import notebooks from other repo

Author: tim3z

notebooks/Core_hours.ipynb

@@ -0,0 +1,114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib as mpl\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import json\n",
+    "import glob\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = {}\n",
+    "\n",
+    "for path in glob.glob(\"../../data/results/lfr_params_ground_truth/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/lfr_params/**/*.json\", recursive=True) + \\\n",
+    "    glob.glob(\"../../data/results/all_real/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/all_real_seq/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/all_real_seq2/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/gossip_map/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/gossip_map2/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/louvain_baseline/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/infomap_baseline/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/weak_scaling/*.json\") + \\\n",
+    "    glob.glob(\"../../data/results/weak_scaling_small_quality/**/*.json\", recursive=True):\n",
+    "  for typename, items in json.load(open(path)).items():\n",
+    "      if typename in data:\n",
+    "        for key, object_data in items.items():\n",
+    "          if key in data[typename]:\n",
+    "            data[typename][key].update(object_data)\n",
+    "          else:\n",
+    "            data[typename][key] = object_data\n",
+    "      else:\n",
+    "        data[typename] = items\n",
+    "\n",
+    "frames = { typename: pd.DataFrame.from_dict(items, orient='index') for typename, items in data.items() }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frames['algorithm_run']['runtime'].fillna((frames['algorithm_run']['done_ts'] - frames['algorithm_run']['start_ts']) / 1000000.0, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frames['program_run']['hosts'].fillna(1, inplace=True)\n",
+    "frames['program_run']['workers_per_host'].fillna(1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5908500805376624"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = frames['algorithm_run'].merge(frames['program_run'], left_on='program_run_id', right_index=True)\n",
+    "(data['runtime'] * data['hosts'] * data['workers_per_host']).sum() / 60 / 60 / 24 / 365"
+   ]
+  }
+ ],
+ "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/LFRGeneration.ipynb

@@ -0,0 +1,66 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from networkit import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "n = 1000000\n",
+    "generator = generators.LFRGenerator(n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for gamma in [-2]:\n",
+    "    for beta in [-1]:\n",
+    "        for mu in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]:\n",
+    "            generator.setDegreeSequence(generators.PowerlawDegreeSequence(50, 10000, gamma).run().getDegreeSequence(n))\n",
+    "            #generator.generatePowerlawDegreeSequence(50, 10000, gamma) EEEEEEVIL\n",
+    "            generator.generatePowerlawCommunitySizeSequence(50, 12000, beta)\n",
+    "            generator.setMu(mu)\n",
+    "            generator.run()\n",
+    "            \n",
+    "            community.writeCommunities(generator.getPartition(), \"graph_g{0}_b{1}_m{2}.part\".format(-gamma, -beta, mu))\n",
+    "            graphio.writeGraph(generator.getGraph(), \"graph_g{0}_b{1}_m{2}.graph\".format(-gamma, -beta, mu), Format.METIS)"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}