sde-cliques-final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphical Models: Similarity Distribution Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sde import *\n",
    "import matplotlib.pyplot as plt\n",
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The jupyternotify extension is already loaded. To reload it, use:\n",
      "  %reload_ext jupyternotify\n"
     ]
    }
   ],
   "source": [
    "%load_ext jupyternotify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished!\n"
     ]
    },
    {
     "data": {
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       "            // append a div with our uuid so we can check that it's already\n",
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       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"a790f3f3-a070-408a-83e0-68e2e746c8c5\"\n",
       "            element.append(notifiedDiv)\n",
       "        }\n",
       "\n",
       "        // only send notifications if the pageload is complete; this will\n",
       "        // help stop extra notifications when a saved notebook is loaded,\n",
       "        // which during testing gives us state \"interactive\", not \"complete\"\n",
       "        if (document.readyState === 'complete') {\n",
       "            // check for the div that signifies that the notification\n",
       "            // was already sent\n",
       "            if (document.getElementById(\"a790f3f3-a070-408a-83e0-68e2e746c8c5\") === null) {\n",
       "                var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell execution has finished!\", \"autonotify_after\": \"30\", \"autonotify_output\": false};\n",
       "                if (Notification.permission !== 'denied') {\n",
       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
       "                            if(!('permission' in Notification)) {\n",
       "                                Notification.permission = permission\n",
       "                            }\n",
       "                        })\n",
       "                    }\n",
       "                    if (Notification.permission === 'granted') {\n",
       "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
       "                    appendUniqueDiv()\n",
       "                    notification.onclick = function () {\n",
       "                        window.focus();\n",
       "                        this.close();\n",
       "                        };\n",
       "                    } \n",
       "                }     \n",
       "            }\n",
       "        }\n",
       "    }\n",
       ")\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%notify\n",
    "import time\n",
    "time.sleep(10)\n",
    "print('Finished!')\n",
    "\n",
    "%autonotify -a 30"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tests - Graph Size and Clique Num"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graph with 10 cliques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "cliqNums = [10, 20, 40, 50, 60, 70, 80, 90, 100]\n",
    "cliqTest = {}\n",
    "graph_size = []\n",
    "for n in cliqNums:\n",
    "    dg = makeDirectedGraph(n*2, n*4)\n",
    "    num = len(clique.clique_removal(dg)[1])\n",
    "    graph_size.append((n*2, n*4))\n",
    "    cliqTest[n] = num\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(graph_size, list(cliqTest.values()))\n",
    "\n",
    "plt.xlabel('Graph Size')\n",
    "plt.ylabel('# of Cliqs')\n",
    "plt.title('Graph Size vs Cliq Num')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cliqNums, list(cliqTest.values()), label='# of Cliqs')\n",
    "\n",
    "plt.xlabel('Intended # of Cliq Nums')\n",
    "plt.ylabel('Actual # of Cliqs')\n",
    "plt.title('Intended vs Real')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9\n",
      "1.2\n",
      "1.15\n",
      "1.16\n",
      "1.1333333333333333\n",
      "1.1428571428571428\n",
      "1.175\n",
      "1.1888888888888889\n",
      "1.17\n"
     ]
    }
   ],
   "source": [
    "for c in cliqNums:\n",
    "    i = cliqNums.index(c)\n",
    "    test_num = list(cliqTest.values())[i]\n",
    "    print(test_num/c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dgWithCliqs(num_cliq):   \n",
    "    #create DAG\n",
    "    dg = makeDirectedGraph(num_cliq*2, num_cliq*4)\n",
    "    \n",
    "    #count num of cliques in graph\n",
    "    cliq_rem = clique.clique_removal(dg)\n",
    "    cliqCount = len(cliq_rem[1])   \n",
    "    \n",
    "    # remake graph if it doesn't match the input\n",
    "    if (cliqCount == num_cliq):\n",
    "        return dg\n",
    "    else:\n",
    "        return dgWithCliqs(num_cliq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dg10 = dgWithCliqs(5)\n",
    "nx.draw_networkx(dg10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$(document).ready(\n",
       "    function() {\n",
       "        function appendUniqueDiv(){\n",
       "            // append a div with our uuid so we can check that it's already\n",
       "            // been sent and avoid duplicates on page reload\n",
       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"da52d60f-2d87-47c9-a57d-b8798b1624b4\"\n",
       "            element.append(notifiedDiv)\n",
       "        }\n",
       "\n",
       "        // only send notifications if the pageload is complete; this will\n",
       "        // help stop extra notifications when a saved notebook is loaded,\n",
       "        // which during testing gives us state \"interactive\", not \"complete\"\n",
       "        if (document.readyState === 'complete') {\n",
       "            // check for the div that signifies that the notification\n",
       "            // was already sent\n",
       "            if (document.getElementById(\"da52d60f-2d87-47c9-a57d-b8798b1624b4\") === null) {\n",
       "                var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell Execution Has Finished!!\", \"autonotify_after\": \"30\", \"autonotify_output\": false};\n",
       "                if (Notification.permission !== 'denied') {\n",
       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
       "                            if(!('permission' in Notification)) {\n",
       "                                Notification.permission = permission\n",
       "                            }\n",
       "                        })\n",
       "                    }\n",
       "                    if (Notification.permission === 'granted') {\n",
       "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
       "                    appendUniqueDiv()\n",
       "                    notification.onclick = function () {\n",
       "                        window.focus();\n",
       "                        this.close();\n",
       "                        };\n",
       "                    } \n",
       "                }     \n",
       "            }\n",
       "        }\n",
       "    }\n",
       ")\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "$(document).ready(\n",
       "    function() {\n",
       "        function appendUniqueDiv(){\n",
       "            // append a div with our uuid so we can check that it's already\n",
       "            // been sent and avoid duplicates on page reload\n",
       "            var notifiedDiv = document.createElement(\"div\")\n",
       "            notifiedDiv.id = \"da52d60f-2d87-47c9-a57d-b8798b1624b4\"\n",
       "            element.append(notifiedDiv)\n",
       "        }\n",
       "\n",
       "        // only send notifications if the pageload is complete; this will\n",
       "        // help stop extra notifications when a saved notebook is loaded,\n",
       "        // which during testing gives us state \"interactive\", not \"complete\"\n",
       "        if (document.readyState === 'complete') {\n",
       "            // check for the div that signifies that the notification\n",
       "            // was already sent\n",
       "            if (document.getElementById(\"da52d60f-2d87-47c9-a57d-b8798b1624b4\") === null) {\n",
       "                var notificationPayload = {\"requireInteraction\": false, \"icon\": \"/static/base/images/favicon.ico\", \"body\": \"Cell Execution Has Finished!!\", \"autonotify_after\": \"30\", \"autonotify_output\": false};\n",
       "                if (Notification.permission !== 'denied') {\n",
       "                    if (Notification.permission !== 'granted') { \n",
       "                        Notification.requestPermission(function (permission) {\n",
       "                            if(!('permission' in Notification)) {\n",
       "                                Notification.permission = permission\n",
       "                            }\n",
       "                        })\n",
       "                    }\n",
       "                    if (Notification.permission === 'granted') {\n",
       "                    var notification = new Notification(\"Jupyter Notebook\", notificationPayload)\n",
       "                    appendUniqueDiv()\n",
       "                    notification.onclick = function () {\n",
       "                        window.focus();\n",
       "                        this.close();\n",
       "                        };\n",
       "                    } \n",
       "                }     \n",
       "            }\n",
       "        }\n",
       "    }\n",
       ")\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cliqNums = [5, 10, 15, 20]\n",
    "\n",
    "imm_sims, full_sims, jaccard_index = {}, {}, {}\n",
    "\n",
    "for c in cliqNums:\n",
    "    immSims, fullSims, jindices  = [], [], []\n",
    "    for i in range(10):\n",
    "        gm = dgWithCliqs(c)\n",
    "        node_list = list(gm.nodes)\n",
    "        node_pairs = list(it.combinations(node_list, 2))\n",
    "\n",
    "        #immediate similarity\n",
    "        for pair in node_pairs:\n",
    "            immSims.append(get_immediate_similarity(gm, pair[0], pair[1]))\n",
    "\n",
    "        #full similarity\n",
    "        for pair in node_pairs:\n",
    "            fullSims.append(get_full_similarity(gm, pair[0], pair[1]))\n",
    "        \n",
    "        #jaccard similarity\n",
    "        for pair in node_pairs:\n",
    "            jindices.append(calculate_jaccard_similarity(gm, pair[0], pair[1]))\n",
    "        \n",
    "        \n",
    "    imm_sims[c] = immSims\n",
    "    full_sims[c] = fullSims\n",
    "    jaccard_index[c] =jindices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Edit distance - Immediate Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "\"\"\"immSim_df = pd.DataFrame({ key:pd.Series(value) for key, value in immSims.items() })\n",
    "immSim_df\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x28c92b93e88>,\n",
       "  <matplotlib.axis.XTick at 0x28c9e9fc888>,\n",
       "  <matplotlib.axis.XTick at 0x28c9ea2b748>,\n",
       "  <matplotlib.axis.XTick at 0x28ca57bf788>],\n",
       " <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot to compare imm sim vals against num of cliques\n",
    "fig2 = plt.figure(1, figsize=(9, 6))\n",
    "\n",
    "imm_sims = collections.OrderedDict(sorted(imm_sims.items()))\n",
    "ax2 = fig2.add_subplot(111)\n",
    "ax2.boxplot(list(imm_sims.values()))\n",
    "\n",
    "ax2.set_xlabel('# of Cliques')\n",
    "ax2.set_ylabel('Edit Distance (Immediate Similarity)')\n",
    "plt.xticks([x for x in range(1, len(list(imm_sims.keys())) + 1)], list(imm_sims.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Edit distance - Full Similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"fullSims_df = pd.DataFrame({ key:pd.Series(value) for key, value in fullSims.items() })\n",
    "fullSims_df\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x28ca7ec4b88>,\n",
       "  <matplotlib.axis.XTick at 0x28ca7ec4208>,\n",
       "  <matplotlib.axis.XTick at 0x28ca5927d88>,\n",
       "  <matplotlib.axis.XTick at 0x28cac676688>],\n",
       " <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot to compare full sim vals against num of cliques\n",
    "plt.close()\n",
    "fig2 = plt.figure(1, figsize=(9, 6))\n",
    "\n",
    "full_sims = collections.OrderedDict(sorted(full_sims.items()))\n",
    "ax2 = fig2.add_subplot(111)\n",
    "ax2.boxplot(list(full_sims.values()))\n",
    "\n",
    "ax2.set_xlabel('# of Cliques')\n",
    "ax2.set_ylabel('Edit Distance (Full Similarity)')\n",
    "plt.xticks([x for x in range(1, len(list(full_sims.keys())) + 1)], list(full_sims.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jaccard Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x28caba3de48>,\n",
       "  <matplotlib.axis.XTick at 0x28caba3d4c8>,\n",
       "  <matplotlib.axis.XTick at 0x28caba1a908>,\n",
       "  <matplotlib.axis.XTick at 0x28cf67929c8>],\n",
       " <a list of 4 Text xticklabel objects>)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot to compare jaccard sim against num of cliques\n",
    "plt.close()\n",
    "fig2 = plt.figure(1, figsize=(9, 6))\n",
    "\n",
    "jaccard_index = collections.OrderedDict(sorted(jaccard_index.items()))\n",
    "ax2 = fig2.add_subplot(111)\n",
    "ax2.boxplot(list(jaccard_index.values()))\n",
    "\n",
    "ax2.set_xlabel('# of Cliques')\n",
    "ax2.set_ylabel(' Jaccard Similarity')\n",
    "plt.xticks([x for x in range(1, len(list(jaccard_index.keys())) + 1)], list(jaccard_index.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}