Untitled3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "module_path = os.path.abspath(os.path.join('/Users/vayer/Documents/OT/Python/GW_tests/'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "from graph import *\n",
    "import copy\n",
    "import NN,time\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import ot_distances\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from ot_distances import Wasserstein_distance as EMD\n",
    "from ot_distances import Gromov_Wasserstein_distance as WGW\n",
    "path='./data/MUTAG_2/'\n",
    "dataset=build_MUTAG_dataset(path)\n",
    "X,y=zip(*dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wgw=NN.WGW_Classifier(epsilon=0.1,alpha=10,method='shortest_path',features_metric='dirac',normalize_distance=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "emd=EMD(features_metric='dirac')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wgw.compute_similarity(X[1],X[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, -1, -1, 1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0],y[1],y[2],y[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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48z//8zh06FBEROzevTu+8IUvxPPPP1/+++EPfziq1yKYAgAAAMiIgYGB2LJl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\nlT0qpgAAAABIQjAFAAAAQBJ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CMAUAAABAEYIpAAAAAIoQTAEAAABQxMDSBQAA\nAADsC5VKpXQJVEnHFAAAAABFCKYAAAAAKEIwBQAAAEARgikAAAAAihBMAQAAAFCEYAoAAACAIgRT\nAAAAABQhmAIAAACgCMEUAAAAAEUIpgAAAAAoYmDpAgAAAAD2hUqlUroEqqRjCgAAAIAiBFMAAAAA\nFCGYAgAAAKAIwRQAAAAARQimAAAAAChCMAUAAABAEQNLFwAAAACwL1QqldIlUCUdUwAAAAAUIZgC\nAAAAoAjBFAAAAABFCKYAAAAAKEIwBQAAAEARgikAAAAAihhYugAAAACAfaFSqZQugSrpmAIAAACg\nCMEUAAAAAEUIpgAAAAAoQjAFAAAAQBGCKQAAAACKEEwBAAAAUMTA0gUAAAAA7AuVSqV0CVRJxxQA\nAAAARQimAAAAAChCMAUAAABAEYIpAAAAAIoQTAEAAABQhGAKAAAAgCIEUwAAAAAUMbB0AQAAAAD7\nQqVSKV0CVdIxBQAAAEARgikAAAAAihBMAQAAAFCEYAoAAACAIgRTAAAAABQhmAIAAACgiIGlCwAA\nAADYFyqVSukSqJKOKQAAAACKEEwBAAAAUIRgCgAAAIAiBFMAAAAAFCGYAgAAAKAIwRQAAAAARQws\nXQAAAADAvlCpVEqXQJV0TAEAAABQhGAKAAAAgCIEUwAAAAAUIZgCAAAAoAjBFAAAAABFCKYAAAAA\nKEIwBQAAAEARgikAAACgJlQqlX77sy+1trZm8uTJOeWUU7J48eI+3fP0009n3LhxPdb/8z//Mx/9\n6Eczfvz4XHjhhXnmmWe6Xb/zzjtz2mmnZeLEibn22muze/fuqmoVTAEAAADUiKVLl2bVqlVpa2vL\nzTffnO9+97u54447fu89zz//fC699NJ0dHT0WJ81a1bOPvvsrFy5MsOGDcusWbO6rn/ve99LW1tb\nFi5cmLvuuitr167tcxD2OsEUAAAAQI1YtmxZ5syZkwkTJqSlpSVz587N8uXL33T//fffn7PPPjuD\nBw/uce1f/uVfctJJJ+XCCy/MMccckxtvvDHPPvtsHn744a5nTZ8+PVOmTMmJJ56Y6667LitWrKiq\na0owBQAAAFADtm7dmueffz6TJk3qWps4cWKee+65tLe393rPD3/4w3z2s5/N/Pnze1xbu3Zt3vve\n93b9Pnjw4Bx//PF57LHHsnfv3qxbt67bs8aPH589e/bkqaee6nPNgikAAACAGrBt27ZUKpU0NTV1\nrTU2NqazszObN2/u9Z6FCxdm2rRpvV7bunVrt896/fO2bNmSnTt3Zvfu3d2uDxgwIEOHDn3TZ/Vm\nYJ93AgAAAFDU7t27s2XLll6vvfrqq0mSQYMGda29/vc3zo/qi1/96lfdPuv1z+vo6MivfvWrHs/6\n3et99X8aTF1++eX/l4+jD84999zSJcB+x3sBPXkvoHfeDejJewFvr7Vr1+aCCy7o9Zv85s6dm+Q3\nIdQbA6khQ4ZU/az6+voeIVNHR0cOP/zwNw28Ojo6qnqWjikAAACAfqKlpeVNZzht3bo1ra2taW9v\nzxFHHJHkt8f7RowYUfWzRo4cmW3btnVba29vz3HHHZdhw4alvr4+7e3tOfroo5Mkv/71r7Njx46q\nnmXGFAAAAEANaGpqyujRo7NmzZqutUceeSSjR49OY2Nj1Z83bty4PProo12/79q1K08++WQmTJiQ\nSqWSk046qduzHnvssRx00EFpbm7u8zN0TAEAAADUiPPOOy+tra0ZOXJkOjs7s2TJklx00UVd1198\n8cUMHjw4Bx988B/8rLPPPjtLly7NV7/61fzJn/xJvvKVr2TMmDFd39R3/vnnZ8GCBTn22GPT1NSU\n6667Lueee27q6+v7XK9gCgAAAKBGXHzxxdm+fXuuuOKKDBgwINOmTcv06dO7rp9zzjmZOnVqZs+e\n/Qc/68gjj8zNN9+cG264IW1tbXnPe96TW265pev6hz/84Tz77LNZsGBB9uzZkw9+8INdc676qtLZ\n2dlZ1R0AAAAAsA+YMQUAAABAEYIpAAAAAIoQTAEAAABQhGAKAAAAgCIEUwAAAAAUIZg6wHR0dGT+\n/Pl573vfm/e///254447SpcE+5WOjo589KMfzcMPP1y6FChuy5YtmTNnTk455ZRMmTIlixYtSkdH\nR+myoLhf/vKXueiiizJhwoScfvrp+drXvla6JNivzJw5M9dcc03pMoB+YmDpAvi/ddNNN+XJJ5/M\nsmXLsmnTpsybNy9HHnlkzjzzzNKlQXEdHR256qqrsn79+tKlwH5hzpw5GTp0aO6+++7s2LEj8+fP\nz4ABA/K5z32udGlQTGdnZ2bOnJlx48blO9/5Tn7xi1/kqquuyqhRo/KRj3ykdHlQ3L333psHH3ww\nn/jEJ0qXAvQTOqYOILt27cqKFSvy+c9/Ps3NzTnjjDNy8cUXZ/ny5aVLg+I2bNiQc889N5s2bSpd\nCuwXNm7cmCeeeCI33nhjjjnmmEycODFz5szJv/3bv5UuDYpqb2/P8ccfnwULFuSoo47KaaedlsmT\nJ2fNmjWlS4PiXnrppSxevDgnn3xy6VKAfkQwdQB56qmn8utf/zrjx4/vWps4cWKeeOKJglXB/mH1\n6tWZPHly7rnnnnR2dpYuB4obMWJEbr/99gwfPrxrrbOzMy+//HLBqqC8ESNGZMmSJTn44IOTJGvW\nrMnDDz+cU045pXBlUN5NN92Uj3/84znmmGNKlwL0I47yHUC2bduWoUOHZuDA3/5nb2hoyO7du7N9\n+/YMGzasYHVQ1l/8xV+ULgH2K4cddlj+6I/+qOv3zs7OLF++PO973/sKVgX7l9NPPz3PP/98/viP\n/9hYBA54Dz30UNasWZPvfve7WbBgQelygH5Ex9QBZNeuXRk0aFC3tdd/N8wWgN/n7//+7/PUU0/l\ns5/9bOlSYL9x88035x//8R/z05/+NDfccEPpcqCYjo6OfOELX8iCBQt6/P8GwB8imDqA1NfX9wig\nXv99yJAhJUoCoB9YvHhxli1bltbWVscz4HeccMIJmTJlSq655pp885vfzGuvvVa6JCji5ptvzokn\nnqirFnhLHOU7gIwcOTI7duzI3r17U1f3m0yyvb09gwcPzuGHH164OgD2RwsXLsw999yTxYsX54wz\nzihdDhT3wgsv5LHHHuv2Phx77LHZs2dPXnnllQwdOrRgdVDGqlWr8sILL2TChAlJkj179iRJvve9\n7+XRRx8tWRrQDwimDiDHHXdcBg4cmMcffzzvec97kiSPPPJITjzxxMKVAbA/+spXvpJ77rknX/rS\nl/Knf/qnpcuB/cKmTZtyxRVX5Ic//GGampqSJOvWrcvw4cOFUhywli9f3q1jcPHixUmSz33uc6VK\nAvoRwdQBZPDgwfn4xz+eBQsW5O/+7u+yZcuW3HHHHVm0aFHp0gDYz2zYsCG33nprLr300kyYMCHt\n7e1d1xobGwtWBmWddNJJOfHEEzN//vxcc8012bRpU1pbW/OZz3ymdGlQzOjRo7v9fsghhyRJxowZ\nU6IcoJ8RTB1grrnmmlx33XWZPn16DjvssFx55ZWOZsAbVCqV0iVAcT/4wQ+yd+/e3Hrrrbn11luT\n/Oab+SqVSn76058Wrg7KqaurS1tbWxYuXJjzzjsvQ4YMyQUXXJC//Mu/LF0aAPRLlc7Ozs7SRQAA\nAABw4PGtfAAAAAAUIZgCAAAAoAjBFAAAAABFCKYAAAAAKEIwBQAAAEARgikAAAAAihBMAQAAAFCE\nYAoAAACAIgRTAAAAABQhmAIAAACgCMEUAAAAAEX8f5LkXmvVwcycAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ae4d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "emd=dict()\n",
    "gw=dict()\n",
    "\n",
    "epsilons=list(np.logspace(-1,2,10))\n",
    "alphas=list(np.linspace(0.01,100,5))\n",
    "i=0\n",
    "j=0\n",
    "goo=np.zeros((len(epsilons),len(alphas)))\n",
    "\n",
    "for eps in epsilons:\n",
    "    for alpha in alphas:\n",
    "        wgw=NN.WGW_Classifier(epsilon=0.1,alpha=10,method='shortest_path',features_metric='dirac',normalize_distance=True)\n",
    "        d=wgw.compute_similarity(X[1],X[1])\n",
    "        emd[i,j]=wgw.emd.transp\n",
    "        gw[i,j]=wgw.gw.transp\n",
    "        goo[i,j]=d\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "    j=0\n",
    "fig = plt.figure(figsize=(16, 12))\n",
    "ax = fig.add_subplot(111)\n",
    "im=ax.imshow(goo,interpolation='nearest',aspect='auto')\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V7VumOUJ2i8/uQHs68lHsRfbjavNNecjw0Py8HBHxwgsvxM9//vO49dZb46ST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\n8e6778aUKVO6Hf/ABz4QERG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B1dOD89GDB58enI8eXL1a7sGe3AIAAACgtAy3AAAAACgt\nwy0AAAAASstwCwAAAIDSMtwCAAAAoLQMtwAAAAAoLcMtAAAAAEqrYbg3cKyurq6k+oaG/JfT3Nyc\ntHZjY2Pu2tTrPnToUO7apqampLXHjx+fu3b06NFJa2dZlru2o6Mjae3U+hQnnnjisK09kJTv5ZT8\nRqRlOCW/EWnXnZLfiLQMp+Q3Ii3DKfmNSMug/PZOD85HD66eHlwMPbh6enA+evDg04Pz0YOrV8s9\n2JNbAAAAAJSW4RYAAAAApWW4BQAAAEBpGW4BAAAAUFqGWwAAAACUluEWAAAAAKVVl6V+Fuwg+sEP\nfjBsa7/11ltJ9SkfoXrmmWcmrd3e3p67dvfu3Ulr79+/P3dt6rfemDFjctfW1dUlrX3OOeck1ae4\n6KKLhm3tgZQ1w6kfgZyS4ZT8RqRlOCW/EWkZTslvRFqG5bd3Zc1vhB6chx6cjwz3Tg+unh489OS3\nd3pwPnrw0Bsow57cAgAAAKC0DLcAAAAAKC3DLQAAAABKy3ALAAAAgNIy3AIAAACgtAy3AAAAACgt\nwy0AAAAASqthuDdwrPr6+qT6pqam3LUdHR3DtnbqdZ9wQv4ZZWNjY9Law3nP6+rqcte2t7cnrb1/\n//6k+lqV8r2c8r0Ukfb9lLp2ynWn5DciLcPDec9T8huRlmH57Z0enI8eXD09uBh6cPX04Hz04MGn\nB+ejB1evlnuwJ7cAAAAAKC3DLQAAAABKy3ALAAAAgNIy3AIAAACgtAy3AAAAACgtwy0AAAAASstw\nCwAAAIDSahjuDRyrvb09qX7SpEm5axcuXJi09nPPPZe79r777ktae8qUKblrP/WpTyWtPX369Ny1\nHR0dSWu/+OKLuWv379+ftPbixYuT6lNkWTZsaw8kJcMp+Y1Iy3BKfiPSMpyS34i0DKfkNyItwyn5\njUjLsPz2Tg/ORw+unh5cDD24enpwPnrw4NOD89GDq1fLPdiTWwAAAACUluEWAAAAAKVluAUAAABA\naRluAQAAAFBahlsAAAAAlJbhFgAAAAClZbgFAAAAQGkZbgEAAABQWoZbAAAAAJSW4RYAAAAApWW4\nBQAAAEBpGW4BAAAAUFqGWwAAAACUluEWAAAAAKVVl2VZNtybAAAAAIA8PLl1nHnjjTdizpw58etf\n/7qi9z///PNx7bXXxvnnnx+XXXZZPPjgg4W9Z6jl3VM1ddXebziWvB6WZVn88Ic/jMWLF8eMGTNi\nwYIF8a1vfSva29uPvqezszOmTZsWLS0t3f7MnDlzwPPLNINNdg+rNJdF9uPj7Z5QDjIc0dra2iO7\nx/75zne+ExHF99/j6Z5wfJLXnvq7J0VeW9nv20DqV61atWq4N8Fhu3fvjmXLlkVbW1tcffXVMWnS\npH7f//LLL8fSpUtj3rx5ccstt8S4cePivvvui7/7u7+LWbNmDep7hlrePVVTV+39hmPJ679bv359\nrFmzJj7xiU/EjTfeGFOnTo1//ud/jpdeeimuuuqqiIj43e9+F0888UTcfffdsXz58rjuuuviuuuu\ni2uuuSYmTJjQ57llmsEmu/+uklwW2Y+Px3vC8U+GDzvllFNiwYIFR3N75M+f//znePvtt+MrX/lK\nnHLKKYX23+PtnnD8kdee+rsnRV5b2e9bRTKGXVdXV/ajH/0omzt3bjZ37tyspaUla21tHbBu6dKl\n2ZIlS7odW7NmTTZr1qzs4MGDg/qeoZZ3T5XU5b3fkGXy+n5dXV3ZnDlzsjvvvLPb8WeeeSZraWnJ\ntm/fnmVZlj3xxBPZtGnTss7OzqrOL9MMFtntqZJcFtmPj8d7wvFLhge2devW7Nxzz802b9589FiR\n/bcM94ThIa89VXJPiry2st63avhriQW7//7748Mf/nDs2rWrz/e8+uqrsWrVqrj66qtj9erVkVXw\na9A6OzujtbU1FixY0O345ZdfHu3t7bFt27ZBe89gG+ie5N1TpXV57jcjg7z2NNA9aW9vj6uuuioW\nLVrU7fjUqVMjy7L44x//GBERr7zySkydOjUaGxsrXlumqZTs9lTJPRkol0X24+G4Jxy/ZLinSu7J\nsQ4ePBjf/OY345JLLomFCxcePV5U/5XhkUteexqMe1LktR2v922wGW7l9MYbb8TKlSvjC1/4QqxY\nsSJ27tzZ6/uuu+66ePzxx2P8+PF9nuvMM8+MLVu2xIoVK2LUqFFRV1c34Pp/+tOf4t13340pU6Z0\nO/6BD3wgIiJef/31QXtPX3bu3BnLly+PGTNmxPLly7v9fp3169f3+S+xge5J3j1VWjdx4sSq7zfl\nJq/F5XXs2LFx++23x4wZM7od37p1a9TV1cU555wTEYf/+lN9fX0sW7YsZsyYEXPnzo2vfvWrceDA\ngT73LNPIbnHZjRg4l0X245R7QnnIcLEZPtbDDz8cbW1tcdttt3U7XlT/leHaI6/F5nWge1LktY2U\nTGNv1DkAACAASURBVDcM9wbKaMeOHXH99dfH6tWr44ILLuj3vaeffnqcfvrp/b5n3LhxMW7cuKr2\ncCRoo0eP7nb8yD8fOHBg0N7T1/o33nhjzJ8/PxYuXBgvvPBCfP3rX481a9bEO++8EyeddFKf/xIb\n6J6k7KmSupNPPjlOPvnkPtentshrsXntzW9+85tYv359XHrppfHBD34wIg7/36qIiCVLlsRNN90U\n27dvj3Xr1sXrr78ejz76aJ/7ruR6Zbo2yW7x2R0ol0X247znpjxkeOj677vvvhuPPPJILFq0KM46\n66xurxXVf2W4tshr8Xkd6J4UfW1Fnft4YriVwx133BGzZs0aMPhF6urq6vf1urq6QXtPbx577LFY\nu3ZttLS0RETEJz/5ybj11lvjwIED8fTTT8fixYv7PW9/8u4pbx21TV6Lzev7bdu2LW666aaYPHly\n3HXXXRFx+NMUH3jggTj11FOPDrtmz54dzc3N8eUvfzmee+65mD9/fo9zyfTIJrvFZreSXI4ZMybX\nvou8J5SHDA9d//3Zz34Wb775Zixbtqzb8SL7rwzXFnkd2p+Xe1PktY2UTPtriVXas2dPtLa2xttv\nvx0rV66MlStXxrPPPjvk+xg7dmxE9JygHpm4jh07dtDe05tFixYdDf4RS5YsiV/96ldx6NChPusq\nkXdPeeuoXfJ6WJF5PdZPfvKTWLp0aUyaNCkeeuiho09T1dXVxZw5c47+YH3ExRdfHFmWxSuvvNLr\n+WR65JLdw4rMbiW5LLIfy3dtk+HDhqr/bt68Oc4+++yjvwrgiCL7rwzXDnk9bKjy2pcir22kZNqT\nW1XavXt31NXVxbe//e2K/w58ESZPnhz19fVHf1nzETt27IiIiLPPPntQ3vP+ZnhEbx/jOnny5PjG\nN74Rn/vc5/Jd1DHnybOnvHXULnk9rMi8HrFhw4ZYu3ZtzJs3L9atW9ftiY+2trZ49tlnY/78+XHG\nGWccPd7R0REREaeeemqv55TpkUt2DysyuwPlsrm5ubB+nHJPKAcZPmwo+u97770Xzz//fHzmM5/p\n8VpR/VeGa4u8HjYUee1Pkdc2UjLtya0qTZgwISKiqk8cKUJTU1PMnj07Nm/e3O34pk2bYty4cXHe\n/8fe3QfZVZd5An86/SIhL7B0El6y4BIRWzKkSEhINKYIkAiVULFkINayOFtFgkoNoqWsCIhioTgx\nWDBEV9cYWClfAhMqSzmoedFZBaakrVBYlVVIKTVRk5CuQDKho90N6bN/ZJNN07e77z2/vn373Hw+\nVf0H997n/H7n9P3yNA+3+1x44bC8ZsaMGWXvafLkybFz585+E+9K5d3TcJ4L9UFeBzZceY2IWL9+\nfaxevTqWLFkSa9eu7ferTIcPH46777471q9f3+fxp556KpqammL27NkljyvTJy7ZHdhwZbecXFar\nHw/3NWH0keGBDWf/jYjYsWNHdHV19buxS0T1+q8M1xd5Hdhw53Uw1Ty3EyXTjffcc889td5EkUyY\nMCGef/75mDBhQrzzne+MiIjXXnstxo4dW/L1e/fujT/84Q/R2toajY2NQx5/165dsXHjxrjmmmv6\nTI9LHefMM8+Mb3/727Fjx44YN25cbNy4MdatWxe33nprzJkzZ1hfU865/PWvf43Ozs645JJLBj3H\ncq5JOXvKe03Kud7UB3mtfl737dsXN910U5x55pnxqU99Kl599dXYu3fvsa+WlpaYMmVK7N69O9av\nXx8NDQ3x5ptvxpNPPhlr1qyJG264Ia6++uqk61bO94Rikd3qZ3fChAmD5nLp0qVl77ua141ikuGR\n+Xk5IuLZZ5+Nn/3sZ3HbbbfFySef3Oe5oXKe2n9luD7I68jldahrUs1zOyEynVGx/fv3Z3feeWe2\nZs2a7IEHHsjWr18/4GvXrFmTtbW1Zbt27Srr2M8991zW1taWtbe3l3WcLVu2ZMuWLcsuvPDCbNGi\nRdkjjzzS75jD9ZqhzuXnP/959stf/nLIcyz3mgy1p5RrctRA15v6Ia/VzeuGDRuytra2Ab82btyY\nZVmW9fT0ZN/85jezq666KpsxY0b2/ve/P/vOd75T1loyfWKS3er32nJyWc6+q33dKCYZHpmfl9eu\nXZu1tbVl3d3dJZ+vdv+V4fogryOT16MG+3m1mudW75luyLIsq/WAjeL48Ic/HA8++GC0trb2e+4L\nX/hC3HrrrSWfA0aevEIxyS4UmwxDcchr/fA3tyhbe3t7/OUvfxkw3Lt37xZ8GCXkFYpJdqHYZBiK\nQ17ri+EWZTv77LPj4YcfLvlcT09PXHDBBSO8I2Ag8grFJLtQbDIMxSGv9cWvJQIAAABQWD65BQAA\nAEBhGW4BAAAAUFhNtd7A8Qb6fddyjRmTf1aX+ofi9u/fn7u2u7s7ae2U8x4/fnzS2pMmTcpd29vb\nm7R2R0dH7trGxsaktTs7O5PqU6xcubJmaw8lJcMp7+OItAyn5DciLcOp552S4ZT8RqRlOCW/EWkZ\nlt/S9OB89ODK6cHVoQdXTg/ORw8efnpwPnpw5eq5B/vkFgAAAACFZbgFAAAAQGEZbgEAAABQWIZb\nAAAAABSW4RYAAAAAhWW4BQAAAEBhNdV6A8dLvR1vT09P7toDBw7UbO3m5uaktVN0dXUl1afc+rWp\nqXZvv5aWlqT6qVOnDtNO6ktKhlMyFJGW4dS1i5rh1NuvFzXD8luaHjzy9OB8ZLg0PXjk6cGVk9/S\n9OCRpwfnM5oz7JNbAAAAABSW4RYAAAAAhWW4BQAAAEBhGW4BAAAAUFiGWwAAAAAUluEWAAAAAIVV\nu3tQltDa2ppUn3Ib0y1btiStfeWVV+aunT9/ftLa+/bty127devWpLU3bdqUuzb1+33ZZZflrk29\n9evixYuT6utVyvc09TbEKRlOyW9EWoZT8huRluGU/Eakfb9T8huRlmH5LU0PzkcPrpweXB16cOX0\n4Hz04OGnB+ejB1eunnuwT24BAAAAUFiGWwAAAAAUluEWAAAAAIVluAUAAABAYRluAQAAAFBYhlsA\nAAAAFJbhFgAAAACF1ZBlWVbrTRz16KOPJtX39PTkrh07dmzS2q+//nru2tRvQVNTU+7a5ubmpLUn\nTpxYs7UPHTqUu7alpSVp7ZNOOimpPsWSJUtqtvZQUjKckt+ItAyn5DciLcMp+Y1Iy1FKflPXTslv\nRFqG5bc0PTgfPbhyenB16MGV04Pz0YOHnx6cjx5cuXruwT65BQAAAEBhGW4BAAAAUFiGWwAAAAAU\nluEWAAAAAIVluAUAAABAYRluAQAAAFBYhlsAAAAAFFZTrTdwvO7u7qT65ubm3LXz589PWvsnP/lJ\n7toNGzYkrT19+vTctddee23S2rNmzcpd29vbm7T2Cy+8kLu2sbExae2HHnooqT7FkiVLarb2UFIy\nnJLfiLQMp+Q3Ii3DKfmNSMtwSn4j0jKckt+ItAzLb2l6cD56cOX04OrQgyunB+ejBw8/PTgfPbhy\n9dyDfXILAAAAgMIy3AIAAACgsAy3AAAAACgswy0AAAAACstwCwAAAIDCMtwCAAAAoLCaar2B440Z\nU7tZ2759+5Lqm5ryX8rU2xBPmjQpd+3BgweT1u7o6Mhde/jw4aS1Ozs7c9emvtfOPffcpPp6VdQM\np+Q3Ii3DKfmNSMtwSn4j0jKckt+ItPea/JZW1PxG6MF56MH1p6gZ1oPz0YPrS1HzG6EH56EHV4dP\nbgEAAABQWIZbAAAAABSW4RYAAAAAhWW4BQAAAEBhGW4BAAAAUFiGWwAAAAAUluEWAAAAAIXVkGVZ\nVutNHPXYY48l1Xd1deWu3bNnT9LaZ5xxRu7aadOmJa198ODB3LU7d+5MWnvfvn25a1taWpLWnjJl\nSu7a5ubmpLUvuuiipPoUM2bMqNnaQ0nJcEp+I9IynJLfiLQMp+Q3Ii3DKfmNSMtwSn4j0jIsv6Xp\nwfnowZXTg6tDD66cHpyPHjz89OB89ODK1XMP9sktAAAAAArLcAsAAACAwjLcAgAAAKCwDLcAAAAA\nKCzDLQAAAAAKy3ALAAAAgMIy3AIAAACgsJpqvYHjTZo0Kal+//79uWs3bdqUtPbHP/7x3LWzZs1K\nWrujoyN37bZt25LWvu+++3LXTp48OWntT3/607lrW1paktaeMWNGUn29SslwSn4j0jKckt+ItAyn\n5DciLcMp+Y1Iy3BKfiPSMiy/penB+ejBldODq0MPrpwenI8ePPz04Hz04MrVcw/2yS0AAAAACstw\nCwAAAIDCMtwCAAAAoLAMtwAAAAAoLMMtAAAAAArLcAsAAACAwjLcAgAAAKCwmmq9geP19vYm1Tc1\n5T+d1tbWpLWbm5tz16ae9+HDh3PXtrS0JK09efLk3LXjxo1LWjvLsty1XV1dSWun1qc46aSTarb2\nUFLeyyn5jUjLcEp+I9LOOyW/EWkZTslvRFqGU/IbkZZB+S1ND85HD66cHlwdenDl9OB89ODhpwfn\nowdXrp57sE9uAQAAAFBYhlsAAAAAFJbhFgAAAACFZbgFAAAAQGEZbgEAAABQWIZbAAAAABRWQ5Z6\nL9hh9P3vf79max84cCCpPuUWqmeddVbS2p2dnblr9+zZk7T2wYMHc9emvvXGjx+fu7ahoSFp7fPP\nPz+pPsWll15as7WHUtQMp94COSXDKfmNSMtwSn4j0jKckt+ItAzLb2lFzW+EHpyHHpyPDJemB1dO\nDx558luaHpyPHjzyhsqwT24BAAAAUFiGWwAAAAAUluEWAAAAAIVluAUAAABAYRluAQAAAFBYhlsA\nAAAAFJbhFgAAAACF1VTrDRyvsbExqb6lpSV3bVdXV83WTj3vMWPyzyibm5uT1q7lNW9oaMhd29nZ\nmbT2wYMHk+rrVcp7OeW9FJH2fkpdO+W8U/IbkZbhWl7zlPxGpGVYfkvTg/PRgyunB1eHHlw5PTgf\nPXj46cH56MGVq+ce7JNbAAAAABSW4RYAAAAAhWW4BQAAAEBhGW4BAAAAUFiGWwAAAAAUluEWAAAA\nAIVluAUAAABAYTXVegPH6+zsTKqfOnVq7trFixcnrf3000/nrn3ooYeS1j733HNz115//fVJa8+Y\nMSN3bVdXV9Lazz33XO7agwcPJq29bNmypPoUWZbVbO2hpGQ4Jb8RaRlOyW9EWoZT8huRluGU/Eak\nZTglvxFpGZbf0vTgfPTgyunB1aEHV04PzkcPHn56cD56cOXquQf75BYAAAAAhWW4BQAAAEBhGW4B\nAAAAUFiGWwAAAAAUluEWAAAAAIVluAUAAABAYRluAQAAAFBYhlsAAAAAFJbhFgAAAACFZbgFAAAA\nQGEZbgEAAABQWIZbAAAAABSW4RYAAAAAhWW4BQAAAEBhNWRZltV6EwAAAACQh09ujQJZlsUPf/jD\nWLZsWcycOTMWLVoUX/nKV6Kzs7PsY7zyyisxZ86c+PWvf93n8Z6enpg+fXq0tbX1+Zo1a9aAx7rl\nllvi8ssvz30+w+WZZ56Ja6+9Ni666KK44oor4uGHHx72uoGuGwxEXvsr55rkObejZJrhILv9lbvv\navbjvMfmxCPDfbW3t/fb7/Ff3/jGNyKi+v1XhilFXgc32M+r1cxdvWe6qdYbIGLt2rXxj//4j7Fy\n5cqYN29e/Nu//Vs8+OCD8fvf/z7WrVs3ZP2ePXtixYoVJf9lsWPHjujt7Y2vfe1rcfbZZx97fMyY\n0nPNJ598MrZu3RpTp07Nf0LD4IUXXoiPfexjcfXVV8cnP/nJ2LZtW6xevToOHz4cN91007DUDXbd\nYCDy2l8516TScztKphkusttfOfuuZj/Oe2xOTDLc1/Tp0+Pxxx/v9/gDDzwQ27dvj6uvvjoiqtt/\nZZiByOvABju3aubuhMh0Rk319vZmc+bMye69994+jz/11FNZW1tbtn379kFrn3jiiWzu3LnZ3Llz\ns7a2tqy9vb3Pax5//PFs+vTpWU9Pz5B72bt3b3bJJZdkCxcuzC6//PJ8JzRMbrzxxmz58uV9Hlu9\nenV28cUXZ93d3Ul15Vw3KEVe+yv3mlRybseTaYaD7JZWzr6r2Y/zHpsTjwyXZ+vWrdm73vWubPPm\nzcceq2b/lWFKkdfSyjm3aubuRMi0X0ussq9//evx7ne/O3bv3l3y+c7OzvjABz4QS5cu7fP4tGnT\nIsuy+OMf/zjgsV966aW455574oMf/GCsWrUqshJ/Pu3FF1+MadOmRXNz85B7/dznPhfve9/7Yt68\neUO+NsVQ16Snpyfa29tj0aJFfR6/8soro7OzM7Zt25ZUV85148Qkr/0N1zWp5NyOkmnKJbv9DXVN\nIobedzX7cd5jU59kuL9yMny87u7u+PKXvxyXXXZZLF68+Njj1eq/Mnziktf+ysnrUOdWzdydKJk2\n3MrplVdeiTvuuCM+8YlPxO233x67du0q+brrrrsuHnvssZg8eXLJ5ydMmBB33XVXzJw5s8/jW7du\njYaGhnjnO9854B7OOuus2LJlS9x+++0xduzYaGho6Pea3/3ud9HY2BgrVqyImTNnxty5c+Pzn/98\nHDp0qM/r/umf/il++9vfxt133z3UqR+za9euWLlyZcycOTNWrlzZ56OVa9euHfA/MIe6Jn/605/i\njTfeiHPPPbfP429/+9sjIuLll19OqjvzzDOHvG7UF3mtXl6Huibnn39+Red2PJlGdquX3XL2Xc1+\nnPfYFIsMVzfDx/vud78bHR0dceedd+Y6t+PJ8IlJXqub16HOrZq5O1Ey7W9u5bBz58644YYbYtWq\nVfHe97530Neefvrpcfrpp1d0/N/85jexdu3auPzyy+O8884b8HUTJ06MiRMnDnqsl156KSIili9f\nHjfffHNs37491qxZEy+//HJ873vfi4gjIf6Hf/iHWLVqVZx66qll7bGzszM++tGPxoIFC2Lx4sXx\n7LPPxhe/+MVYvXp1vP7663HyyScP+B+YQ12To/8SGTduXJ/Hj/7zQM243LpTTjklTjnllKFOkToh\nr9XNaynHX5N3vOMdZZ9bqX1HyPSJSnarn92h9l3Nfpz32BSHDI9c/33jjTfi0UcfjaVLl/b5G0Tl\nnlupfUfI8IlEXquf16HOrZq5O1EybbiVw9133x0XX3zxkMHPY9u2bXHzzTfHOeecE/fdd1/SsbIs\ni29961tx2mmnHfsPzNmzZ0dra2v8t//23+Lpp5+OBQsWxF133RULFy7s9xHEwfzgBz+I+++/P9ra\n2iIi4kMf+lDcdtttcejQofjRj34Uy5Yty73v3t7eQZ8f6F8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E3/3d38XMmTNj/vz58dnP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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110631358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(10, 5,figsize=(15, 15))\n",
    "\n",
    "for i, row in enumerate(axs):\n",
    "    for j, ax in enumerate(row):\n",
    "        ax.imshow(gw[i,j],interpolation='nearest', aspect='auto')\n",
    "        ax.set_axis_off()\n",
    "        title='$\\epsilon$ : '+str(\"%.3f\" % epsilons[i])+','+'$\\gamma$ : '+str(\"%.2f\" % alphas[j])\n",
    "        ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, -1, -1, 1)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0],y[1],y[2],y[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cyZDnqNeO/UrvG6MYmrg2atQIFxcX9u3bh4+PZa2MiIgImjRpkmm/5s2b8/3332fa9vjj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1167a2eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[0].display_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jj2X6a2Y2nWu2Y9XE1c3NjX/++YfY2FgcHIwMOzQ0lPz586e4WkB80hqvfPny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\nKiIiIiJOQcFVRERERJzC/wPCMk9UhI8Y5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117eda160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[1].display_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classe : -1 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAACVp0kGAAAA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\nf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1130c63c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1=2\n",
    "n2=3\n",
    "foo=dict()\n",
    "epsilons=list(np.logspace(-1,1,5))\n",
    "alphas=list(np.linspace(0.01,0.99,10))\n",
    "goo=np.zeros((len(epsilons),len(alphas)))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "for eps in epsilons:\n",
    "    for alpha in alphas:\n",
    "        wgw=NN.Graph_WGW_1NN_Classifier(epsilon=eps,alpha=alpha,method='shortest_path',features_metric='dirac',normalize_distance=True)\n",
    "        d=wgw.compute_similarity(X[n1],X[n2])\n",
    "        foo[i,j]=wgw.gw.transp\n",
    "        goo[i,j]=d\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "    j=0\n",
    "\n",
    "print('classe : '+str(y[n1])+' '+str(y[n2]))\n",
    "\n",
    "fig, axs = plt.subplots(5, 10,figsize=(15, 15))\n",
    "\n",
    "for i, row in enumerate(axs):\n",
    "    for j, ax in enumerate(row):\n",
    "        ax.imshow(foo[i,j],interpolation='nearest', aspect='auto')\n",
    "        ax.set_axis_off()\n",
    "        title='$\\epsilon$ : '+str(\"%.3f\" % epsilons[i])+','+'$\\\\alpha$ : '+str(\"%.2f\" % alphas[j])\n",
    "        ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QRLEEAAAAQBHFEgAAAABFFEsAAAAAFFEsAQAA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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117f1df98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "ax = fig.add_subplot(111)\n",
    "im=ax.imshow(goo,interpolation='nearest',aspect='auto')\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Toytrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os,sys\n",
    "module_path = os.path.abspath(os.path.join('/Users/vayer/Documents/OT/Python/GW_tests/'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "from graph import *\n",
    "import copy\n",
    "import NN,time\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import ot_distances\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from ot_distances import Wasserstein_distance as EMD\n",
    "from ot_distances import Gromov_Wasserstein_distance as WGW\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nTree=100\n",
    "depth=3\n",
    "c=20\n",
    "d=30\n",
    "dataset=build_binary_uniform_dataset(nTree1=nTree,nTree2=nTree,maxdepth=depth,c=c,d=d)\n",
    "X,y=zip(*dataset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, 0, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0],y[101],y[1],y[102]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GOOOOOOOOO\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wgw=NN.Tree_WGW_1NN_Classifier(epsilon=10000,alpha=0.5,method='weighted_shortest_path',normalize_distance=True)\n",
    "wgw.compute_similarity(X[0],X[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "classe : 0 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/colors.py:943: UserWarning: Warning: converting a masked element to nan.\n",
      "  vmin = float(vmin)\n",
      "//anaconda/lib/python3.5/site-packages/matplotlib/colors.py:944: UserWarning: Warning: converting a masked element to nan.\n",
      "  vmax = float(vmax)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11851c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1=0\n",
    "n2=101\n",
    "foo=dict()\n",
    "epsilons=list(np.logspace(-3,2,5))\n",
    "alphas=list(np.linspace(0.01,0.99,10))\n",
    "goo=np.zeros((len(epsilons),len(alphas)))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "for eps in epsilons:\n",
    "    for alpha in alphas:\n",
    "        wgw=NN.Tree_WGW_1NN_Classifier(epsilon=eps,alpha=alpha,method='shortest_path',normalize_distance=True)\n",
    "        d=wgw.compute_similarity(X[n1],X[n2])\n",
    "        foo[i,j]=wgw.gw.transp\n",
    "        goo[i,j]=d\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "    j=0\n",
    "\n",
    "print('classe : '+str(y[n1])+' '+str(y[n2]))\n",
    "\n",
    "fig, axs = plt.subplots(5, 10,figsize=(15, 15))\n",
    "\n",
    "for i, row in enumerate(axs):\n",
    "    for j, ax in enumerate(row):\n",
    "        ax.imshow(foo[i,j],interpolation='nearest', aspect='auto')\n",
    "        ax.set_axis_off()\n",
    "        title='$\\epsilon$ : '+str(\"%.3f\" % epsilons[i])+','+'$\\\\alpha$ : '+str(\"%.2f\" % alphas[j])\n",
    "        ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAEamAQdLixYtikWLFg1nLQAAAABUkHy5CwAAAACgMhX9qXAAAAAAI5EZS6Wn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\nlu7u7nKXwBlZeC9yuVyMHj0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tXHHFFXHNNdeMVT7GSDn8q065KJd/0bEUjtFW\nDs9GlmWDS+GKfG8VOXs5Kvr1yLIsqquro6enp/DnUg7fp8pF0e+liHdmLNXW1kapVCr8vVX0/OWk\nrq4u7wjjrru7O+8IyYq6x9qIiyXKg8s+cZTLD17FEqOtHJ4NxRJjoejXQ7HEWCj6vRShWGJsKJaK\npajFks+xBAAAACCJYgkAAACAJIolAAAAAJJU5h0AAAAAYDRkWZZ3hEnHjCUAAAAAkiiWAAAAAEii\nWAIAAAAgiWIJAAAAgCSKJQAAAACSKJYAAAAASFKZdwAAAACA0ZBlWd4RJh0zlgAAAABIolgCAAAA\nIIliCQAAAIAkiiUAAAAAkiiWAAAAAEiiWAIAAAAgSWXeAQAAAABGQ5ZleUeYdMxYAgAAACCJYgkA\nAACAJIolAAAAAJIolgAAAABIolgCAAAAIIliCQAAAIAklXkHAAAAABgNWZblHWHSMWMJAAAAgCSK\nJQAAAACSKJYAAAAASKJYAgAAACCJYgkAAACAJIolAAAAAJIolgAAAABIUpl3AAAAAIDRkGVZ3hEm\nHTOWAAAAAEiiWAIAAAAgiWIJAAAAgCSKJQAAAACSKJYAAAAASKJYAgAAACBJZd4BAAAAAEZDlmV5\nR5h0zFgCAAAAIIliCQAAAIAkiiUAAAAAkiiWAAAAAEiiWAIAAAAgiWIJAAAAgCSVeQcAAAAAGA1Z\nluUdYdIxYwkAAACgQHp7e2PlypUxb968mD9/fmzcuPF/Hbtv375YvHhxNDU1xeWXXx7PPffckPcf\nfvjhuOiii6KpqSn+7M/+LA4dOjSiLIolAAAAgAJZv3597Nu3Lx544IFobW2Nu+66Kx5//PETxpVK\npVi2bFnMmzcvtmzZEk1NTXH11VdHd3d3RES0t7fHqlWr4s///M/joYceiiNHjsSKFStGlEWxBAAA\nAFAQpVIpNm/eHKtWrYqGhoZYuHBhtLS0xKZNm04Yu23btqitrY3ly5fHmWeeGTfeeGNMmzYtHn30\n0YiI+Ju/+Zu45JJL4nd+53fiIx/5SGzYsCGefPLJePXVV4edR7EEAAAAUBD79++Pvr6+aGpqGjzW\n3Nwc7e3tJ4xtb2+P5ubmIcfOOeec2L17d0RE7NmzJ+bNmzf43imnnBKnnnpqPPvss8POo1gCAAAA\nKIjOzs6YMWNGVFb+v89jq6+vj56enhP2Rzp48GDMnDlzyLH6+vp47bXXBn+vX3z/137t1+LAgQPD\nzqNYAgAAACiIUqkUVVVVQ44df93b2zvkeHd397uOPT7ul70/HIolAAAAgIKorq4+ofg5/rq2tnZY\nY2tqaob1/nBU/vIhAAAAABNflmV5Rxhzs2bNisOHD0d/f39MmfLOfKGurq6oqamJ6dOnnzC2s7Nz\nyLGurq44+eSTIyJi5syZ0dXVdcL7v7g87v9ixhIAAABAQcyZMycqKytjz549g8d27NgRjY2NJ4yd\nO3fu4Ebdx+3atSvOPvvsiIhoamqKnTt3Dr733//933HgwIGYO3fusPMolgAAAAAKoqamJhYtWhSt\nra2xd+/e2L59e2zcuDGWLFkSEe/MOOrp6YmIiIsvvjiOHj0aa9eujY6Ojmhra4tSqRSf//znIyLi\nD//wD+Mf/uEfYvPmzbF///64/vrr47d+67di9uzZw86TDQwMDIz+aTLRuewTR39/f94RRkVFRUX0\n9fXlHeM982xMHOXwbGRZFlOnTo1jx44V+t4qcvZyVPTrkWVZVFdXR09PT+HPpRy+T5WLot9LERFT\npkyJ2traKJVKhb+3ip6/nNTV1eUdgTHS3d0dq1evjsceeyzq6uqipaUlrrzyyoiIaGhoiHXr1sWl\nl14aERF79+6N1tbWePHFF+OjH/1orF69OhoaGgZ/r61bt8a3v/3tOHLkSJx//vmxZs2a+JVf+ZVh\nZ1EsTVIu+8RRLj94FUuMtnJ4NhRLjIWiXw/FEmOh6PdShGKJsaFYYjxYCgcAAABAEjOWAAAAAEhi\nxhIAAAAASRRLAAAAACRRLAEAAACQRLEEAAAAQBLFEgAAAABJFEsAAAAAJFEsAQAAAJBEsQQAAABA\nEsUSAAAAAEkUSwAAAAAkUSyNQG9vb6xcuTLmzZsX8+fPj40bN+YdCXL32muvxbXXXhuf/vSnY8GC\nBbFu3bro7e3NOxZMGMuWLYsVK1bkHQMmhN7e3li9enV86lOfivPPPz/uuOOOvCNB7g4cOBB/8id/\nEs3NzXHhhRfGX//1X+cdCWBEKvMOUCTr16+Pffv2xQMPPBCvvPJKXH/99TF79uz43Oc+l3c0yM21\n114bM2bMiB/84Adx+PDhWLlyZVRUVMTy5cvzjga527ZtWzz11FPxu7/7u3lHgQmhra0tfvzjH8f3\nv//9ePPNN+NrX/tazJ49OxYvXpx3NMjNddddFx/4wAfi7//+7+OFF16Iv/iLv4jZs2fHwoUL844G\nMCxmLA1TqVSKzZs3x6pVq6KhoSEWLlwYLS0tsWnTpryjQW5efPHFaG9vj1tvvTXOOuusaG5ujmuv\nvTYefvjhvKNB7o4cORIbNmyIT3ziE3lHgQnhyJEjsWXLlmhra4vGxsb4zGc+E0uXLo1nn30272iQ\nmzfeeCOeffbZuOaaa+KDH/xgXHjhhTF//vz4t3/7t7yjAQybYmmY9u/fH319fdHU1DR4rLm5Odrb\n23NMBfk6+eST43vf+16cdNJJg8cGBgbi6NGjOaaCiWH9+vWxaNGiOOuss/KOAhPCzp07o66uLj75\nyU8OHrvqqqviL//yL3NMBfmqqamJ2tra+NGPfhRvv/12vPjii7Fr16742Mc+lnc0gGFTLA1TZ2dn\nzJgxIyor/9/qwfr6+ujp6YlDhw7lmAzyU1dXF+edd97g64GBgdi0aVOce+65OaaC/D3zzDOxc+fO\n+OpXv5p3FJgwfvrTn8bs2bNj69atcckll8TChQvj7rvvjoGBgbyjQW6qqqri5ptvjr/927+NuXPn\nxm//9m/HZz/72fi93/u9vKMBDJs9loapVCpFVVXVkGPHX9uoGN5x2223xf79++NHP/pR3lEgN729\nvXHLLbdEa2vrCT83YDJ766234qWXXoqHHnoo1q1bF52dnXHTTTfF+973vvjSl76UdzzITUdHR1xw\nwQXxla98JZ5//vlYs2ZNnHvuufGFL3wh72gAw6JYGqbq6uoTCqTjr2tra/OIBBPKhg0b4oEHHohv\nfetblv4wqd15553R2Nho5h78goqKivjZz34Wt99+e5xyyikREfHqq6/Ggw8+qFhi0nrmmWdi8+bN\n8dRTT0VVVVV87GMfiwMHDsQ999yjWAIKQ7E0TLNmzYrDhw9Hf39/TJnyzgrCrq6uqKmpienTp+ec\nDvK1Zs2a+OEPfxgbNmzwCSZMev/4j/8Yr7/+epx99tkREXHs2LGIiHjsscdi165deUaDXM2cOTOq\nq6sHS6WIiDPOOCMOHDiQYyrI13PPPRcf+tCHhsxwnTNnTtx77705pgIYGcXSMM2ZMycqKytjz549\ncc4550RExI4dO6KxsTHnZJCvu+66K374wx/GHXfcERdddFHecSB3mzZtirfffnvw9YYNGyIiYvny\n5XlFgglh7ty50dPTEy+//HKcfvrpEfHOEqDZs2fnnAzyM3PmzHj55Zfj7bffHtzL9cUXX4wPfOAD\nOScDGD6bdw9TTU1NLFq0KFpbW2Pv3r2xffv22LhxYyxZsiTvaJCbjo6OuOeee2LZsmVx9tlnR1dX\n1+AXTFannnpqnHbaaYNf06ZNi2nTpsVpp52WdzTI1RlnnBELFiyIG264Ifbv3x9PP/103HffffHF\nL34x72iQmwsuuCAqKytj1apV8dJLL8U//dM/xb333ht//Md/nHc0gGHLBnwUx7B1d3fH6tWr47HH\nHou6urpoaWmJK6+8Mu9YkJvvfve7cccddww5NjAwEFmWxX/8x3/klAomlhUrVkRExK233ppzEsjf\nm2++GW1tbfHEE09EbW1tXHHFFXHNNdfkHQty1dHREWvXro329vY46aST4o/+6I/8NwZQKIolAAAA\nAJJYCgcAAABAEsUSAAAAAEkUSwAAAAAkUSwBAAAAkESxBAAAAEASxRIAAAAASRRLAAAAACRRLAEA\nAACQRLEEAAAAQBLFEgAAAABJFEsAAAAAJPn/AH7Ms+as+YE1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1213900f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "ax = fig.add_subplot(111)\n",
    "im=ax.imshow(goo,interpolation='nearest',aspect='auto')\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "classe : 0 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/colors.py:943: UserWarning: Warning: converting a masked element to nan.\n",
      "  vmin = float(vmin)\n",
      "//anaconda/lib/python3.5/site-packages/matplotlib/colors.py:944: UserWarning: Warning: converting a masked element to nan.\n",
      "  vmax = float(vmax)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c42f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1=0\n",
    "n2=101\n",
    "foo=dict()\n",
    "epsilons=list(np.logspace(-3,2,5))\n",
    "alphas=list(np.linspace(0.01,0.99,10))\n",
    "goo=np.zeros((len(epsilons),len(alphas)))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "for eps in epsilons:\n",
    "    for alpha in alphas:\n",
    "        wgw=NN.Tree_WGW_1NN_Classifier(epsilon=eps,alpha=alpha,method='weighted_shortest_path',normalize_distance=True)\n",
    "        d=wgw.compute_similarity(X[n1],X[n2])\n",
    "        foo[i,j]=wgw.gw.transp\n",
    "        goo[i,j]=d\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "    j=0\n",
    "\n",
    "print('classe : '+str(y[n1])+' '+str(y[n2]))\n",
    "\n",
    "fig, axs = plt.subplots(5, 10,figsize=(15, 15))\n",
    "\n",
    "for i, row in enumerate(axs):\n",
    "    for j, ax in enumerate(row):\n",
    "        ax.imshow(foo[i,j],interpolation='nearest', aspect='auto')\n",
    "        ax.set_yticks([])\n",
    "        ax.set_xticks([])\n",
    "        title='$\\epsilon$ : '+str(\"%.3f\" % epsilons[i])+','+'$\\\\alpha$ : '+str(\"%.2f\" % alphas[j])\n",
    "        ax.set_title(title)\n",
    "        ax.set_ylabel(str(\"%.3f\" %goo[i,j]),fontsize=8)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xBQAAAEASiikAAAAAklBMAQAAAJCEYgoAAACA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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122cb8898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "ax = fig.add_subplot(111)\n",
    "im=ax.imshow(goo,interpolation='nearest',aspect='auto')\n",
    "fig.colorbar(im)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NCI1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='./data/NCI1/'\n",
    "dataset=list(build_NCI1_dataset(path))\n",
    "X,y=zip(*dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CjRo1rlZba9as5OjRH3n33eUEBbUjNTWVTaWkKTQNak/XsMEqeSV2o1QBcQi6teRaNN6u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\niIhb+P8RTpVRuQNlfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11050feb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[0].display_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7q45sdstXSnkGSy/7Vq2e4oEa97B0xzbGkrte9gAXkPz6yuZ5DmzfyvT33nbx2uee/cW/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\nJa4iIiIiYgtKXEVERETEFv4/U3iiIRwDM0kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111ca77f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[10].display_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B/YwFWpK0Wf8GlQBDwUdtVPaxDXA+aAZR8NIxbIZq+bWGl49hUVQQWxE1nobX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\nX5uuEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116796ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X[2200].display_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1, 0, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0],y[2200],y[10],y[2201]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "Warning: numerical errors at iteration 0\n",
      "classe : 0 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/colors.py:943: UserWarning: Warning: converting a masked element to nan.\n",
      "  vmin = float(vmin)\n",
      "//anaconda/lib/python3.5/site-packages/matplotlib/colors.py:944: UserWarning: Warning: converting a masked element to nan.\n",
      "  vmax = float(vmax)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "First argument must be a path or file object reading bytes",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    228\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2230\u001b[0m                 \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2231\u001b[0m                 \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2232\u001b[0;31m                 **kwargs)\n\u001b[0m\u001b[1;32m   2233\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2234\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 527\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    528\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    529\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    472\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    473\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 474\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    475\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    476\u001b[0m             \u001b[0mRendererAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelease\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1157\u001b[0m         \u001b[0mdsu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mitemgetter\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[0m\n\u001b[1;32m   1158\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1159\u001b[0;31m             \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2318\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdsu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2319\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2321\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mbefore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m         \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0mafter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    747\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    748\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 749\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    750\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    359\u001b[0m                 w, h, d = renderer.get_text_width_height_descent(clean_line,\n\u001b[1;32m    360\u001b[0m                                                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontproperties\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 361\u001b[0;31m                                                         ismath=ismath)\n\u001b[0m\u001b[1;32m    362\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m                 \u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfonts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mused_characters\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 234\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmathtext_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    235\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m   3111\u001b[0m             \u001b[0mfontset_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_font_type_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\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   3112\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfontset_class\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3113\u001b[0;31m                 \u001b[0mfont_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfontset_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3114\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3115\u001b[0m                 raise ValueError(\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 680\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stix_fallback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStixFonts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    682\u001b[0m         \u001b[0mTruetypeFonts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m         \u001b[0mTruetypeFonts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfontmap\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    916\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fontmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python3.5/site-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, default_font_prop, mathtext_backend)\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfindfont\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_font_prop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m         \u001b[0mdefault_font\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCachedFont\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFT2Font\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\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    567\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fonts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'default'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_font\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fonts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'regular'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_font\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: First argument must be a path or file object reading bytes"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121387198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1=0\n",
    "n2=2200\n",
    "foo=dict()\n",
    "epsilons=list(np.logspace(-2,1,5))\n",
    "alphas=list(np.linspace(0.01,0.99,10))\n",
    "goo=np.zeros((len(epsilons),len(alphas)))\n",
    "\n",
    "i=0\n",
    "j=0\n",
    "for eps in epsilons:\n",
    "    for alpha in alphas:\n",
    "        wgw=NN.Graph_WGW_1NN_Classifier(epsilon=eps,alpha=alpha,method='shortest_path',features_metric='dirac',normalize_distance=True)\n",
    "        d=wgw.compute_similarity(X[n1],X[n2])\n",
    "        foo[i,j]=wgw.gw.transp\n",
    "        goo[i,j]=d\n",
    "        j=j+1\n",
    "    i=i+1\n",
    "    j=0\n",
    "\n",
    "print('classe : '+str(y[n1])+' '+str(y[n2]))\n",
    "\n",
    "fig, axs = plt.subplots(5, 10,figsize=(15, 15))\n",
    "\n",
    "for i, row in enumerate(axs):\n",
    "    for j, ax in enumerate(row):\n",
    "        ax.imshow(foo[i,j],interpolation='nearest', aspect='auto')\n",
    "        ax.set_yticks([])\n",
    "        ax.set_xticks([])\n",
    "        title='$\\epsilon$ : '+str(\"%.3f\" % epsilons[i])+','+'$\\\\alpha$ : '+str(\"%.2f\" % alphas[j])\n",
    "        ax.set_title(title)\n",
    "        ax.set_ylabel(str(\"%.3f\" %goo[i,j]),fontsize=8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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