POC_net_vis-circle-packing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "076df02e",
   "metadata": {},
   "source": [
    "# POC of Network visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bba0c99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.80687351, 0.28834204, 0.28834204, 0.59713033,\n",
       "        0.53356025],\n",
       "       [0.80687351, 1.        , 0.28834204, 0.28834204, 0.50566167,\n",
       "        0.59224592],\n",
       "       [0.28834204, 0.28834204, 1.        , 0.31670559, 0.42810435,\n",
       "        0.32441524],\n",
       "       [0.28834204, 0.28834204, 0.31670559, 1.        , 0.33517101,\n",
       "        0.32441524],\n",
       "       [0.59713033, 0.50566167, 0.42810435, 0.33517101, 1.        ,\n",
       "        0.53593382],\n",
       "       [0.53356025, 0.59224592, 0.32441524, 0.32441524, 0.53593382,\n",
       "        1.        ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['Cersei_Lannister',\n",
       " 'Jaime_Lannister',\n",
       " 'Petyr_Baelish',\n",
       " 'Tywin_Lannister',\n",
       " 'Joffrey_Baratheon',\n",
       " 'Brienne_of_Tarth']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "sys.path.insert(0, os.path.abspath('../semantic_mapping'))\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import circlify\n",
    "import semantic_similarity\n",
    "\n",
    "from sklearn.cluster import AffinityPropagation\n",
    "\n",
    "\n",
    "\n",
    "# Computing similarity matrix\n",
    "dataset_name = 'fictional_chars'\n",
    "#semantic_similarity.build_semantic_distance_matrix(dataset_name)\n",
    "\n",
    "# Load similarity matrix\n",
    "\n",
    "sim_matrix = np.loadtxt(\"similarity_matrices/{0}.txt\".format(dataset_name))\n",
    "lst_names = semantic_similarity.load_names(dataset_name)\n",
    "\n",
    "display(sim_matrix)\n",
    "display(lst_names)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6fe4e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 2, 0, 0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 2, 3])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute clusters using Affinity Propagation algorithm\n",
    "clustering = AffinityPropagation(random_state=10, max_iter=800).fit(sim_matrix)\n",
    "display(clustering.labels_)\n",
    "display(clustering.cluster_centers_indices_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3c63cade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create semantic maps using the exemplars as centers\n",
    "exemplars_dict = {}\n",
    "av_size = 0\n",
    "for i in range(0, len(clustering.labels_)):\n",
    "    if clustering.labels_[i] not in exemplars_dict.keys():\n",
    "        exemplars_dict[clustering.labels_[i]] = []\n",
    "    exemplars_dict[clustering.labels_[i]].append(i)\n",
    "    \n",
    "# Create circles_dict for cicrle packing\n",
    "circle_lst = []\n",
    "\n",
    "for k,v in exemplars_dict.items():\n",
    "    exemplar_indx = semantic_similarity.find_exemplar(clustering.cluster_centers_indices_, v)\n",
    "    children_list = []\n",
    "    av_size += len(v)\n",
    "    for item in v:\n",
    "        children_list.append({'id': ' ', 'datum': 1})\n",
    "    circle_item = dict()\n",
    "    circle_item['id'] = lst_names[exemplar_indx]\n",
    "    circle_item['datum'] = len(v)\n",
    "    circle_item['children'] = children_list\n",
    "    circle_lst.append(circle_item)\n",
    "\n",
    "av_size = av_size / len(circle_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f32f4b67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute circle positions:\n",
    "circles = circlify.circlify(\n",
    "    circle_lst, \n",
    "    show_enclosure=False, \n",
    "    target_enclosure=circlify.Circle(x=0, y=0, r=1)\n",
    ")\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create just a figure and only one subplot\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "\n",
    "# Remove axes\n",
    "ax.axis('off')\n",
    "\n",
    "# Find axis boundaries\n",
    "lim = max(\n",
    "    max(\n",
    "        abs(circle.x) + circle.r,\n",
    "        abs(circle.y) + circle.r,\n",
    "    )\n",
    "    for circle in circles\n",
    ")\n",
    "plt.xlim(-lim, lim)\n",
    "plt.ylim(-lim, lim)\n",
    "\n",
    "\n",
    "# print circles\n",
    "for circle in circles:\n",
    "    if not circle:\n",
    "        continue\n",
    "    x, y, r = circle\n",
    "    label = circle.ex[\"id\"]\n",
    "    ax.add_patch(plt.Circle((x, y), r, alpha=0.2, linewidth=2))\n",
    "    plt.annotate(\n",
    "          label, \n",
    "          (x,y ) ,\n",
    "          va='center',\n",
    "          ha='center'\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0a927d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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