graphkit-learn/notebooks/py-graph_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import paths\n",
    "\n",
    "from ged.GED import ged\n",
    "from utils.graphfiles import loadDataset\n",
    "from ged.costfunctions import RiesenCostFunction, BasicCostFunction\n",
    "from ged.bipartiteGED import computeBipartiteCostMatrix, getOptimalMapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SKiqSFDCc4hm5XwTscs7tds59DDwHjIjaZwSwOPL9C8BlZmaJiykiGWvRouYf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\nuYuIhJDKXUQkhP4/gBCHmOhWdeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e1cd877f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4]\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot  as plt\n",
    "\n",
    "# We load a ds dataset\n",
    "dataset, y = loadDataset(\"/home/bgauzere/work/Datasets/Acyclic/dataset_bps.ds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 183/183 [03:48<00:00,  1.25s/it]\n"
     ]
    }
   ],
   "source": [
    "#Compute graph edit distances\n",
    "\n",
    "from tqdm import tqdm\n",
    "from c_ext.lsape_binders import  lsap_solverHG\n",
    "import time\n",
    "\n",
    "cf = BasicCostFunction(3,1,3,1)\n",
    "N=len(dataset)\n",
    "\n",
    "methods=['Riesen + LSAP', 'Neigh + LSAP', 'Riesen + LSAPE', 'Neigh + LSAPE']\n",
    "ged_distances = [ np.zeros((N,N)), np.zeros((N,N)), np.zeros((N,N)), np.zeros((N,N))]\n",
    "\n",
    "times = list()\n",
    "start = time.clock()\n",
    "for i in tqdm(range(0,N)):\n",
    "    for j in range(0,N):\n",
    "        ged_distances[0][i,j] = ged(dataset[i],dataset[j],cf=cf, method='Riesen')[0]\n",
    "times.append(time.clock() - start)\n",
    "\n",
    "\n",
    "start = time.clock()\n",
    "for i in tqdm(range(0,N)):\n",
    "    for j in range(0,N):\n",
    "        ged_distances[1][i,j] = ged(dataset[i],dataset[j],cf=cf, method='Neighboorhood')[0]\n",
    "\n",
    "times.append(time.clock() - start)\n",
    "\n",
    "start = time.clock()\n",
    "for i in tqdm(range(0,N)):\n",
    "    for j in range(0,N):\n",
    "        ged_distances[2][i,j] = ged(dataset[i],dataset[j],cf=cf, method='Riesen',solver=lsap_solverHG)[0]\n",
    "times.append(time.clock() - start)\n",
    "\n",
    "start = time.clock()\n",
    "for i in tqdm(range(0,N)):\n",
    "    for j in range(0,N):\n",
    "        ged_distances[3][i,j] = ged(dataset[i],dataset[j],cf=cf, method='Neighboorhood',solver=lsap_solverHG)[0]\n",
    "times.append(time.clock() - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "autoscroll": false,
    "collapsed": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " method \t mean \t mean \t dev \t time\n",
      " Riesen + LSAP \t  0.0 \t 0.0 \t -0.994535519125683 \t -14.283137746722804\n",
      " Neigh + LSAP \t  0.0 \t 0.0 \t -0.994535519125683 \t -14.283137746722804\n",
      " Riesen + LSAPE \t  19.770670966586042 \t 19.122338678372003 \t 0.4757055896177035 \t 4.839200931649199\n",
      " Neigh + LSAPE \t  0.0 \t 0.0 \t -0.994535519125683 \t -14.283137746722804\n"
     ]
    }
   ],
   "source": [
    "print(\" method \\t mean \\t mean \\t time\")\n",
    "data = list()\n",
    "\n",
    "for i in range(0,len(ged_distances)):\n",
    "    ged_ = np.minimum(ged_distances[i],ged_distances[i].transpose())\n",
    "    print(\" {} \\t  {} \\t {} \\t {} \".format(methods[i], np.mean(ged_distances[i]),np.mean(ged_), times[i])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  },
  "name": "py-graph_test.ipynb"
 },
 "nbformat": 4,
 "nbformat_minor": 2
}