exercise_37_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.metrics import normalized_mutual_info_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_edgelist(\"1/data.txt\", create_using = nx.Graph(), delimiter = \"\\t\")\n",
    "nodes = list(G.nodes)\n",
    "A = nx.to_numpy_array(G, nodelist = nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ground_truth = {}\n",
    "with open(\"1/nodes.txt\", 'r') as f:\n",
    "   for line in f:\n",
    "      fields = line.strip().split('\\t')\n",
    "      ground_truth[fields[0]] = int(fields[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "A_embedded = TSNE(n_components = 3).fit_transform(A)\n",
    "kmeans = KMeans(n_clusters = 8).fit(A_embedded)\n",
    "ground_truth = [ground_truth[n] for n in nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lp = list(nx.algorithms.community.asyn_lpa_communities(G))\n",
    "lp = {n: c for c in range(len(lp)) for n in lp[c]}\n",
    "lp_array = []\n",
    "for n in G.nodes:\n",
    "   lp_array.append(lp[n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2781393636303484\n",
      "0.5402861976540427\n"
     ]
    }
   ],
   "source": [
    "print(normalized_mutual_info_score(kmeans.labels_, ground_truth))\n",
    "print(normalized_mutual_info_score(lp_array, ground_truth))"
   ]
  }
 ],
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