{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['dist', 'bar']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "import irm\n",
    "import seaborn as sns\n",
    "import sys\n",
    "sys.path.append(\"../code/\")\n",
    "import cvpipelineutil\n",
    "import sklearn.metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# synthetic data, no distance-dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "true_classes = ['gradstudent', 'postdoc', 'faculty', 'vendor']\n",
    "\n",
    "tries_talk_probs = {}\n",
    "MAXDIST = 100\n",
    "tries_talk_probs[('gradstudent', 'gradstudent')] =  (MAXDIST, 0.5)\n",
    "\n",
    "tries_talk_probs[('gradstudent', 'postdoc')] = (MAXDIST, 0.2) \n",
    "tries_talk_probs[('gradstudent', 'faculty')] = (MAXDIST, 0.1) \n",
    "tries_talk_probs[('gradstudent', 'vendor')] = (MAXDIST, 0.5)\n",
    "\n",
    "tries_talk_probs[('postdoc', 'postdoc')] = (MAXDIST, 0.7)\n",
    "tries_talk_probs[('postdoc', 'faculty')] = (MAXDIST, 0.7)\n",
    "\n",
    "tries_talk_probs[('faculty', 'faculty')] = (MAXDIST, 0.9)\n",
    "\n",
    "tries_talk_probs[('vendor', 'gradstudent')] =  (MAXDIST, 0.2)\n",
    "\n",
    "tries_talk_probs[('vendor', 'postdoc')] = (MAXDIST, 0.4) \n",
    "tries_talk_probs[('vendor', 'faculty')] = (MAXDIST, 0.7) \n",
    "tries_talk_probs[('vendor', 'vendor')] = (MAXDIST, 0.1)\n",
    "\n",
    "# turn into numeric types\n",
    "ttp_numeric = {}\n",
    "for k, v in tries_talk_probs.iteritems():\n",
    "    ttp_numeric[(true_classes.index(k[0]), true_classes.index(k[1]))] = v\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "nodes, conmat = irm.data.generate.c_class_neighbors(10, ttp_numeric, JITTER=0.1)\n",
    "np.fill_diagonal(conmat, 0) # don't try and talk to yourself\n",
    "#randomize the order \n",
    "ca = np.random.permutation(len(nodes))\n",
    "nodes = nodes[ca]\n",
    "conmat = conmat[ca]\n",
    "conmat = conmat[:, ca]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "AAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e9f0850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.imshow(conmat, interpolation='nearest')\n",
    "pylab.grid(False)\n",
    "pylab.ylabel('e_i')\n",
    "pylab.xlabel('e_j')\n",
    "pylab.title(\"e_i tries to talk to e_j\")\n",
    "pylab.xticks([])\n",
    "pylab.yticks([])\n",
    "pylab.savefig(\"nodist.data.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating threadpool with 8 cores\n",
      "FIXED_K= False\n",
      "Running with relation_class= <class 'irm.pyirmutil.ParRelation'>\n",
      "This model fixed_k=  False\n",
      "Annealing at temp= 32.0\n",
      "kernels: nonconj_gibbs 2.58 sec\n",
      "kernels: slice_params 0.07 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.02 sec\n",
      "kernels: anneal 2.70 sec\n",
      "Iter 1\n",
      "Annealing at temp= 21.7726400028\n",
      "kernels: nonconj_gibbs 1.62 sec\n",
      "kernels: slice_params 0.05 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 1.71 sec\n",
      "Iter 2\n",
      "Annealing at temp= 14.8139953966\n",
      "kernels: nonconj_gibbs 0.91 sec\n",
      "kernels: slice_params 0.03 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.98 sec\n",
      "Iter 3\n",
      "Annealing at temp= 10.0793683992\n",
      "kernels: nonconj_gibbs 0.48 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.53 sec\n",
      "Iter 4\n",
      "Annealing at temp= 6.85795186282\n",
      "kernels: nonconj_gibbs 0.44 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.49 sec\n",
      "Iter 5\n",
      "Annealing at temp= 4.6661161583\n",
      "kernels: nonconj_gibbs 0.37 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.42 sec\n",
      "Iter 6\n",
      "Annealing at temp= 3.17480210394\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 7\n",
      "Annealing at temp= 2.16011947778\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 8\n",
      "Annealing at temp= 1.46973449228\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 9\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.39 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.44 sec\n",
      "Iter 10\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 11\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.39 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.45 sec\n",
      "Iter 12\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.37 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 13\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.36 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.41 sec\n",
      "Iter 14\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.37 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 15\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.35 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.40 sec\n",
      "Iter 16\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.36 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.41 sec\n",
      "Iter 17\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.35 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.40 sec\n",
      "Iter 18\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.36 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.43 sec\n",
      "Iter 19\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.35 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.40 sec\n",
      "Iter 20\n"
     ]
    }
   ],
   "source": [
    "# inference? \n",
    "\n",
    "\n",
    "conmat = conmat.astype(np.uint8)\n",
    "\n",
    "model_name = \"BetaBernoulliNonConj\"\n",
    "init, data = irm.irmio.default_graph_init(conmat, model_name)\n",
    "init['domains']['d1']['assignment'] = np.random.permutation(len(conmat)) % 100\n",
    "\n",
    "mulamb = 10.0\n",
    "p_max = 0.95\n",
    "\n",
    "HPS = {'alpha' : 1.0, 'beta' : 1.0}\n",
    "\n",
    "init['relations']['R1']['hps'] = HPS\n",
    "slow_anneal = irm.runner.default_kernel_anneal(iterations=10)\n",
    "\n",
    "kc ={'ITERS' : 20, \n",
    "    'kernels' : slow_anneal}\n",
    "\n",
    "s = cvpipelineutil.run_exp_pure(data, init, 'anneal_slow_10', 0, kc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res = s['res']\n",
    "scores, state, times, _ = res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "/j8Rj7blnPPkHAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fea1b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = irm.util.canonicalize_assignment(state['domains']['d1']['assignment'])\n",
    "ca = np.argsort(a).flatten()\n",
    "conmat_sorted = conmat[ca]\n",
    "conmat_sorted = conmat_sorted[:, ca]\n",
    "pylab.imshow(conmat_sorted, interpolation='nearest')\n",
    "pylab.grid(False)\n",
    "pylab.ylabel('e_i')\n",
    "pylab.xlabel('e_j')\n",
    "pylab.title(\"e_i tries to talk to e_j\")\n",
    "pos = np.argwhere(np.diff(a[ca]))\n",
    "for p in pos:\n",
    "    pylab.axhline(p, c='b', alpha=0.2)\n",
    "    pylab.axvline(p, c='b', alpha=0.2)\n",
    "pylab.xticks([])\n",
    "pylab.yticks([])\n",
    "pylab.savefig(\"nodist.recovered.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# synthetic data, distance-dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "true_classes = ['gradstudent', 'postdoc', 'faculty', 'vendor']\n",
    "\n",
    "tries_talk_probs = {}\n",
    "MAXDIST = 100\n",
    "\n",
    "tries_talk_probs[('gradstudent', 'gradstudent')] =  (2.0, 0.5)\n",
    "tries_talk_probs[('gradstudent', 'postdoc')] = (1.0, 0.5) \n",
    "tries_talk_probs[('gradstudent', 'faculty')] = (3.0, 0.8) \n",
    "tries_talk_probs[('gradstudent', 'vendor')] = (0.8, 0.5)\n",
    "\n",
    "tries_talk_probs[('postdoc', 'postdoc')] = (1.0, 0.8)\n",
    "tries_talk_probs[('postdoc', 'faculty')] = (2.0, 0.8)\n",
    "\n",
    "tries_talk_probs[('faculty', 'faculty')] = (2.0, 0.9)\n",
    "\n",
    "tries_talk_probs[('vendor', 'gradstudent')] =  (0.3, 0.2)\n",
    "tries_talk_probs[('vendor', 'postdoc')] = (3.0, 0.5) \n",
    "tries_talk_probs[('vendor', 'faculty')] = (3.0, 0.5) \n",
    "tries_talk_probs[('vendor', 'vendor')] = (1.0, 0.1)\n",
    "\n",
    "# turn into numeric types\n",
    "ttp_numeric = {}\n",
    "for k, v in tries_talk_probs.iteritems():\n",
    "    ttp_numeric[(true_classes.index(k[0]), true_classes.index(k[1]))] = v\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "\n",
    "nodes, conmat = irm.data.generate.c_class_neighbors(10, ttp_numeric, JITTER=0.1)\n",
    "np.fill_diagonal(conmat, 0) # don't try and talk to yourself\n",
    "#randomize the order \n",
    "ca = np.random.permutation(len(nodes))\n",
    "nodes = nodes[ca]\n",
    "conmat = conmat[ca]\n",
    "conmat = conmat[:, ca]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11181b0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_conmat = conmat.copy()\n",
    "pylab.imshow(dist_conmat, interpolation='nearest')\n",
    "pylab.grid(False)\n",
    "pylab.ylabel('e_i')\n",
    "pylab.xlabel('e_j')\n",
    "\n",
    "pylab.xticks([])\n",
    "pylab.yticks([])\n",
    "pylab.title(\"e_i tries to talk to e_j\")\n",
    "pylab.savefig(\"dist.data.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inference with no distance dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating threadpool with 8 cores\n",
      "FIXED_K= False\n",
      "Running with relation_class= <class 'irm.pyirmutil.ParRelation'>\n",
      "This model fixed_k=  False\n",
      "Annealing at temp= 32.0\n",
      "kernels: nonconj_gibbs 2.47 sec\n",
      "kernels: slice_params 0.04 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 2.55 sec\n",
      "Iter 1\n",
      "Annealing at temp= 21.7726400028\n",
      "kernels: nonconj_gibbs 0.63 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.68 sec\n",
      "Iter 2\n",
      "Annealing at temp= 14.8139953966\n",
      "kernels: nonconj_gibbs 0.42 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.47 sec\n",
      "Iter 3\n",
      "Annealing at temp= 10.0793683992\n",
      "kernels: nonconj_gibbs 0.42 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.47 sec\n",
      "Iter 4\n",
      "Annealing at temp= 6.85795186282\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.44 sec\n",
      "Iter 5\n",
      "Annealing at temp= 4.6661161583\n",
      "kernels: nonconj_gibbs 0.48 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.53 sec\n",
      "Iter 6\n",
      "Annealing at temp= 3.17480210394\n",
      "kernels: nonconj_gibbs 0.51 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.56 sec\n",
      "Iter 7\n",
      "Annealing at temp= 2.16011947778\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 8\n",
      "Annealing at temp= 1.46973449228\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 9\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 10\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.51 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.56 sec\n",
      "Iter 11\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 12\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 13\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.59 sec\n",
      "Iter 14\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.59 sec\n",
      "Iter 15\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.51 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 16\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 17\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 18\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 19\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 20\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 21\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 22\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 23\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 24\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 25\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 26\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.59 sec\n",
      "Iter 27\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 28\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.57 sec\n",
      "Iter 29\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.53 sec\n",
      "kernels: slice_params 0.02 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.01 sec\n",
      "kernels: anneal 0.58 sec\n",
      "Iter 30\n"
     ]
    }
   ],
   "source": [
    "conmat = conmat.astype(np.uint8)\n",
    "\n",
    "model_name = \"BetaBernoulliNonConj\"\n",
    "init, data = irm.irmio.default_graph_init(conmat, model_name)\n",
    "init['domains']['d1']['assignment'] = np.random.permutation(len(conmat))  % 100\n",
    "\\\n",
    "HPS = {'alpha' : 1.0, 'beta' : 1.0}\n",
    "\n",
    "init['relations']['R1']['hps'] = HPS\n",
    "slow_anneal = irm.runner.default_kernel_anneal(iterations=10)\n",
    "\n",
    "kc ={'ITERS' : 30, \n",
    "    'kernels' : slow_anneal}\n",
    "\n",
    "s = cvpipelineutil.run_exp_pure(data, init, 'anneal_slow_10', 0, kc)\n",
    "res = s['res']\n",
    "scores, state, times, _ = res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "Ug9XSl0AcALguHGZJgWZAr9htNYfBPABAHcDOAVwj9b63W1LNSnJdMtNRCilHgXgc1rrR7QuyySe\n",
       "OYeffBOl1EMB/CNx6pEAHgXgj+uWaJKbqeEnkw0x5/CTyYaYAj+ZbIgp8JPJhpgCP5lsiCnwk8mG\n",
       "mAI/mWyI/wfCrmJ+r6lL4QAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1129059d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = irm.util.canonicalize_assignment(state['domains']['d1']['assignment'])\n",
    "ca = np.argsort(a).flatten()\n",
    "conmat_sorted = conmat[ca]\n",
    "conmat_sorted = conmat_sorted[:, ca]\n",
    "pylab.imshow(conmat_sorted, interpolation='nearest')\n",
    "pylab.grid(False)\n",
    "pylab.ylabel('e_i')\n",
    "pylab.xlabel('e_j')\n",
    "pos = np.argwhere(np.diff(a[ca]))\n",
    "for p in pos:\n",
    "    pylab.axhline(p, c='b', alpha=0.2)\n",
    "    pylab.axvline(p, c='b', alpha=0.2)\n",
    "pylab.xticks([])\n",
    "pylab.yticks([])\n",
    "pylab.title(\"e_i tries to talk to e_j\")\n",
    "pylab.savefig(\"dist.nodistrecover.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "print np.unique(a) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[40,  1, 56,  0,  2,  1,  0],\n",
       "       [ 0,  0,  0,  0, 49, 51,  0],\n",
       "       [ 0,  0,  0, 55,  2,  1, 42],\n",
       "       [43, 57,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.confusion_matrix(nodes['class'], a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conn_and_dist.dtype [('link', 'u1'), ('distance', '<f4')]\n",
      "Creating threadpool with 8 cores\n",
      "FIXED_K= False\n",
      "Running with relation_class= <class 'irm.pyirmutil.ParRelation'>\n",
      "This model fixed_k=  False\n",
      "Annealing at temp= 32.0\n",
      "kernels: nonconj_gibbs 2.87 sec\n",
      "kernels: slice_params 0.17 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.12 sec\n",
      "kernels: anneal 3.19 sec\n",
      "Iter 1\n",
      "Annealing at temp= 21.7726400028\n",
      "kernels: nonconj_gibbs 2.67 sec\n",
      "kernels: slice_params 0.17 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 2.99 sec\n",
      "Iter 2\n",
      "Annealing at temp= 14.8139953966\n",
      "kernels: nonconj_gibbs 2.77 sec\n",
      "kernels: slice_params 0.17 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.18 sec\n",
      "kernels: anneal 3.16 sec\n",
      "Iter 3\n",
      "Annealing at temp= 10.0793683992\n",
      "kernels: nonconj_gibbs 2.73 sec\n",
      "kernels: slice_params 0.22 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.16 sec\n",
      "kernels: anneal 3.15 sec\n",
      "Iter 4\n",
      "Annealing at temp= 6.85795186282\n",
      "kernels: nonconj_gibbs 2.56 sec\n",
      "kernels: slice_params 0.16 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.14 sec\n",
      "kernels: anneal 2.89 sec\n",
      "Iter 5\n",
      "Annealing at temp= 4.6661161583\n",
      "kernels: nonconj_gibbs 2.34 sec\n",
      "kernels: slice_params 0.15 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 2.65 sec\n",
      "Iter 6\n",
      "Annealing at temp= 3.17480210394\n",
      "kernels: nonconj_gibbs 1.75 sec\n",
      "kernels: slice_params 0.12 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.12 sec\n",
      "kernels: anneal 2.03 sec\n",
      "Iter 7\n",
      "Annealing at temp= 2.16011947778\n",
      "kernels: nonconj_gibbs 0.65 sec\n",
      "kernels: slice_params 0.11 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.92 sec\n",
      "Iter 8\n",
      "Annealing at temp= 1.46973449228\n",
      "kernels: nonconj_gibbs 0.37 sec\n",
      "kernels: slice_params 0.11 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.11 sec\n",
      "kernels: anneal 0.62 sec\n",
      "Iter 9\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.35 sec\n",
      "kernels: slice_params 0.13 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.14 sec\n",
      "kernels: anneal 0.64 sec\n",
      "Iter 10\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.41 sec\n",
      "kernels: slice_params 0.14 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.70 sec\n",
      "Iter 11\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.34 sec\n",
      "kernels: slice_params 0.11 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.14 sec\n",
      "kernels: anneal 0.62 sec\n",
      "Iter 12\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.39 sec\n",
      "kernels: slice_params 0.08 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.63 sec\n",
      "Iter 13\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.46 sec\n",
      "kernels: slice_params 0.08 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.71 sec\n",
      "Iter 14\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.43 sec\n",
      "kernels: slice_params 0.10 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.69 sec\n",
      "Iter 15\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.52 sec\n",
      "kernels: slice_params 0.08 sec\n",
      "kernels: domain_hp_grid 0.02 sec\n",
      "kernels: relation_hp_grid 0.10 sec\n",
      "kernels: anneal 0.72 sec\n",
      "Iter 16\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.36 sec\n",
      "kernels: slice_params 0.08 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.60 sec\n",
      "Iter 17\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.44 sec\n",
      "kernels: slice_params 0.08 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.13 sec\n",
      "kernels: anneal 0.69 sec\n",
      "Iter 18\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.48 sec\n",
      "kernels: slice_params 0.10 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.15 sec\n",
      "kernels: anneal 0.77 sec\n",
      "Iter 19\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.45 sec\n",
      "kernels: slice_params 0.09 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.11 sec\n",
      "kernels: anneal 0.68 sec\n",
      "Iter 20\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.43 sec\n",
      "kernels: slice_params 0.10 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.15 sec\n",
      "kernels: anneal 0.72 sec\n",
      "Iter 21\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.42 sec\n",
      "kernels: slice_params 0.10 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.15 sec\n",
      "kernels: anneal 0.69 sec\n",
      "Iter 22\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.40 sec\n",
      "kernels: slice_params 0.09 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.12 sec\n",
      "kernels: anneal 0.64 sec\n",
      "Iter 23\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.36 sec\n",
      "kernels: slice_params 0.10 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.12 sec\n",
      "kernels: anneal 0.61 sec\n",
      "Iter 24\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.43 sec\n",
      "kernels: slice_params 0.11 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.17 sec\n",
      "kernels: anneal 0.75 sec\n",
      "Iter 25\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.45 sec\n",
      "kernels: slice_params 0.11 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.14 sec\n",
      "kernels: anneal 0.74 sec\n",
      "Iter 26\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.09 sec\n",
      "kernels: domain_hp_grid 0.04 sec\n",
      "kernels: relation_hp_grid 0.14 sec\n",
      "kernels: anneal 0.65 sec\n",
      "Iter 27\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.38 sec\n",
      "kernels: slice_params 0.09 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.12 sec\n",
      "kernels: anneal 0.62 sec\n",
      "Iter 28\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.50 sec\n",
      "kernels: slice_params 0.09 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.11 sec\n",
      "kernels: anneal 0.73 sec\n",
      "Iter 29\n",
      "Annealing at temp= 1.0\n",
      "kernels: nonconj_gibbs 0.42 sec\n",
      "kernels: slice_params 0.10 sec\n",
      "kernels: domain_hp_grid 0.03 sec\n",
      "kernels: relation_hp_grid 0.12 sec\n",
      "kernels: anneal 0.68 sec\n",
      "Iter 30\n"
     ]
    }
   ],
   "source": [
    "def dist(a, b):\n",
    "    return np.sqrt(np.sum((b-a)**2))\n",
    "\n",
    "conn_and_dist = np.zeros(conmat.shape, \n",
    "                         dtype=[('link', np.uint8), \n",
    "                                ('distance', np.float32)])\n",
    "print \"conn_and_dist.dtype\", conn_and_dist.dtype\n",
    "for ni, foo in enumerate(nodes):\n",
    "    for nj, bar in enumerate(nodes):\n",
    "        conn_and_dist[ni, nj]['link'] = conmat[ni, nj]\n",
    "        conn_and_dist[ni, nj]['distance'] = dist(nodes[ni]['pos'],\n",
    "                                                 nodes[nj]['pos'])\n",
    "            \n",
    "\n",
    "model_name = \"LogisticDistance\"\n",
    "init, data = irm.irmio.default_graph_init(conn_and_dist, model_name)\n",
    "init['domains']['d1']['assignment'] = np.random.permutation(len(conmat))  % 100\n",
    "\n",
    "mulamb = 1.0\n",
    "p_max = 0.95\n",
    "\n",
    "HPS = {'mu_hp' : mulamb,\n",
    "       'lambda_hp' : mulamb,\n",
    "       'p_min' : 0.01, \n",
    "       'p_max' : p_max}\n",
    "\n",
    "init['relations']['R1']['hps'] = HPS\n",
    "\n",
    "slow_anneal = irm.runner.default_kernel_anneal(iterations=10)\n",
    "\n",
    "kc ={'ITERS' : 30, \n",
    "    'kernels' : slow_anneal}\n",
    "\n",
    "s = cvpipelineutil.run_exp_pure(data, init, 'anneal_slow_10', 0, kc)\n",
    "res = s['res']\n",
    "scores, state, times, _ = res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "bVHk6B5e+X+MMQ8E8A+Brx4K4GEA/mjdFim1UQ2vKDtC9/CKsiNU4BVlR6jAK8qOUIFXlB2hAq8o\n",
       "O0IFXlF2xP8BNl1Z60W5yPAAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112912c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = irm.util.canonicalize_assignment(state['domains']['d1']['assignment'])\n",
    "ca = np.argsort(a).flatten()\n",
    "\n",
    "conmat_sorted = conn_and_dist['link'][ca]\n",
    "conmat_sorted = conmat_sorted[:, ca]\n",
    "pylab.imshow(conmat_sorted, interpolation='nearest')\n",
    "pylab.grid(False)\n",
    "pylab.ylabel('e_i')\n",
    "pylab.xlabel('e_j')\n",
    "\n",
    "pos = np.argwhere(np.diff(a[ca]))\n",
    "for p in pos:\n",
    "    pylab.axhline(p, c='b', alpha=0.2)\n",
    "    pylab.axvline(p, c='b', alpha=0.2)\n",
    "\n",
    "pylab.xticks([])\n",
    "pylab.yticks([])\n",
    "pylab.title(\"e_i tries to talk to e_j\")\n",
    "pylab.savefig(\"dist.distrecover.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0, 100,   0],\n",
       "       [  0, 100,   0,   0],\n",
       "       [  0,   0,   0, 100],\n",
       "       [100,   0,   0,   0]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.confusion_matrix(nodes['class'], a)\n"
   ]
  }
 ],
 "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}