graph_format.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph format\n",
    "\n",
    "The EDeN library allows the vectorization of graphs, i.e. the transformation of graphs into sparse vectors.\n",
    "\n",
    "The graphs that can be processed by the EDeN library have the following restrictions:\n",
    "- the graphs are implemented as *networkx* graphs\n",
    "- nodes and edges have identifiers: the following identifiers are used as reserved words\n",
    "    1. label\n",
    "    2. weight\n",
    "    3. vec or svec\n",
    "    4. nesting\n",
    "    \n",
    "- nodes and edges **must** have the 'label' attribute \n",
    "- the 'label' attribute can be of one of the following python types: \n",
    "    1. string \n",
    "    2. list \n",
    "    3. dictionary\n",
    "    \n",
    " strings are used to represent categorical values; \n",
    " \n",
    " lists are used to represent dense vectors;\n",
    " \n",
    " dictionaries are used to represent sparse vectors: keys are of string type and values are of type float;\n",
    " \n",
    " \n",
    "- nodes and edges **can** have a 'weight' attribute of type float \n",
    "- nodes **can** have a 'vec' attribute of type list of reals \n",
    "- nodes **can** have a 'svec' attribute of type dictionary of reals \n",
    "- nesting edges **must** have a 'nesting' attribute of type boolean set to True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from eden.util import configure_logging\n",
    "import logging\n",
    "logger = logging.getLogger()\n",
    "configure_logging(logger,verbosity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pylab as plt\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A')\n",
    "G.add_node(1, label='B')\n",
    "G.add_node(2, label='C')\n",
    "\n",
    "G.add_edge(0,1, label='x')\n",
    "G.add_edge(1,2, label='y')\n",
    "G.add_edge(2,0, label='z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"directed\":false,\n",
      "    \"graph\":[],\n",
      "    \"nodes\":[\n",
      "        {\n",
      "            \"id\":0,\n",
      "            \"label\":\"A\"\n",
      "        },\n",
      "        {\n",
      "            \"id\":1,\n",
      "            \"label\":\"B\"\n",
      "        },\n",
      "        {\n",
      "            \"id\":2,\n",
      "            \"label\":\"C\"\n",
      "        }\n",
      "    ],\n",
      "    \"links\":[\n",
      "        {\n",
      "            \"source\":0,\n",
      "            \"target\":1,\n",
      "            \"label\":\"x\"\n",
      "        },\n",
      "        {\n",
      "            \"source\":0,\n",
      "            \"target\":2,\n",
      "            \"label\":\"z\"\n",
      "        },\n",
      "        {\n",
      "            \"source\":1,\n",
      "            \"target\":2,\n",
      "            \"label\":\"y\"\n",
      "        }\n",
      "    ],\n",
      "    \"multigraph\":false\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from eden.display import serialize_graph\n",
    "print serialize_graph(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Mr2ZblVQ6g5xUo+zSSZJUmhDCKOACYCrp9+fVMcbV2VYllYdBTqoDdukkSRqc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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f24c35e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from eden.display import draw_graph\n",
    "draw_graph(G,  size=15, vertex_size=1500, font_size=14, vertex_border=True, size_x_to_y_ratio=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', vec=[0,0,.1])\n",
    "G.add_node(1, label='B', vec=[0,.1,0])\n",
    "G.add_node(2, label='C', vec=[.1,0,0])\n",
    "\n",
    "G.add_edge(0,1, label='x')\n",
    "G.add_edge(1,2, label='y')\n",
    "G.add_edge(2,0, label='z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Prz8wmKNB/wA+pL7rnKvs+N+UiIiIiDQwsyzgauA0/FyFuc65jcFWJV2dglyS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f20677390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graph(G, secondary_vertex_label='vec', size=15, vertex_size=1500, font_size=14, vertex_border=True, size_x_to_y_ratio=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', svec={'A':1, 'B':2, 'C':3})\n",
    "G.add_node(1, label='B', svec={'A':1, 'B':2, 'D':3})\n",
    "G.add_node(2, label='C', svec={'A':1, 'D':2, 'E':3})\n",
    "\n",
    "G.add_edge(0,1, label='x')\n",
    "G.add_edge(1,2, label='y')\n",
    "G.add_edge(2,0, label='z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GDVtRUlKyE2DXrl1dV61a1TN2iqfMbI+Z7cMPPfk78EDcS1QDy/Edt/o8gF+r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\ncQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f202f3850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graph(G, secondary_vertex_label='svec',  size=15, vertex_size=1500, font_size=14, vertex_border=True, size_x_to_y_ratio=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', weight=.5)\n",
    "G.add_node(1, label='B', weight=1)\n",
    "G.add_node(3, label='D', weight=1)\n",
    "G.add_node(4, label='E', weight=1)\n",
    "G.add_node(5, label='F', weight=.01)\n",
    "\n",
    "G.add_edge(0,1, label='x', weight=.75)\n",
    "G.add_edge(1,3, label='z', nesting=True, weight=.5)\n",
    "G.add_edge(0,3, label='z', nesting=True, weight=.1)\n",
    "G.add_edge(3,4, label='k')\n",
    "G.add_edge(3,5, label='j')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EWqjGByfH3j81jyAYMmQI77//Pk1NTa1nyaXhqwF6haDkMZPI+9JyaSPz2EqY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\nRERERPLM/wcw7omTK261LwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f20151890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_graph(G, secondary_vertex_label='weight', secondary_edge_label='weight', size=15, vertex_size=1500, font_size=14, vertex_border=True, size_x_to_y_ratio=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build graphs and then display them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "graph_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A')\n",
    "G.add_node(1, label='B')\n",
    "G.add_node(2, label='C')\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A')\n",
    "G.add_node(1, label='B')\n",
    "G.add_node(2, label='X')\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A')\n",
    "G.add_node(1, label='B')\n",
    "G.add_node(2, label='X')\n",
    "G.add_edge(0,1, label='x')\n",
    "G.add_edge(1,2, label='x')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='X')\n",
    "G.add_node(1, label='X')\n",
    "G.add_node(2, label='X')\n",
    "G.add_edge(0,1, label='x')\n",
    "G.add_edge(1,2, label='x')\n",
    "graph_list += [G.copy()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', vec=[1,0,0])\n",
    "G.add_node(1, label='B', vec=[0,1,0])\n",
    "G.add_node(2, label='C', vec=[0,0,1])\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', vec=[1,1,0])\n",
    "G.add_node(1, label='B', vec=[0,1,1])\n",
    "G.add_node(2, label='C', vec=[0,0,1])\n",
    "G.add_edge(0,1, label='a', entity='CATEG_EDGE')\n",
    "G.add_edge(1,2, label='b', entity='CATEG_EDGE')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', vec=[1,0.1,0.2])\n",
    "G.add_node(1, label='B', vec=[0.3,1,0.4])\n",
    "G.add_node(2, label='C', vec=[0.5,0.6,1])\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', vec=[0.1,0.2,0.3])\n",
    "G.add_node(1, label='B', vec=[0.4,0.5,0.6])\n",
    "G.add_node(2, label='C', vec=[0.7,0.8,0.9])\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', svec={'A':1, 'B':1, 'C':1})\n",
    "G.add_node(1, label='B', svec={'a':1, 'B':1, 'C':1})\n",
    "G.add_node(2, label='C', svec={'a':1, 'b':1, 'C':1})\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', svec={'A':1,  'C':1, 'D':1})\n",
    "G.add_node(1, label='B', svec={'a':1,  'C':1, 'D':1})\n",
    "G.add_node(2, label='C', svec={'a':1,  'C':1, 'D':1})\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', svec={'A':1, 'D':1, 'E':1})\n",
    "G.add_node(1, label='B', svec={'a':1, 'D':1, 'E':1})\n",
    "G.add_node(2, label='C', svec={'a':1, 'D':1, 'E':1})\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]\n",
    "\n",
    "G=nx.Graph()\n",
    "G.add_node(0, label='A', svec={'A':1, 'B':1, 'C':1, 'D':1, 'E':1})\n",
    "G.add_node(1, label='B', svec={'a':1, 'B':1, 'C':1, 'D':1, 'E':1})\n",
    "G.add_node(2, label='C', svec={'a':1, 'b':1, 'C':1, 'D':1, 'E':1})\n",
    "G.add_edge(0,1, label='a')\n",
    "G.add_edge(1,2, label='b')\n",
    "graph_list += [G.copy()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a vector representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instances: 12 \n",
      "Features: 65537 with an avg of 101 features per instance\n",
      "CPU times: user 100 ms, sys: 196 ms, total: 296 ms\n",
      "Wall time: 1.11 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from eden.graph import vectorize\n",
    "X = vectorize(graph_list, complexity=2, nbits=16, discrete=False)\n",
    "y=[1]*4+[2]*4+[3]*4\n",
    "print 'Instances: %d \\nFeatures: %d with an avg of %d features per instance' % (X.shape[0], X.shape[1],  X.getnnz()/X.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "embed the high dimensional vector in 2D using PCA and plot the instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f18189d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab as plt\n",
    "def plot(X,y):\n",
    "    size=8\n",
    "    cmap = 'rainbow'\n",
    "    plt.figure(figsize=(size, size))    \n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.axis('off')\n",
    "    plt.scatter(X[:, 0], X[:, 1], alpha=0.7, c=y, cmap=cmap, s=50, edgecolors='k')\n",
    "    for i in range(X.shape[0]):\n",
    "        plt.annotate(str(i), (X[i, 0], X[i, 1]), xytext=(-3, 8), textcoords='offset points')\n",
    "    plt.show()\n",
    "\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "Xd = TruncatedSVD(n_components=2).fit_transform(X)\n",
    "plot(Xd,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute pairwise similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "K=metrics.pairwise.pairwise_kernels(X, metric='linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named ipy_table",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-efe59951ba66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mipy_table\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprep_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mK\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mheader\u001b[0m \u001b[0;34m=\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      4\u001b[0m     \u001b[0mheader\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\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[0mK\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\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[1;32m      5\u001b[0m     \u001b[0mmat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mheader\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named ipy_table"
     ]
    }
   ],
   "source": [
    "from ipy_table import * \n",
    "def prep_table(K):    \n",
    "    header = [' ']\n",
    "    header += [i for i in range(K.shape[0])]\n",
    "    mat = [header]\n",
    "    for id, row in enumerate(K):\n",
    "        new_row = [id]\n",
    "        new_row += list(row)\n",
    "        mat.append(new_row)\n",
    "    return mat\n",
    "\n",
    "mat=prep_table(K)\n",
    "make_table(mat)\n",
    "apply_theme('basic')\n",
    "set_global_style(float_format = '%0.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J/9wl5/WqWyUd6vQSc+Drkr4fEX8j6W/VBedoe42kz0gaioj3SmpIurGzW1Vyn6Trp9y2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0f2f8bdb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pylab as plt\n",
    "plt.figure( figsize=(6,6) )\n",
    "img = plt.imshow( K, interpolation='none', cmap=plt.get_cmap( 'YlOrRd' ) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}