08.topological_order.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.append('..')\n",
    "from gesetz import Gesetz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(\"pickles/laws_links_topics.pickle\", \"rb\") as fp:   # Unpickling\n",
    "    Gesetz.collected_laws = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from graph import Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "{0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14}\n"
    }
   ],
   "source": [
    "Graph.set_big(Gesetz.collected_laws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subgraph(reduced):\n",
    "    laws = []\n",
    "    for law in reduced:\n",
    "        if type(law) == Gesetz:\n",
    "            laws.append(law)\n",
    "        else:\n",
    "            laws.append(Gesetz.collected_laws[law])\n",
    "\n",
    "    G = nx.MultiDiGraph()\n",
    "    for law in laws:\n",
    "        G.add_node(law.name_short)\n",
    "\n",
    "    for law in laws:\n",
    "        for link in law.links:\n",
    "            alles = False\n",
    "            if alles or link.target!= None and link.target in G:\n",
    "                G.add_edge(law.name_short,link.target)\n",
    "    return G"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "not found: []\n"
    }
   ],
   "source": [
    "graph = Graph.build_handpicked(\"GesR beck\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = Graph.build_topics([\"GesR\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "graph = graph.filter_oldest(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = graph.G_rel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "WaStrÜbgVtrG\nPachtkredG\nBauFordSiG\nGmbHG\nGenG\nVerlG\nFeinGehG\nHypÜberlG\nFeinGehStempG\nBGB\nBGBEG\nKErzG\nTKabelVtrAG\nZVGEG\nInhPapG\nHGB\nVVGEG\nHGBEG\nRHBG\nZVG\n"
    }
   ],
   "source": [
    "for node in network:\n",
    "    print(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "GenG\nZVG\nTKabelVtrAG\nInhPapG\nBGB\nZVGEG\nBGBEG\nGmbHG\nFeinGehG\nFeinGehStempG\n"
    }
   ],
   "source": [
    "for node in network:\n",
    "    print(node)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Flow Hierarchy: 0.30452674897119336\n"
    }
   ],
   "source": [
    "from networkx.algorithms.hierarchy import flow_hierarchy\n",
    "print(\"Flow Hierarchy: \"+str(nx.flow_hierarchy(network)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Flow Hierarchy: 0.0\n"
    }
   ],
   "source": [
    "#je weniger desto mehr Kreise\n",
    "G = nx.DiGraph([(0,1),(1,2),(2,0)])\n",
    "print(\"Flow Hierarchy: \"+str(nx.flow_hierarchy(G)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_metagraph(components,graph):\n",
    "    G = nx.MultiDiGraph()\n",
    "    translation_of_keys = {}\n",
    "    for point in components:\n",
    "        if components[point] not in translation_of_keys:\n",
    "            translation_of_keys[components[point]] = []\n",
    "        translation_of_keys[components[point]].append(point)\n",
    "    for key in translation_of_keys:\n",
    "        G.add_node(key)\n",
    "    for (s,t,n) in graph.edges:\n",
    "        #if n==0:\n",
    "            #print(s+\"->\"+t)\n",
    "        if components[s] != components[t]:\n",
    "            G.add_edge(components[s],components[t])\n",
    "    return (G, translation_of_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from networkx.algorithms.shortest_paths.generic import has_path\n",
    "from networkx.algorithms.dag import lexicographical_topological_sort\n",
    "from networkx.algorithms.components import strongly_connected_components\n",
    "\n",
    "\n",
    "\n",
    "def get_topo_sort(graph,sort_key):\n",
    "    comp = strongly_connected_components(graph)\n",
    "    components = {}\n",
    "    i = 0\n",
    "    keys = {}\n",
    "    for component in strongly_connected_components(graph):\n",
    "        i = i+1\n",
    "        for node in component:\n",
    "            components[node] = i\n",
    "        if i not in keys or keys[i]<sort_key(node):\n",
    "            keys[i]=sort_key(node)\n",
    "\n",
    "    (meta_graph, translation_of_keys) = build_metagraph(components,graph)\n",
    "    to_return = []\n",
    "\n",
    "    def max_key(compared_meta_node):\n",
    "        to_ret = keys[compared_meta_node]\n",
    "        #print(\"returned: \"+str(to_return))\n",
    "        return to_ret\n",
    "    #print(keys)\n",
    "    connected_components = list(reversed(list(lexicographical_topological_sort(meta_graph,max_key))))\n",
    "\n",
    "    for index in range(len(connected_components)):\n",
    "        meta_node = connected_components[index]\n",
    "\n",
    "        to_return.append([])\n",
    "        for node in components:\n",
    "            if components[node] == meta_node:\n",
    "                to_return[-1].append(node)\n",
    "                #print(node)\n",
    "    return to_return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms.components import weakly_connected_components\n",
    "\n",
    "def sort_digraph(graph,sort_key):\n",
    "    to_sort = []\n",
    "    for comp in weakly_connected_components(graph):\n",
    "        to_sort.append((len(comp),comp))\n",
    "    to_sort.sort(reverse=True)\n",
    "    to_return = []\n",
    "    for comp in to_sort:\n",
    "        #print(comp)\n",
    "        sub_graph = get_subgraph(comp[1])\n",
    "        to_return.append(get_topo_sort(sub_graph,sort_key))\n",
    "    return to_return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_sorted_digraph(sorted):\n",
    "    for weak_component in sorted:\n",
    "        print()\n",
    "        i=0\n",
    "        for strong_component in weak_component:\n",
    "            i=i+1\n",
    "            #if len(strong_component)>1 or len(strong_component[0])>1:\n",
    "            print(str(i)+\". \"+str(strong_component))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n1. ['HGB', 'GmbHG', 'BGB', 'GenG', 'BGBEG']\n2. ['ZVG']\n3. ['ZVGEG']\n4. ['HGBEG']\n5. ['VerlG']\n6. ['VVGEG']\n7. ['BauFordSiG']\n8. ['RHBG']\n9. ['KErzG']\n10. ['PachtkredG']\n\n1. ['FeinGehG']\n2. ['FeinGehStempG']\n\n1. ['WaStrÜbgVtrG']\n\n1. ['HypÜberlG']\n\n1. ['TKabelVtrAG']\n\n1. ['InhPapG']\n"
    }
   ],
   "source": [
    "# by Ausfertigungsdatum\n",
    "import datetime\n",
    "def sort_key(node):\n",
    "    return datetime.datetime.today() - Gesetz.collected_laws[node].date\n",
    "sorted_di = sort_digraph(network,sort_key)\n",
    "print_sorted_digraph(sorted_di)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "components = {}\n",
    "i = 0\n",
    "for component in strongly_connected_components(network):\n",
    "    i = i+1\n",
    "    for node in component:\n",
    "        components[node] = i\n",
    "\n",
    "meta_graph, translation_of_keys = build_metagraph(components,network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorting = sort_digraph(network,sort_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "flatten = lambda l: [item for sublist in l for item in sublist]\n",
    "sorting = flatten(flatten(sorting))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ordG = nx.OrderedDiGraph()\n",
    "for node in sorting:\n",
    "    ordG.add_node(str(translation_of_keys[components[node]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (s,t) in meta_graph.edges():\n",
    "    ordG.add_edge(str(translation_of_keys[s]),str(translation_of_keys[t]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {}
    }
   ],
   "source": [
    "nx.drawing.draw_networkx(ordG,nx.shell_layout(ordG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms.dag import transitive_reduction\n",
    "reduced_ordG = transitive_reduction(ordG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "nx.drawing.draw_networkx(reduced_ordG,nx.shell_layout(reduced_ordG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.7 64-bit",
   "language": "python",
   "name": "python_defaultSpec_1600878799902"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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