sde-reverse-test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphical Models: Similarity Distribution Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sde import *\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### make directed graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 2), (1, 0), (4, 2), (4, 1)]\n"
     ]
    }
   ],
   "source": [
    "g = makeDirectedGraph(5, 7)\n",
    "edges = nx.edges(g)\n",
    "\n",
    "print(edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw_networkx(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(1, 2): 0.766588215996896, (1, 0): 0.09776877729017086, (4, 2): 0.9609335472405739, (4, 1): 0.13785153719092236}\n"
     ]
    }
   ],
   "source": [
    "edge_weights = nx.get_edge_attributes(g, 'weight')\n",
    "print(edge_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conditional prob P(A|B) = (likelihood*pior)/marginal prob\n",
    "# likelihood P(B|A). Likelihood = (conditional prob * marginal prob)/pior. Or, P(B|A) = P(A|B)P(B)/P(A)\n",
    "# prior P(A)\n",
    "# marginal prob P(B)\n",
    "\n",
    "def reverseEdgeWeight(conditional_prob):\n",
    "    '''Accepts a conditional probability value P(A|B), returns P(B|A) and a pair of values for P(A) and P(B) that\n",
    "        would be consistent with the given conditional probability and its reversed conditional probability.)'''\n",
    "    prior = random.uniform(0,1)\n",
    "    marginal_prob = random.uniform(0,1)\n",
    "    \n",
    "    likelihood = (conditional_prob*marginal_prob)/prior\n",
    "    known_conditional_prob = (prior*likelihood)/marginal_prob\n",
    "    \n",
    "    if (known_conditional_prob != conditional_prob):\n",
    "        reverseEdgeWeight(conditional_prob)\n",
    "    else: return likelihood\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(1, 2): 0.16180792557542925, (1, 0): 0.08773275385727283, (4, 2): 1.1293621244370913, (4, 1): 1.1796867797769486}\n",
      "{(0, 1): 0.08773275385727283, (1, 4): 1.1796867797769486, (2, 4): 1.1293621244370913, (2, 1): 0.16180792557542925}\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def reverseWeightedDG(dg):\n",
    "    rdg = nx.reverse_view(dg)\n",
    "    \n",
    "    for e in dg.edges():\n",
    "        rdg[e[::-1][0]][e[::-1][1]]['weight'] = reverseEdgeWeight(dg[e[0]][e[1]]['weight'])\n",
    "        \n",
    "    print(nx.get_edge_attributes(dg, 'weight'))\n",
    "    print(nx.get_edge_attributes(rdg,'weight'))\n",
    "    return rdg\n",
    "\n",
    "\n",
    "\n",
    "g_reversed = reverseWeightedDG(g)\n",
    "nx.draw_networkx(g_reversed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reversed graph edge data: {(0, 1): 0.08773275385727283, (1, 4): 1.1796867797769486, (2, 4): 1.1293621244370913, (2, 1): 0.16180792557542925}\n"
     ]
    }
   ],
   "source": [
    "print('Reversed graph edge data:', nx.get_edge_attributes(g_reversed, 'weight'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original graph edge data: {(1, 2): 0.16180792557542925, (1, 0): 0.08773275385727283, (4, 2): 1.1293621244370913, (4, 1): 1.1796867797769486}\n"
     ]
    }
   ],
   "source": [
    "print('Original graph edge data:', nx.get_edge_attributes(g, 'weight'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the similarity measures of a weighted graph and its reverse graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "methods adapted from Nico's getSimilarityData function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = list(g.nodes)\n",
    "node_pairs = list(it.combinations(nodes, 2))\n",
    "\n",
    "nodes_reversed = list(g_reversed.nodes)\n",
    "node_pairs_reversed = list(it.combinations(nodes_reversed, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity method: Jaccard Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.0, 0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.16928724658216965, 0.16928724658216965, 0, 0, 0.0, 0.0, 0.0, 0.0]\n"
     ]
    }
   ],
   "source": [
    "jindex_original = []\n",
    "for pair in node_pairs:\n",
    "    jindex_original.append(calculate_similarity(g, pair[0], pair[1])[\"method 1\"])\n",
    "    jindex_original.append(calculate_similarity(g, pair[1], pair[0])[\"method 1\"])\n",
    "    \n",
    "print(jindex_original)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0816597560099774, 0.0816597560099774, 0.542203069134428, 0.542203069134428, 0.0, 0.0, 0.0, 0.0, 0.1422910354419372, 0.1422910354419372, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "jindex_reversed = []\n",
    "for pair in node_pairs_reversed:\n",
    "    jindex_reversed.append(calculate_similarity(g_reversed, pair[0], pair[1])[\"method 1\"])\n",
    "    jindex_reversed.append(calculate_similarity(g_reversed, pair[1], pair[0])[\"method 1\"])\n",
    "    \n",
    "print(jindex_reversed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity method: Edit distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based on node children"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1.0, 0, 0, 0, 0, 0, 1.0, 1.0, 0, 1.0, 0, 0.983777099430831, 0.983777099430831, 0, 0, 0, 1.0, 0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "ed_immediate_sim = []\n",
    "for pair in node_pairs:\n",
    "    ed_immediate_sim.append(get_immediate_similarity(g, pair[0], pair[1]))\n",
    "    ed_immediate_sim.append(get_immediate_similarity(g, pair[1], pair[0]))\n",
    "    \n",
    "print(ed_immediate_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.0, 0.07407517171815642, 0.5370375858590782, 1.0, 0, 1.0, 0, 0.050324655339857305, 0.5251623276699287, 1.0, 0, 1.0, 0, 1.0, 0, 1.0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "ed_immediate_sim_reversed = []\n",
    "for pair in node_pairs_reversed:\n",
    "    ed_immediate_sim_reversed.append(get_immediate_similarity(g_reversed, pair[0], pair[1]))\n",
    "    ed_immediate_sim_reversed.append(get_immediate_similarity(g_reversed, pair[1], pair[0]))\n",
    "    \n",
    "print(ed_immediate_sim_reversed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "based on node descendants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1.0, 0, 0, 0, 0, 0, 1.0, 1.0, 0, 1.0, 0, 0.02241958055530703, 0.34827972037020466, 0, 0, 0, 1.0, 0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "ed_full_sim = []\n",
    "for pair in node_pairs:\n",
    "    ed_full_sim.append(get_full_similarity(g, pair[0], pair[1]))\n",
    "    ed_full_sim.append(get_full_similarity(g, pair[1], pair[0]))\n",
    "    \n",
    "print(ed_full_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0380948049490493, 1.0761896098980988, 0.08073033625188643, 0.08073033625188643, 1.0, 0, 1.0, 0, 0.9888041091124823, 0.9944020545562411, 1.0, 0, 1.0, 0, 1.0, 0, 1.0, 0, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "ed_full_sim_reversed = []\n",
    "for pair in node_pairs_reversed:\n",
    "    ed_full_sim_reversed.append(get_full_similarity(g_reversed, pair[0], pair[1]))\n",
    "    ed_full_sim_reversed.append(get_full_similarity(g_reversed, pair[1], pair[0]))\n",
    "    \n",
    "print(ed_full_sim_reversed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Similarity method: Edit-distance (Networkx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for v in nx.optimize_graph_edit_distance(g, g_reversed):\n",
    "    minv = v\n",
    "\n",
    "minv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object optimize_edit_paths at 0x000001E8B785F2C8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.optimize_edit_paths(g, g_reversed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.graph_edit_distance(g, g_reversed)"
   ]
  }
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