09.abgeschlossenheit.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load"
   ]
  },
  {
   "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": [
    "## Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ovw = nx.MultiDiGraph()\n",
    "\n",
    "interesting_topics = [\"GesR\",\"ArbR\"]\n",
    "\n",
    "for law in Gesetz.collected_laws.values():\n",
    "    for link in law.links:\n",
    "        if link.target ==None:\n",
    "            continue\n",
    "\n",
    "        targ_top = Gesetz.collected_laws[link.target].get_topic()\n",
    "        if targ_top not in interesting_topics:\n",
    "            targ_top = 0\n",
    "        src_top = law.get_topic()\n",
    "        if src_top not in interesting_topics:\n",
    "            src_top = 0\n",
    "        ovw.add_edge(src_top,targ_top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0, 'GesR', 'ArbR']\n"
    }
   ],
   "source": [
    "print(ovw.nodes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## start visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = {}\n",
    "for (s,t,n) in ovw.edges:\n",
    "    if (s,t) not in values or values[(s,t)]<n:\n",
    "        values[(s,t)]=n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "9"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "len(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "source = []\n",
    "target = []\n",
    "value = []\n",
    "\n",
    "for (s,t) in values:\n",
    "    if (s != 0) and (t==s or t==0):\n",
    "        source.append(s)\n",
    "        target.append(t)\n",
    "        value.append(values[(s,t)])\n",
    "\n",
    "\n",
    "\n",
    "nodes = [node for node in ovw.nodes]\n",
    "\n",
    "source_scaled = source\n",
    "target_scaled = target\n",
    "label = []\n",
    "for index in range(len(nodes)):\n",
    "    node = nodes[index]\n",
    "    source_scaled = [index if src==node else src for src in source_scaled]\n",
    "    target_scaled = [index+(len(nodes)) if tgt==node else tgt for tgt in target_scaled]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[1, 1, 2, 2]"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "source_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "[4, 3, 5, 3]"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "target_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "['others', 'GesR_out', 'ArbR_out', 'others', 'GesR_in', 'ArbR_in']\n"
    }
   ],
   "source": [
    "label = [\"\"+str(lbl).zfill(2)+\"_out\" if lbl != 0 else \"others\" for lbl in ovw.nodes]\n",
    "for nde in ovw.nodes:\n",
    "    label.append(\"\"+str(nde).zfill(2)+\"_in\"if nde != 0 else \"others\")\n",
    "print(label)"
   ]
  },
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   "execution_count": 13,
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           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {}
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = label,\n",
    "      color = [\"gray\",\"goldenrod\",\"green\",\"gray\",\"goldenrod\",\"green\"]\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = source_scaled, # indices correspond to labels, eg A1, A2, A2, B1, ...\n",
    "      target = target_scaled,\n",
    "      value = value,\n",
    "      color = [\"goldenrod\",\"goldenrod\",\"green\",\"green\"]\n",
    "  ))])\n",
    "\n",
    "fig.update_layout(font_size=10,font=dict(family='Times New Roman',size=12))\n",
    "fig.show()"
   ]
  },
  {
   "source": [
    "# FHM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "from graph import Graph\n",
    "Graph.set_big(Gesetz.collected_laws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "SachenR\nnot found: []\nStatistikR\nnot found: []\nBankR\nnot found: []\nStrafR\nnot found: []\nOrganR\nnot found: []\nVersorgungsR\nnot found: []\nPatentR\nnot found: []\nKommR\nnot found: []\nArbR\nnot found: []\nAgrarR\nnot found: []\nVerkehrsR\nnot found: []\nMedR\nnot found: []\nInsolvenzR\nnot found: []\nEntsorgungsR\nnot found: []\nGebührenR\nnot found: []\nBerufsR\nnot found: []\nProduktR\nnot found: []\nUmweltR\nnot found: []\nWohnR\nnot found: []\nProzessR\nnot found: []\nGesR\nnot found: []\nFinMarktR\nnot found: []\nAsylR\nnot found: []\nBeamtenR\nnot found: []\nSteuerR\nnot found: []\nKriegsfolgenR\nnot found: []\nBauR\nnot found: []\nBahnR\nnot found: []\nWahlR\nnot found: []\n"
    }
   ],
   "source": [
    "topics = set([law.get_topic() for law in Gesetz.collected_laws.values()])\n",
    "FHMs = []\n",
    "for topic in topics:\n",
    "    print(topic)\n",
    "    graph = Graph.build_topics([topic])\n",
    "    if len(graph.G_rel.edges) >0:\n",
    "        FHMs.append((graph.get_flow(),topic))\n",
    "\n",
    "FHMs.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[(0.0, 'FinMarktR'), (0.0928616550852811, 'SteuerR'), (0.09859154929577463, 'BauR'), (0.24293785310734461, 'ProzessR'), (0.2571428571428571, 'PatentR'), (0.2735949098621421, 'StrafR'), (0.2845303867403315, 'BeamtenR'), (0.3224043715846995, 'BahnR'), (0.3294573643410853, 'UmweltR'), (0.3648474430597336, 'GesR'), (0.3669724770642202, 'MedR'), (0.435820895522388, 'SachenR'), (0.4444444444444444, 'KommR'), (0.4931506849315068, 'StatistikR'), (0.5128205128205128, 'ArbR'), (0.6, 'VerkehrsR'), (0.7280219780219781, 'VersorgungsR'), (0.8467153284671532, 'OrganR'), (0.92, 'BerufsR'), (1.0, 'BankR'), (1.0, 'EntsorgungsR'), (1.0, 'GebührenR'), (1.0, 'InsolvenzR'), (1.0, 'KriegsfolgenR'), (1.0, 'ProduktR'), (1.0, 'WahlR')]\n"
    }
   ],
   "source": [
    "print(FHMs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open(\"FHM.txt\",\"w\",encoding=\"utf8\")\n",
    "\n",
    "\n",
    "for r,n in FHMs:\n",
    "    file.write(str(r).ljust(19,\"0\")+\": \")\n",
    "    file.write(n+\"\\n\")\n",
    "\n",
    "file.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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