3], 4: [0, 3, 4]}, 1: {0: [1, 3, 0], 1: [1], 2: [1, 3, 4, 2], 3: [1, 3], 4: [1, 3, 4]}, 2: {0: [2, 4, 3, 0], 1: [2, 4, 3, 1], 2: [2], 3: [2, 4, 3], 4: [2, 4]}, 3: {0: [3, 0], 1: [3, 1], 2: [3, 4, 2], 3: [3], 4: [3, 4]}, 4: {0: [4, 3, 0], 1: [4, 3, 1], 2: [4, 2], 3: [4, 3], 4: [4]}}\n",
      "[[ 0.  2.  3.  1.  2.]\n",
      " [ 2.  0.  3.  1.  2.]\n",
      " [ 3.  3.  0.  2.  1.]\n",
      " [ 1.  1.  2.  0.  1.]\n",
      " [ 2.  2.  1.  1.  0.]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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VZGZCly7JShgTFQERkboYMaLKWXcBmXhXyiz0H98VbaBz1a6nPqgIiIjURdu2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\nIhJi/wt6qJB07qybrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe534212fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "2.0\n",
      "3.0\n",
      "1.0\n",
      "2.0\n",
      "2.0\n",
      "0.0\n",
      "3.0\n",
      "1.0\n",
      "2.0\n",
      "3.0\n",
      "3.0\n",
      "0.0\n",
      "2.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "2.0\n",
      "0.0\n",
      "1.0\n",
      "2.0\n",
      "2.0\n",
      "1.0\n",
      "1.0\n",
      "0.0\n",
      "{(0, 1): 2.0, (1, 2): 3.0, (0, 0): 0.0, (3, 3): 0.0, (2, 2): 0.0, (4, 4): 0.0, (1, 4): 2.0, (1, 1): 0.0, (1, 3): 1.0, (2, 3): 2.0, (0, 4): 2.0, (2, 4): 1.0, (0, 3): 1.0, (3, 4): 1.0, (0, 2): 3.0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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27eNbSqnx9PQkV1dXvmUUyaNHj2jVqlU0ceJEOnr0aIFrqamp5ObmRjt27CAi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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe533d89940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset, y = loadDataset(\"/home/ljia/Documents/research-repo/datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
    "G1 = dataset[12]\n",
    "\n",
    "nx.draw_networkx(G1)\n",
    "# print(list(dataset[12][4]))\n",
    "\n",
    "l = nx.shortest_path(G1)\n",
    "\n",
    "l2 = nx.floyd_warshall_numpy(G1)\n",
    "print(np.array(l2[0]))\n",
    "print(l)\n",
    "print(l2)\n",
    "plt.show()\n",
    "\n",
    "S = getSPGraph(G1)\n",
    "nx.draw_networkx(S)\n",
    "pos = nx.spring_layout(S)\n",
    "edge_labels = nx.get_edge_attributes(S,'cost')\n",
    "print(edge_labels)\n",
    "nx.draw_networkx_edge_labels(S, pos, edge_labels = edge_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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b+vVMHDuWs7ffHmpr4445FHgIK5PYwknAu2gG94ciLH7vPe48/vjWn2+wcKH6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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5341ac908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.clear()\n",
    "G.add_edges_from([(1, 2), (1, 3)])\n",
    "G.add_node(1)\n",
    "G.add_edge(1, 2)\n",
    "G.add_node(\"spam\")        # adds node \"spam\"\n",
    "G.add_nodes_from(\"spam\")  # adds 4 nodes: 's', 'p', 'a', 'm'\n",
    "G.add_edge(3, 'm')\n",
    "K_5 = nx.random_lobster(30, 0.9, 0.9)\n",
    "nx.draw_networkx(K_5)\n",
    "import matplotlib.pyplot  as plt\n",
    "plt.show()\n",
    "G.number_of_nodes()\n",
    "G.number_of_edges()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}