solution-landscape.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze the solution landscape of networks partitioned with Infomap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To install or upgrade to to the latest version of Infomap, see https://mapequation.github.io/infomap/\n",
    "# pip install infomap / pip install --upgrade infomap\n",
    "import sys\n",
    "import infomap\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from IPython.display import Image\n",
    "from scipy.interpolate import griddata\n",
    "import umap\n",
    "from matplotlib import rc\n",
    "rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "rc('text', usetex=True)\n",
    "sns.set(style=\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Update to latest partition validation code (requires a gcc compiler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already up to date.\n",
      "make: `partition-validation' is up to date.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "#Clone partition-validation repo if it does not exist, otherwhise pull. Then compile.\n",
    "if [ ! -d \"partition-validation\" ] ; then\n",
    "    git clone https://github.com/mapequation/partition-validation.git\n",
    "else\n",
    "    git -C partition-validation pull\n",
    "fi\n",
    "make -C partition-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Update infomap module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
      "\u001b[0mRequirement already satisfied: infomap in /opt/homebrew/lib/python3.9/site-packages (2.7.1)\n",
      "\u001b[33mDEPRECATION: Configuring installation scheme with distutils config files is deprecated and will no longer work in the near future. If you are using a Homebrew or Linuxbrew Python, please see discussion at https://github.com/Homebrew/homebrew-core/issues/76621\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade infomap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find communities in the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findCommunities(networkName=\"data/jazz.net\", seed=1):\n",
    "    \"\"\"\n",
    "    Partition network with the Infomap algorithm.\n",
    "    Annotates nodes with 'community' id.\n",
    "    \"\"\"\n",
    "\n",
    "    im = infomap.Infomap(f\"-s {seed} --silent\")\n",
    "    im.read_file(networkName)\n",
    "    \n",
    "    print(f\"Find communities in {networkName} with Infomap...\")\n",
    "    im.run()\n",
    "\n",
    "    print(f\"Found {im.num_top_modules} top modules with codelength: {im.codelength}\")\n",
    "    communityDict = {node.node_id: \":\".join(map(str, node.path[:-1]))\n",
    "                    for node in im.nodes}\n",
    "    \n",
    "    return im.codelength, [path for _, path in sorted(communityDict.items())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def printCommunities(networkName=\"data/jazz.net\", seed=1, outfile=\"outfile.tree\"):\n",
    "    \"\"\"\n",
    "    Partition network with the Infomap algorithm.\n",
    "    Print partition to .tree file\n",
    "    \"\"\"\n",
    "\n",
    "    im = infomap.Infomap(f\"-s {seed} --silent\")\n",
    "    im.read_file(networkName)\n",
    "    \n",
    "    print(f\"Find communities in {networkName} with Infomap...\")\n",
    "    im.run()\n",
    "    print(f\"Found {im.num_top_modules} top modules with codelength: {im.codelength}\")\n",
    "    \n",
    "    print(f\"Writing results to {outfile}\")\n",
    "    im.write_tree(outfile)\n",
    "    return im.codelength"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze partition robustness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Definitions\n",
    "\n",
    "<dl>\n",
    "  <dt>Distance threshold</dt>\n",
    "  <dd>The maximum weighted Jaccard distance between a cluster center and any other partition in the same cluster.</dd>    \n",
    "    \n",
    "  <dt>Validation score</dt>\n",
    "  <dd>The fraction of validation partitions that fit into existing clusters.</dd>\n",
    "\n",
    "  <dt>Validation set size</dt>\n",
    "  <dd>The number of partitions hold out when measuring the validation score.</dd>       \n",
    "    \n",
    "  <dt>Accuracy</dt>\n",
    "  <dd>The threshold validation score for a complete solution landscape.</dd>\n",
    "</dl>\n",
    "\n",
    "#### Partition clustering algorithm\n",
    "\n",
    "1. Order all p network partitions from highest to lowest quality (from shortest to longest code length).\n",
    "2. Let the highest quality network partition form cluster center 1.\n",
    "3. Repeat until all network partitions have been clustered. Among the not yet clustered partitions, pick the one with the highest quality and assign it to the first of the m cluster centers that it is closer to than the distance threshold. If no such cluster center exists, let it form cluster center m + 1.\n",
    "\n",
    "#### Solution landscape completeness algorithm\n",
    "\n",
    "To determine the sufficient number of searches for a solution that is good enough given the distance threshold and solution landscape accuracy, we \n",
    "\n",
    "1. Run Infomap twice the validation set size and add the new partitions to previously identified partitions.\n",
    "2. Randomly split all partitions into a validation set and a training set. Run the partition clustering algorithm on the training set and measure the validation score on the validation set. Resample a specified number of times for better estimate.\n",
    "3. Repeat 1. and 2. until the validation score is higher than the specified accuracy.\n",
    "\n",
    "#### Guidelines\n",
    "\n",
    "In general, a solution landscape with a small distance threshold and a high accuracy requires many partitions. Moreover, networks with a flat solution landscape, characterized by relatively small differences in code length for distant solutions, require more partitions than networks with a peaked solutions landscape, characterized by relatively large difference in code length for similar solutions.\n",
    "\n",
    "When exploring the solution landscape of a network, start with a relativelely large distance threshold, say 0.3, for fast results. If the alluvial diagram shows that the solutions vary more than desired, reduce the distance threshold and repeat the analysis. Continue this procedure until the solution landscape is complete for an acceptable distance threshold."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = 'data/jazz.net' #  A network with a peaked solutions landscape, characterized by relatively large difference in code length for similar solutions.\n",
    "accuracy = 0.9\n",
    "distance_threshold = 0.02 # Requires 100 partitions\n",
    "\n",
    "#network = 'data/email.net' # A network with a flat solution landscape, characterized by relatively small differences in code length for distant solutions.\n",
    "#accuracy = 0.9\n",
    "#distance_threshold = 0.3 # Requires 100 partitions\n",
    "#distance_threshold = 0.25 # Requires > 2000 partitions\n",
    "\n",
    "networkname = network.split(\"data/\", 1)\n",
    "networkname = networkname[len(networkname) - 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find new solutions until complete solution landscape\n",
    "Run as many searches as sufficient for a solution that is good enough given the distance threshold and solution landscape accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864742285794233\n",
      "Iteration 2\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.863047469426565\n",
      "Iteration 3\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.862841103282644\n",
      "Iteration 4\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 7 top modules with codelength: 6.86296375781697\n",
      "Iteration 5\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.909779914575198\n",
      "Iteration 6\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.910117006606931\n",
      "Iteration 7\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 4 top modules with codelength: 6.9099482056850015\n",
      "Iteration 8\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.861229774903978\n",
      "Iteration 9\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.910366215895199\n",
      "Iteration 10\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.863047469426565\n",
      "Iteration 11\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 4 top modules with codelength: 7.067762701593166\n",
      "Iteration 12\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.910093999462083\n",
      "Iteration 13\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.863047469426565\n",
      "Iteration 14\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 7 top modules with codelength: 6.86296375781697\n",
      "Iteration 15\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864742285794233\n",
      "Iteration 16\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864836925288081\n",
      "Iteration 17\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864742285794233\n",
      "Iteration 18\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.910117006606931\n",
      "Iteration 19\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.868479896506625\n",
      "Iteration 20\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.863047469426565\n",
      "Running partition validation with 10 training partitions and 10 validation partitions.\n",
      "Version: Feb 23, 2022.\n",
      "Command: partition-validation/partition-validation -s 123 -t 0.02 --validation-sampling 10 10 100 results/partitions.csv results/partitions_clustering.txt\n",
      "Setup:\n",
      "--> Using seed: 123\n",
      "--> Will cluster partitions such that no partition is farther away from its center than: 0.02\n",
      "--> Will read partitions from file: results/partitions.csv\n",
      "--> Will perform validation sampling of 10 partitions given 10 training partitions 100 times.\n",
      "--> Will write clusters to file: results/partitions_clustering.txt\n",
      "--> Will use number of threads: 1\n",
      "Reading partitions file with 20 partitions...of 198 nodes...done!\n",
      "Clustering 10 partitions:\n",
      "--> Number of validation partitions out of 10 that fits in one of 7 clusters is...6. \n",
      "--> Fraction of validation partitions that fits in a cluster after 1 samples: 0.6\n",
      "Clustering 10 partitions:\n",
      "60.0% of validation partitions fit in partition clusters from 10 training clusters.\n",
      "Iteration 21\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.865010491756674\n",
      "Iteration 22\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.862755928271476\n",
      "Iteration 23\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.8636291347290195\n",
      "Iteration 24\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.86729610173525\n",
      "Iteration 25\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.862755928271476\n",
      "Iteration 26\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.865646631163504\n",
      "Iteration 27\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.863047469426565\n",
      "Iteration 28\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.863047469426565\n",
      "Iteration 29\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864742285794233\n",
      "Iteration 30\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864742285794233\n",
      "Iteration 31\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.868479896506625\n",
      "Iteration 32\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.868479896506625\n",
      "Iteration 33\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.910117006606931\n",
      "Iteration 34\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.909779914575198\n",
      "Iteration 35\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 4 top modules with codelength: 6.868409015398904\n",
      "Iteration 36\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.864187311208735\n",
      "Iteration 37\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.910117006606931\n",
      "Iteration 38\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.861229774903978\n",
      "Iteration 39\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.868479896506625\n",
      "Iteration 40\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 5 top modules with codelength: 6.909779914575198\n",
      "Running partition validation with 30 training partitions and 10 validation partitions.\n",
      "Version: Feb 23, 2022.\n",
      "Command: partition-validation/partition-validation -s 123 -t 0.02 --validation-sampling 30 10 100 results/partitions.csv results/partitions_clustering.txt\n",
      "Setup:\n",
      "--> Using seed: 123\n",
      "--> Will cluster partitions such that no partition is farther away from its center than: 0.02\n",
      "--> Will read partitions from file: results/partitions.csv\n",
      "--> Will perform validation sampling of 10 partitions given 30 training partitions 100 times.\n",
      "--> Will write clusters to file: results/partitions_clustering.txt\n",
      "--> Will use number of threads: 1\n",
      "Reading partitions file with 40 partitions...of 198 nodes...done!\n",
      "Clustering 30 partitions:\n",
      "--> Number of validation partitions out of 10 that fits in one of 16 clusters is...10. \n",
      "--> Fraction of validation partitions that fits in a cluster after 1 samples: 1\n",
      "Clustering 30 partitions:\n",
      "100.0% of validation partitions fit in partition clusters from 30 training clusters.\n"
     ]
    }
   ],
   "source": [
    "n_validation = 10\n",
    "n_resamples = 100\n",
    "currentValidationScore = 0.0\n",
    "validation_data_df = pd.DataFrame(columns = [\"n_validated\",\"n_clusters\",\"n_partitions\",\"n_training\",\"n_validation\"])\n",
    "partitions_df = pd.DataFrame()\n",
    "niter = 5000\n",
    "\n",
    "for i in range(niter):\n",
    "    print(f\"Iteration {i + 1}\")\n",
    "    codelength, communities = findCommunities(network, i+1)\n",
    "    communities.insert(0, codelength)\n",
    "    partitions_df = pd.concat([partitions_df, pd.DataFrame(communities, columns = [str(i)])], axis=1)\n",
    "    \n",
    "    if (i + 1) % (2 * n_validation) == 0:\n",
    "        n_training = i + 1 - n_validation\n",
    "        print(f\"Running partition validation with {n_training} training partitions and {n_validation} validation partitions.\")\n",
    "        partitions_df.sort_values(by=0, axis=1, inplace=True)\n",
    "        partitions_df.drop(0, axis=0, inplace=False).to_csv(r'results/partitions.csv', index=False, header=False, sep=' ')    \n",
    "        !partition-validation/partition-validation -s 123 -t $distance_threshold --validation-sampling $n_training $n_validation $n_resamples results/partitions.csv results/partitions_clustering.txt\n",
    "        validation_data_tmp_df = pd.read_csv('results/partitions_clustering_validation.txt', skiprows=1, header=None, delimiter=' ').rename(columns={0: \"n_validated\", 1: \"n_clusters\"})\n",
    "        validation_data_tmp_df[\"n_partitions\"] = i + 1\n",
    "        validation_data_tmp_df[\"n_training\"] = n_training\n",
    "        validation_data_tmp_df[\"n_validation\"] = n_validation\n",
    "        currentValidationScore = validation_data_tmp_df['n_validated'].mean() / n_validation\n",
    "        print(f\"{100 * currentValidationScore}% of validation partitions fit in partition clusters from {n_training} training clusters.\")\n",
    "        validation_data_df = pd.concat([validation_data_df,validation_data_tmp_df],axis=0)\n",
    "    if currentValidationScore > accuracy:\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot validation score for solution landscape completeness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "validation_data_df[\"score\"] = (validation_data_df[\"n_validated\"] / validation_data_df[\"n_validation\"]).astype(float)\n",
    "validation_data_df[\"n_partitions\"] = validation_data_df[\"n_partitions\"].astype(int)\n",
    "sns.set(style=\"ticks\", font_scale=1.5)\n",
    "ax = sns.lineplot(x=\"n_partitions\", y=\"score\", markers=True, errorbar=('ci', 95), data=validation_data_df)\n",
    "ax.set(ylabel=\"Validation score\", xlabel=\"Number of partitions\", ylim=(0,1))\n",
    "##plt.legend(loc=4)\n",
    "plt.plot([0, validation_data_df[\"n_partitions\"].max()], [accuracy, accuracy], '-', lw=1)\n",
    "sns.despine(offset=10, trim=True);\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(FuncFormatter(lambda x, _: int(x)))\n",
    "plt.savefig(f\"results/validation_score_{networkname.split('.net')[0]}_d={distance_threshold}_a={accuracy}.png\",\n",
    "            bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 1** The validation score as a function of number of partitions with bootstrapped 95% confidence intervals and given accuracy level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare partitions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find partitions (not necessary if already done above)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "generatePartitions = False\n",
    "\n",
    "if generatePartitions:\n",
    "    partitions_df = pd.DataFrame() \n",
    "    niter = 100\n",
    "    for i in range(niter):\n",
    "        print(f\"Iteration {i+1}\")\n",
    "        codelength = findCommunities(G, i)\n",
    "        communities = list(nx.get_node_attributes(G, 'community').values())\n",
    "        communities.insert(0, codelength)\n",
    "        partitions_df = pd.concat([partitions_df, pd.DataFrame(communities,columns = [str(i)])], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify partition clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version: Feb 23, 2022.\n",
      "Command: partition-validation/partition-validation -s 123 -t 0.02 results/partitions.csv results/partitions_clustering.txt\n",
      "Setup:\n",
      "--> Using seed: 123\n",
      "--> Will cluster partitions such that no partition is farther away from its center than: 0.02\n",
      "--> Will read partitions from file: results/partitions.csv\n",
      "--> Will write clusters to file: results/partitions_clustering.txt\n",
      "--> Will use number of threads: 1\n",
      "Reading partitions file with 40 partitions...of 198 nodes...done!\n",
      "Clustering 40 partitions:\n",
      "--> Writing 16 clusters...done!\n"
     ]
    }
   ],
   "source": [
    "partitions_df.sort_values(by=0, axis=1,inplace=True)\n",
    "codelengths = partitions_df.loc[0,:].to_numpy()\n",
    "partitions_df.drop(0, axis=0,inplace=False).to_csv(r'results/partitions.csv',index=False,header=False,sep=' ')\n",
    "!partition-validation/partition-validation -s 123 -t $distance_threshold results/partitions.csv results/partitions_clustering.txt    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read results and identify cluster sizes and cluster centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read partition clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solution landscape contains 16 clusters with 40 partitions.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClusterId</th>\n",
       "      <th>PartitionId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClusterId  PartitionId\n",
       "0          1            1\n",
       "1          1            2\n",
       "2          2            3\n",
       "3          2            4\n",
       "4          2            5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only influde best maxclusters clusters\n",
    "maxclusters = 20\n",
    "partition_clusters_df = pd.read_csv('results/partitions_clustering.txt', delimiter=' ', comment='#')\n",
    "Nclusters = partition_clusters_df['ClusterId'].max()\n",
    "print(f\"Solution landscape contains {Nclusters} clusters with {partition_clusters_df.shape[0]} partitions.\")\n",
    "\n",
    "if Nclusters > maxclusters:\n",
    "    partition_clusters_df.drop(partition_clusters_df[partition_clusters_df.ClusterId > maxclusters].index, inplace=True)\n",
    "    Nclusters = partition_clusters_df['ClusterId'].max()\n",
    "    print(f\"Including only the best {Nclusters} clusters with {partition_clusters_df.shape[0]} partitions.\")\n",
    "\n",
    "partition_clusters_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clustersize</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClusterId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           clustersize\n",
       "ClusterId             \n",
       "1                    2\n",
       "2                    4\n",
       "3                    2\n",
       "4                   11\n",
       "5                    1\n",
       "6                    1\n",
       "7                    1\n",
       "8                    1\n",
       "9                    1\n",
       "10                   1\n",
       "11                   4\n",
       "12                   3\n",
       "13                   1\n",
       "14                   5\n",
       "15                   1\n",
       "16                   1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusterSizes = partition_clusters_df['ClusterId'].value_counts(sort=False)\n",
    "maxClusterSize = max(clusterSizes)\n",
    "solution_landscape_df = clusterSizes.to_frame('ClusterId').rename(columns={'ClusterId':'clustersize'})\n",
    "solution_landscape_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cluster centers and code lengths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nclusters = partition_clusters_df['ClusterId'].max()\n",
    "clusterCenters = np.empty(Nclusters)\n",
    "\n",
    "for clusterId in range(1, Nclusters + 1):\n",
    "    clusterCenters[clusterId - 1] = partition_clusters_df['PartitionId'][(partition_clusters_df['ClusterId'] == clusterId).idxmax()]\n",
    "\n",
    "clusterCenters = clusterCenters.astype(int)\n",
    "clusterCenterCodelengths = codelengths[clusterCenters - 1]\n",
    "solution_landscape_df['partitionid'] = clusterCenters.tolist()\n",
    "solution_landscape_df['codelength'] = clusterCenterCodelengths.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read pairwise cluster center distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ClusterId1</th>\n",
       "      <th>ClusterId2</th>\n",
       "      <th>Distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.140729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.053765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.029196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.047624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.165848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ClusterId1  ClusterId2  Distance\n",
       "0           1           2  0.140729\n",
       "1           1           3  0.053765\n",
       "2           1           4  0.029196\n",
       "3           1           5  0.047624\n",
       "4           1           6  0.165848"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances_df = pd.read_csv('results/partitions_clustering_distances.txt', delimiter=' ', comment='#')\n",
    "distances_df.drop(distances_df[(distances_df['ClusterId1'] > maxclusters) | (distances_df['ClusterId2'] > maxclusters)].index, inplace=True)\n",
    "distances_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create partition distance matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Partition distance matrix')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "row = distances_df.to_numpy()[:,0].astype(int) - 1\n",
    "col = distances_df.to_numpy()[:,1].astype(int) - 1\n",
    "distances = distances_df.to_numpy()[:,2]\n",
    "distance_matrix = np.zeros((Nclusters, Nclusters), dtype=distances.dtype)\n",
    "distance_matrix[row, col] = distances\n",
    "distance_matrix[col, row] = distances # Add transpose\n",
    "plt.imshow(distance_matrix, zorder=2, cmap='Blues', interpolation='nearest')\n",
    "plt.colorbar();\n",
    "plt.title('Partition distance matrix', fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify coordinates in 2D with [Uniform Manifold Approximation and Projection (UMAP)](https://github.com/lmcinnes/umap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.9/site-packages/umap/umap_.py:1857: UserWarning: using precomputed metric; inverse_transform will be unavailable\n",
      "  warn(\"using precomputed metric; inverse_transform will be unavailable\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>clustersize</th>\n",
       "      <th>partitionid</th>\n",
       "      <th>codelength</th>\n",
       "      <th>xcoord</th>\n",
       "      <th>ycoord</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ClusterId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.861230</td>\n",
       "      <td>10.802049</td>\n",
       "      <td>1.856047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>6.862756</td>\n",
       "      <td>11.316298</td>\n",
       "      <td>2.980371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6.862964</td>\n",
       "      <td>9.804729</td>\n",
       "      <td>3.215210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>6.863047</td>\n",
       "      <td>9.459167</td>\n",
       "      <td>2.041821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>6.863629</td>\n",
       "      <td>10.614752</td>\n",
       "      <td>2.455745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           clustersize  partitionid  codelength     xcoord    ycoord\n",
       "ClusterId                                                           \n",
       "1                    2            1    6.861230  10.802049  1.856047\n",
       "2                    4            3    6.862756  11.316298  2.980371\n",
       "3                    2            6    6.862964   9.804729  3.215210\n",
       "4                   11            8    6.863047   9.459167  2.041821\n",
       "5                    1           14    6.863629  10.614752  2.455745"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U = umap.UMAP(metric='precomputed', n_neighbors=Nclusters - 1, min_dist=distance_threshold)\n",
    "out = U.fit_transform(distance_matrix)\n",
    "solution_landscape_df['xcoord'] = out[:, 0]\n",
    "solution_landscape_df['ycoord'] = out[:, 1]\n",
    "solution_landscape_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize solution landscape with contour plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = solution_landscape_df['xcoord'].values\n",
    "y = solution_landscape_df['ycoord'].values\n",
    "codelength = solution_landscape_df['codelength'].values\n",
    "\n",
    "def plot_contour(x, y, codelength, resolution=200, contour_method='cubic'):\n",
    "    resolution = f\"{resolution}j\"\n",
    "    X, Y = np.mgrid[min(x):max(x):complex(resolution), min(y):max(y):complex(resolution)]\n",
    "    points = list(zip(x, y))\n",
    "    Z = griddata(points, codelength, (X, Y), method=contour_method)\n",
    "    return X, Y, Z\n",
    "\n",
    "X, Y, Z = plot_contour(x, y, codelength, resolution=200, contour_method='cubic')\n",
    "\n",
    "palette = sns.light_palette(\"navy\", reverse=True, as_cmap=True)\n",
    "sns.set(style=\"whitegrid\", font_scale=1.5)\n",
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "sns.despine(f, left=True, bottom=True)\n",
    "p0 = ax.contourf(X, Y, Z, cmap=palette, alpha=0.5)\n",
    "#cbaxes = f.add_axes([0.8, 0.1, 0.03, 0.8])\n",
    "#cbar = f.colorbar(p0, cax=cbaxes)\n",
    "p1 = sns.scatterplot(x=\"xcoord\", y=\"ycoord\", hue=\"codelength\", size=\"clustersize\",\n",
    "            sizes=(50, 500), alpha=1, palette=palette, legend=\"full\",\n",
    "            data=solution_landscape_df)\n",
    "plt.axis('equal')\n",
    "plt.title(f'Solution landscape of {networkname} with partition distance {distance_threshold} and accuracy {accuracy}')\n",
    "plt.xlabel('')\n",
    "#plt.xlabel('Embedded partition distance')\n",
    "plt.ylabel('')\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "for handle in handles[Nclusters + 1:]:\n",
    "    handle.set_color(palette(0))\n",
    "labels[Nclusters + 1] = 'Cluster size'\n",
    "#handlestepsize = max(int((len(handles)-(Nclusters+1))/4),1)\n",
    "plt.legend([handles[Nclusters + 1], handles[Nclusters + 2], handles[-1]],\n",
    "           [labels[Nclusters + 1], labels[Nclusters + 2], labels[-1]],\n",
    "           bbox_to_anchor=(1.1, 1), loc='upper left', frameon=False, ncol=1, labelspacing=1.1)\n",
    "\n",
    "for clusterId in range(1, len(out) + 1):\n",
    "    p1.text(solution_landscape_df['xcoord'].iloc[clusterId - 1] + 0.07 * solution_landscape_df['clustersize'].iloc[clusterId - 1] / maxClusterSize + 0.05,\n",
    "            solution_landscape_df['ycoord'].iloc[clusterId - 1] - 0.1, \n",
    "            np.around(solution_landscape_df['codelength'].iloc[clusterId - 1], decimals=3),\n",
    "            horizontalalignment='left', \n",
    "            size='small',\n",
    "            color=palette(0),\n",
    "            weight='semibold')\n",
    "    p1.text(solution_landscape_df['xcoord'].iloc[clusterId - 1] + 0.07 *solution_landscape_df['clustersize'].iloc[clusterId - 1] / maxClusterSize + 0.05,\n",
    "            solution_landscape_df['ycoord'].iloc[clusterId - 1] + 0.05,\n",
    "            f\"{clusterId} ({solution_landscape_df['clustersize'].iloc[clusterId - 1]})\",\n",
    "            horizontalalignment='left', \n",
    "            size='small',\n",
    "            color=palette(0),\n",
    "            weight='semibold')\n",
    "\n",
    "plt.savefig(f\"results/solution_landscape_{networkname.split('.net')[0]}_d={distance_threshold}_a={accuracy}.png\",\n",
    "            bbox_inches=\"tight\")    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 2** Solution landscape. Circles represent partition clusters with size for the number of partitions in the cluster and color for the code length of the cluster center partition. Each cluster center is labeled by its number (ordered by increasing code length), the number of partitions it contains in parenthesis, and the code length of the cluster center partition. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print two best partitions in first and second cluster and visualize their differences\n",
    "Can be analyzed and compared in https://www.mapequation.org/alluvial/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.861229774903978\n",
      "Writing results to results/jazz_cluster_1_partition_1.tree\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.861229774903978\n",
      "Writing results to results/jazz_cluster_1_partition_2.tree\n",
      "2\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.862755928271476\n",
      "Writing results to results/jazz_cluster_2_partition_1.tree\n",
      "Find communities in data/jazz.net with Infomap...\n",
      "Found 6 top modules with codelength: 6.862755928271476\n",
      "Writing results to results/jazz_cluster_2_partition_2.tree\n"
     ]
    }
   ],
   "source": [
    "for clusterRank in range(1, 3):\n",
    "    print(clusterRank)\n",
    "    for partitionRank in range(1, 3):\n",
    "        partitionId = partition_clusters_df[partition_clusters_df['ClusterId'] == clusterRank].iloc[partitionRank - 1][1]\n",
    "        seed = int(partitions_df.columns[partitionId - 1])        \n",
    "        printCommunities(network, seed, outfile=f\"results/{networkname.split('.net')[0]}_cluster_{clusterRank}_partition_{partitionRank}.tree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='results/jazz_cluster_1-1-2-2_partition_1-2-1-2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Figure 3** Close-by cluster centers are more similar than distant cluster centers. The alluvial diagram shows top two partitions in cluster 1 and 2."
   ]
  }
 ],
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