plot_paper.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import brickschema\n",
    "import datetime\n",
    "from relaxation_graphs.fast_relaxation_graph import *\n",
    "from evaluation.similarity_score import *\n",
    "import uuid\n",
    "import pickle\n",
    "from rdflib import URIRef, Literal, BNode\n",
    "from queries.mortar import queries as q1\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "font = {'family' : 'sans-serif',\n",
    "        'weight' : 'normal',\n",
    "        'size'   : 32}\n",
    "\n",
    "matplotlib.rc('font', **font)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"metrics.json\") as fp:\n",
    "    metrics = json.load(fp)\n",
    "\n",
    "with open(\"metrics_all_buildings.json\") as fp:\n",
    "    metrics = json.load(fp)\n",
    "    \n",
    "sim_df = pd.read_csv('similarity_metrics.csv')\n",
    "\n",
    "    \n",
    "useful_queries = [4, 8, 9, 10, 11, 12, 15]\n",
    "useful_buildings = range(6)\n",
    "\n",
    "files = os.listdir('Data - Brick models/')\n",
    "brick_models = []\n",
    "\n",
    "with open(\"brick_models.pkl\", 'rb') as fp:\n",
    "    brick_models = pickle.load(fp)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.0003065\n",
      "2 0.000598\n",
      "12 0.006944\n",
      "4 0.001054\n",
      "9 0.001823\n",
      "3 0.001252\n",
      "7 0.0012726666666666667\n"
     ]
    }
   ],
   "source": [
    "relaxation_time = {}\n",
    "for q in metrics:\n",
    "    size = metrics[q]['query_size']\n",
    "    if size in relaxation_time:\n",
    "        relaxation_time[size].append(metrics[q]['relaxation_time'])\n",
    "    else:\n",
    "        relaxation_time[size] = [metrics[q]['relaxation_time']]\n",
    "\n",
    "x = []\n",
    "y = []\n",
    "for size in relaxation_time:\n",
    "    print(size, sum(relaxation_time[size])/len(relaxation_time[size]))\n",
    "    x.append(size)\n",
    "    y.append(sum(relaxation_time[size])/len(relaxation_time[size])*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.Figure()\n",
    "plt.rcParams['figure.figsize'] = [10, 10]\n",
    "plt.scatter(x, y)\n",
    "plt.xlabel('size of query')\n",
    "plt.ylabel('relaxation time (ms)')\n",
    "plt.savefig('results/relaxation_time.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = []\n",
    "y1 = []\n",
    "y2 = []\n",
    "for q_idx, q in enumerate(metrics):\n",
    "    for level in metrics.get(q).get('level_building_num_nodes_with_data'):\n",
    "        building_node_num_map = metrics.get(q).get('level_building_num_nodes_with_data').get(level)\n",
    "        unique_buildings = []\n",
    "        for building in building_node_num_map:\n",
    "            if building_node_num_map[building] > 0:\n",
    "                if building not in unique_buildings:\n",
    "                    unique_buildings.append(building)\n",
    "        # print(q_idx, level, len(unique_buildings))\n",
    "        x.append(level)\n",
    "        y1.append(len(unique_buildings))\n",
    "        y2.append(q_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>level</th>\n",
       "      <th>unique_buildings</th>\n",
       "      <th>q</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     level  unique_buildings   q\n",
       "0        0                50   0\n",
       "1        1                50   0\n",
       "2        2                50   0\n",
       "3        3                 0   0\n",
       "4        4                 0   0\n",
       "..     ...               ...  ..\n",
       "97       1                 0  16\n",
       "98       2                 0  16\n",
       "99       3                 0  16\n",
       "100      4                 0  16\n",
       "101      5                 0  16\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data={'level': x, 'unique_buildings': y1, 'q':y2})\n",
    "df.level = df.level.astype(int)\n",
    "df.q = df.q.astype(int)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_level0 = df.loc[df.level==0]\n",
    "df_level1 = df.loc[df.level==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"results/level_query_buildings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.Figure()\n",
    "plt.rcParams['figure.figsize'] = [20, 15]\n",
    "plt.bar(x=[str(q) for q in df_level0.q], height=df_level0.unique_buildings)\n",
    "plt.xlabel(\"Query\")\n",
    "plt.ylabel(\"Number of Unique Buildings\")\n",
    "plt.savefig('results/level0_qualified.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.Figure(figsize=(20,10))\n",
    "# plt.bar(x=[str(q) for q in df_level1.q], height=df_level1.unique_buildings)\n",
    "# plt.xlabel(\"Query\")\n",
    "# plt.ylabel(\"Number of Unique Buildings at Level 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.Figure()\n",
    "plt.rcParams['figure.figsize'] = [20, 10]\n",
    "df.boxplot(column='unique_buildings', by='level')\n",
    "plt.grid(b=None)\n",
    "plt.title(None)\n",
    "plt.ylabel(\"Number of Unique Buildings\")\n",
    "plt.savefig('results/unique_building_hist.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for q in metrics:\n",
    "#     print('# relaxed queries: {}'.format(metrics.get(q).get('size_relaxed_graph')))\n",
    "#     print('max edits: {}'.format(metrics.get(q).get('max_edits')))\n",
    "#     num_nodes_with_data = {}\n",
    "\n",
    "#     for level in metrics.get(q).get('level_building_num_nodes_with_data'):\n",
    "#         building_node_num_map = metrics.get(q).get('level_building_num_nodes_with_data').get(level)\n",
    "#         for building_id in building_node_num_map:\n",
    "#             if building_id not in num_nodes_with_data:\n",
    "#                 num_nodes_with_data[building_id] = building_node_num_map[building_id]\n",
    "#             else:\n",
    "#                 num_nodes_with_data[building_id] += building_node_num_map[building_id]\n",
    "\n",
    "#     print(\"# relaxed queries with data: {}\".format(num_nodes_with_data))\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query_id, level0, level1, level2, level3, level4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "latex_ops = {}\n",
    "for q_idx in useful_queries:\n",
    "    q = q1[q_idx]\n",
    "    data = metrics.get(q)\n",
    "    level_dict = data.get('level_building_num_nodes_with_data')\n",
    "    for building_idx in useful_buildings:\n",
    "        latex_op = \"Q{} \".format(q_idx)\n",
    "        for level in level_dict:\n",
    "            num_nodes = level_dict.get(level).get(str(building_idx))\n",
    "            latex_op += \"& {} \".format(num_nodes)\n",
    "        \n",
    "        if building_idx not in latex_ops:\n",
    "            latex_ops[building_idx] = latex_op +\"\\\\\\ \\n\"\n",
    "        else:\n",
    "            latex_ops[building_idx] += latex_op+\" \\\\\\ \\n\"\n",
    "        \n",
    "#     for level in metrics.get(q).get('level_building_num_nodes_with_data'):\n",
    "#         building_node_num_map = metrics.get(q).get('level_building_num_nodes_with_data').get(level)\n",
    "#         for building_id in building_node_num_map:\n",
    "#             if building_id not in num_nodes_with_data:\n",
    "#                 num_nodes_with_data[building_id] = building_node_num_map[building_id]\n",
    "#             else:\n",
    "#                 num_nodes_with_data[building_id] += building_node_num_map[building_id]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Query & L0& L1& L2& L3& L4& L5\\\\ \\midrule \n",
      "Q4 & 2 & 2 & 2 & 2 & 2 & 2 \\\\ \n",
      "Q8 & 0 & 0 & 0 & 0 & 0 & 0  \\\\ \n",
      "Q9 & 0 & 0 & 1 & 4 & 8 & 11  \\\\ \n",
      "Q10 & 0 & 0 & 0 & 0 & 0 & 0  \\\\ \n",
      "Q11 & 0 & 0 & 0 & 0 & 0 & 0  \\\\ \n",
      "Q12 & 2 & 3 & 5 & 7 & 9 & 10  \\\\ \n",
      "Q15 & 2 & 1 & 1 & 1 & 1 & 0  \\\\ \\bottomrule \n",
      "\n"
     ]
    }
   ],
   "source": [
    "building_table = 1\n",
    "header = \"Query \"\n",
    "for level in level_dict:\n",
    "    header+= \"& L{}\".format(level)\n",
    "header+=\"\\\\\\ \\midrule \\n\"\n",
    "print(header+latex_ops[building_table][:-1]+\"\\\\bottomrule \\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## getting metrics for failed query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "failing_query = q1[useful_queries[4]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SELECT * WHERE {\n",
      "    ?equip        rdf:type   brick:VAV .\n",
      "    ?equip        brick:isFedBy+                 ?ahu .\n",
      "    ?vlv          rdf:type                    brick:Valve .\n",
      "    ?ahu          brick:hasPoint                 ?upstream_ta .\n",
      "    ?equip        brick:hasPoint                 ?dnstream_ta .\n",
      "    ?upstream_ta  rdf:type   brick:Supply_Air_Temperature_Sensor .\n",
      "    ?dnstream_ta  rdf:type   brick:Supply_Air_Temperature_Sensor .\n",
      "    ?equip        brick:hasPoint                 ?vlv .\n",
      "    ?vlv          rdf:type   brick:Valve_Command\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "print(failing_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_level = 5\n",
    "query_metrics = {}\n",
    "start_time = datetime.datetime.now()\n",
    "G = get_fast_relaxed_graph(query=failing_query, max_level=max_level)\n",
    "end_time = datetime.datetime.now()\n",
    "query_metrics[failing_query]={'graph': G}\n",
    "query_metrics[failing_query]['relaxation_time']  = (end_time-start_time).total_seconds()\n",
    "query_metrics[failing_query]['query_size'] = len(extract_triples(failing_query))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "level 0 done\n",
      "level 1 done\n",
      "level 2 done\n",
      "level 3 done\n",
      "level 4 done\n"
     ]
    }
   ],
   "source": [
    "\n",
    "buildings = brick_models\n",
    "# for query in q1[:-1]:\n",
    "relaxed_graph = query_metrics[failing_query]['graph']\n",
    "select_statement = failing_query.split(\"{\")[0] + \"{\\n\"\n",
    "\n",
    "level_building_nodes = {}\n",
    "max_edits = -1\n",
    "for level in range(max_level+1):\n",
    "\n",
    "    building_nodes_with_data = {}\n",
    "    for building_idx in range(len(buildings)):\n",
    "        building_model = buildings[building_idx]\n",
    "        nodes_with_data= []\n",
    "\n",
    "        for node_idx, node in relaxed_graph.nodes().data():\n",
    "            if node['level'] == level:\n",
    "\n",
    "                if max_edits == -1:\n",
    "                    max_edits = level\n",
    "                elif level>max_edits:\n",
    "                    max_edits = level\n",
    "\n",
    "                brick_query = generate_brick_query_from_node(node['query'], select_statement=select_statement)\n",
    "                res = run_brick_query(building_model=building_model, query=brick_query)\n",
    "\n",
    "                if len(res) > 0:\n",
    "                    nodes_with_data.append(node)\n",
    "        building_nodes_with_data[building_idx] = len(nodes_with_data)\n",
    "    level_building_nodes[level] = building_nodes_with_data\n",
    "    print(\"level {} done\".format(level))\n",
    "query_metrics[failing_query]['level_building_num_nodes_with_data'] = level_building_nodes\n",
    "query_metrics[failing_query]['size_relaxed_graph'] = len(relaxed_graph.nodes())\n",
    "query_metrics[failing_query]['max_edits'] = max_edits\n",
    "query_metrics[failing_query] = {\n",
    "    'level_building_num_nodes_with_data': level_building_nodes,\n",
    "    'size_relaxed_graph': len(relaxed_graph.nodes()),\n",
    "    'max_edits': max_edits,\n",
    "    'query_size': query_metrics[failing_query]['query_size'],\n",
    "    'relaxation_time': query_metrics[failing_query]['relaxation_time']\n",
    "}\n",
    "print(query_metrics[failing_query])\n",
    "#     with open(\"metrics_all_buildings.json\", 'w') as fp:\n",
    "#         json.dump(metrics, fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## next steps: remove metrics[list(metrics.keys())[11]] \n",
    "## add metrics[q1[11]] = query_metrics[failing_query]\n",
    "## write metrics to json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SELECT * WHERE { ?s ?p ?o }',\n",
       " 'SELECT ?point ?point_type WHERE {\\n    ?point rdf:type brick:Point .\\n    ?point rdf:type ?point_type \\n}',\n",
       " 'SELECT ?meter WHERE { ?meter rdf:type brick:Green_Button_Meter }',\n",
       " ' SELECT ?t WHERE { ?t rdf:type brick:Weather_Temperature_Sensor }',\n",
       " 'SELECT ?sensor WHERE {\\n    ?sensor rdf:type brick:Zone_Air_Temperature_Sensor .\\n    ?sensor brick:isPointOf ?equip \\n}',\n",
       " 'SELECT ?sp WHERE {\\n    ?sp rdf:type brick:Zone_Air_Temperature_Setpoint .\\n    ?sp brick:isPointOf ?equip\\n}',\n",
       " 'SELECT ?meter WHERE { ?meter rdf:type brick:Building_Electric_Meter }',\n",
       " 'SELECT ?point WHERE { ?point rdf:type brick:Occupancy_Sensor }',\n",
       " 'SELECT ?tstat ?room ?zone ?state ?temp ?hsp ?csp WHERE {\\n            ?tstat brick:hasLocation ?room .\\n            ?zone brick:hasPart ?room .\\n            ?tstat brick:hasPoint ?state .\\n            ?tstat brick:hasPoint ?temp .\\n            ?tstat brick:hasPoint ?hsp .\\n            ?tstat brick:hasPoint ?csp .\\n            ?zone rdf:type brick:Zone .\\n            ?tstat rdf:type brick:Thermostat .\\n            ?state rdf:type brick:Thermostat_Status .\\n            ?temp  rdf:type brick:Temperature_Sensor  .\\n            ?hsp   rdf:type brick:Supply_Air_Temperature_Heating_Setpoint .\\n            ?csp   rdf:type brick:Supply_Air_Temperature_Cooling_Setpoint\\n        }\\n    ',\n",
       " 'SELECT ?sensor ?sp ?equip WHERE {\\n    ?sensor    rdf:type     brick:Air_Flow_Sensor .\\n    ?sp    rdf:type     brick:Air_Flow_Setpoint .\\n    ?sensor    brick:isPointOf ?equip .\\n    ?sp    brick:isPointOf ?equip\\n}',\n",
       " 'SELECT ?cooling_point ?heating_point ?ahu WHERE {\\n        ?cooling_point rdf:type brick:Cooling_Valve_Command .\\n        ?heating_point rdf:type brick:Heating_Valve_Command .\\n        ?ahu brick:hasPoint ?cooling_point .\\n        ?ahu brick:hasPoint ?heating_point\\n    }',\n",
       " 'SELECT * WHERE {\\n    ?equip        rdf:type   brick:VAV .\\n    ?equip        brick:isFedBy+                 ?ahu .\\n    ?vlv          rdf:type                    ?vlv_type .\\n    ?ahu          brick:hasPoint                 ?upstream_ta .\\n    ?equip        brick:hasPoint                 ?dnstream_ta .\\n    ?upstream_ta  rdf:type   brick:Supply_Air_Temperature_Sensor .\\n    ?dnstream_ta  rdf:type   brick:Supply_Air_Temperature_Sensor .\\n    ?equip        brick:hasPoint                 ?vlv .\\n    ?vlv          rdf:type   brick:Valve_Command\\n}',\n",
       " 'SELECT * WHERE {\\n        ?equip        rdf:type   brick:VAV .\\n        ?equip        brick:hasPoint                 ?air_flow .\\n        ?air_flow     rdf:type   brick:Supply_Air_Flow_Sensor\\n    }',\n",
       " 'SELECT * WHERE {\\n        ?vlv        rdf:type   brick:Valve_Command .\\n        ?vlv        rdf:type                    ?vlv_type .\\n        ?equip      brick:hasPoint                 ?vlv .\\n        ?equip      rdf:type   brick:Air_Handling_Unit .\\n        ?air_temps  rdf:type   brick:Supply_Air_Temperature_Sensor .\\n        ?equip      brick:hasPoint                 ?air_temps .\\n        ?air_temps  rdf:type                    ?temp_type\\n    }',\n",
       " 'SELECT * WHERE {\\n        ?vlv        rdf:type   brick:Valve_Command .\\n        ?vlv        rdf:type                    ?vlv_type .\\n        ?equip      brick:hasPoint                 ?vlv .\\n        ?equip      rdf:type   brick:Air_Handling_Unit .\\n        ?air_temps  rdf:type   brick:Return_Air_Temperature_Sensor .\\n        ?equip      brick:hasPoint                 ?air_temps .\\n        ?air_temps  rdf:type                    ?temp_type\\n    }',\n",
       " 'SELECT ?vav WHERE {\\n    ?vav rdf:type brick:VAV\\n}',\n",
       " 'SELECT ?sensor ?room WHERE {\\n    ?sensor rdf:type brick:Zone_Temperature_Sensor .\\n    ?room rdf:type brick:Room .\\n    ?vav rdf:type brick:VAV .\\n    ?zone rdf:type brick:HVAC_Zone .\\n    ?vav brick:feeds+ ?zone .\\n    ?zone brick:hasPart ?room .\\n    ?vav brick:hasPoint ?sensor\\n}']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(metrics.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'level_building_num_nodes_with_data': {'0': {'0': 1,\n",
       "   '1': 1,\n",
       "   '2': 0,\n",
       "   '3': 1,\n",
       "   '4': 1,\n",
       "   '5': 1,\n",
       "   '6': 1,\n",
       "   '7': 1,\n",
       "   '8': 0,\n",
       "   '9': 1,\n",
       "   '10': 0,\n",
       "   '11': 0,\n",
       "   '12': 1,\n",
       "   '13': 1,\n",
       "   '14': 1,\n",
       "   '15': 1,\n",
       "   '16': 1,\n",
       "   '17': 1,\n",
       "   '18': 1,\n",
       "   '19': 0,\n",
       "   '20': 1,\n",
       "   '21': 1,\n",
       "   '22': 1,\n",
       "   '23': 1,\n",
       "   '24': 0,\n",
       "   '25': 1,\n",
       "   '26': 0,\n",
       "   '27': 1,\n",
       "   '28': 1,\n",
       "   '29': 1,\n",
       "   '30': 1,\n",
       "   '31': 1,\n",
       "   '32': 1,\n",
       "   '33': 0,\n",
       "   '34': 1,\n",
       "   '35': 1,\n",
       "   '36': 1,\n",
       "   '37': 1,\n",
       "   '38': 1,\n",
       "   '39': 1,\n",
       "   '40': 0,\n",
       "   '41': 1,\n",
       "   '42': 1,\n",
       "   '43': 0,\n",
       "   '44': 1,\n",
       "   '45': 0,\n",
       "   '46': 1,\n",
       "   '47': 1,\n",
       "   '48': 1,\n",
       "   '49': 1},\n",
       "  '1': {'0': 0,\n",
       "   '1': 0,\n",
       "   '2': 0,\n",
       "   '3': 0,\n",
       "   '4': 0,\n",
       "   '5': 0,\n",
       "   '6': 0,\n",
       "   '7': 0,\n",
       "   '8': 0,\n",
       "   '9': 0,\n",
       "   '10': 0,\n",
       "   '11': 0,\n",
       "   '12': 0,\n",
       "   '13': 0,\n",
       "   '14': 0,\n",
       "   '15': 0,\n",
       "   '16': 0,\n",
       "   '17': 0,\n",
       "   '18': 0,\n",
       "   '19': 0,\n",
       "   '20': 0,\n",
       "   '21': 0,\n",
       "   '22': 0,\n",
       "   '23': 0,\n",
       "   '24': 0,\n",
       "   '25': 0,\n",
       "   '26': 0,\n",
       "   '27': 0,\n",
       "   '28': 0,\n",
       "   '29': 0,\n",
       "   '30': 0,\n",
       "   '31': 0,\n",
       "   '32': 0,\n",
       "   '33': 0,\n",
       "   '34': 0,\n",
       "   '35': 0,\n",
       "   '36': 0,\n",
       "   '37': 0,\n",
       "   '38': 0,\n",
       "   '39': 0,\n",
       "   '40': 0,\n",
       "   '41': 0,\n",
       "   '42': 0,\n",
       "   '43': 0,\n",
       "   '44': 0,\n",
       "   '45': 0,\n",
       "   '46': 0,\n",
       "   '47': 0,\n",
       "   '48': 0,\n",
       "   '49': 0},\n",
       "  '2': {'0': 0,\n",
       "   '1': 0,\n",
       "   '2': 0,\n",
       "   '3': 0,\n",
       "   '4': 0,\n",
       "   '5': 0,\n",
       "   '6': 0,\n",
       "   '7': 0,\n",
       "   '8': 0,\n",
       "   '9': 0,\n",
       "   '10': 0,\n",
       "   '11': 0,\n",
       "   '12': 0,\n",
       "   '13': 0,\n",
       "   '14': 0,\n",
       "   '15': 0,\n",
       "   '16': 0,\n",
       "   '17': 0,\n",
       "   '18': 0,\n",
       "   '19': 0,\n",
       "   '20': 0,\n",
       "   '21': 0,\n",
       "   '22': 0,\n",
       "   '23': 0,\n",
       "   '24': 0,\n",
       "   '25': 0,\n",
       "   '26': 0,\n",
       "   '27': 0,\n",
       "   '28': 0,\n",
       "   '29': 0,\n",
       "   '30': 0,\n",
       "   '31': 0,\n",
       "   '32': 0,\n",
       "   '33': 0,\n",
       "   '34': 0,\n",
       "   '35': 0,\n",
       "   '36': 0,\n",
       "   '37': 0,\n",
       "   '38': 0,\n",
       "   '39': 0,\n",
       "   '40': 0,\n",
       "   '41': 0,\n",
       "   '42': 0,\n",
       "   '43': 0,\n",
       "   '44': 0,\n",
       "   '45': 0,\n",
       "   '46': 0,\n",
       "   '47': 0,\n",
       "   '48': 0,\n",
       "   '49': 0},\n",
       "  '3': {'0': 0,\n",
       "   '1': 0,\n",
       "   '2': 0,\n",
       "   '3': 0,\n",
       "   '4': 0,\n",
       "   '5': 0,\n",
       "   '6': 0,\n",
       "   '7': 0,\n",
       "   '8': 0,\n",
       "   '9': 0,\n",
       "   '10': 0,\n",
       "   '11': 0,\n",
       "   '12': 0,\n",
       "   '13': 0,\n",
       "   '14': 0,\n",
       "   '15': 0,\n",
       "   '16': 0,\n",
       "   '17': 0,\n",
       "   '18': 0,\n",
       "   '19': 0,\n",
       "   '20': 0,\n",
       "   '21': 0,\n",
       "   '22': 0,\n",
       "   '23': 0,\n",
       "   '24': 0,\n",
       "   '25': 0,\n",
       "   '26': 0,\n",
       "   '27': 0,\n",
       "   '28': 0,\n",
       "   '29': 0,\n",
       "   '30': 0,\n",
       "   '31': 0,\n",
       "   '32': 0,\n",
       "   '33': 0,\n",
       "   '34': 0,\n",
       "   '35': 0,\n",
       "   '36': 0,\n",
       "   '37': 0,\n",
       "   '38': 0,\n",
       "   '39': 0,\n",
       "   '40': 0,\n",
       "   '41': 0,\n",
       "   '42': 0,\n",
       "   '43': 0,\n",
       "   '44': 0,\n",
       "   '45': 0,\n",
       "   '46': 0,\n",
       "   '47': 0,\n",
       "   '48': 0,\n",
       "   '49': 0},\n",
       "  '4': {'0': 0,\n",
       "   '1': 0,\n",
       "   '2': 0,\n",
       "   '3': 0,\n",
       "   '4': 0,\n",
       "   '5': 0,\n",
       "   '6': 0,\n",
       "   '7': 0,\n",
       "   '8': 0,\n",
       "   '9': 0,\n",
       "   '10': 0,\n",
       "   '11': 0,\n",
       "   '12': 0,\n",
       "   '13': 0,\n",
       "   '14': 0,\n",
       "   '15': 0,\n",
       "   '16': 0,\n",
       "   '17': 0,\n",
       "   '18': 0,\n",
       "   '19': 0,\n",
       "   '20': 0,\n",
       "   '21': 0,\n",
       "   '22': 0,\n",
       "   '23': 0,\n",
       "   '24': 0,\n",
       "   '25': 0,\n",
       "   '26': 0,\n",
       "   '27': 0,\n",
       "   '28': 0,\n",
       "   '29': 0,\n",
       "   '30': 0,\n",
       "   '31': 0,\n",
       "   '32': 0,\n",
       "   '33': 0,\n",
       "   '34': 0,\n",
       "   '35': 0,\n",
       "   '36': 0,\n",
       "   '37': 0,\n",
       "   '38': 0,\n",
       "   '39': 0,\n",
       "   '40': 0,\n",
       "   '41': 0,\n",
       "   '42': 0,\n",
       "   '43': 0,\n",
       "   '44': 0,\n",
       "   '45': 0,\n",
       "   '46': 0,\n",
       "   '47': 0,\n",
       "   '48': 0,\n",
       "   '49': 0},\n",
       "  '5': {'0': 0,\n",
       "   '1': 0,\n",
       "   '2': 0,\n",
       "   '3': 0,\n",
       "   '4': 0,\n",
       "   '5': 0,\n",
       "   '6': 0,\n",
       "   '7': 0,\n",
       "   '8': 0,\n",
       "   '9': 0,\n",
       "   '10': 0,\n",
       "   '11': 0,\n",
       "   '12': 0,\n",
       "   '13': 0,\n",
       "   '14': 0,\n",
       "   '15': 0,\n",
       "   '16': 0,\n",
       "   '17': 0,\n",
       "   '18': 0,\n",
       "   '19': 0,\n",
       "   '20': 0,\n",
       "   '21': 0,\n",
       "   '22': 0,\n",
       "   '23': 0,\n",
       "   '24': 0,\n",
       "   '25': 0,\n",
       "   '26': 0,\n",
       "   '27': 0,\n",
       "   '28': 0,\n",
       "   '29': 0,\n",
       "   '30': 0,\n",
       "   '31': 0,\n",
       "   '32': 0,\n",
       "   '33': 0,\n",
       "   '34': 0,\n",
       "   '35': 0,\n",
       "   '36': 0,\n",
       "   '37': 0,\n",
       "   '38': 0,\n",
       "   '39': 0,\n",
       "   '40': 0,\n",
       "   '41': 0,\n",
       "   '42': 0,\n",
       "   '43': 0,\n",
       "   '44': 0,\n",
       "   '45': 0,\n",
       "   '46': 0,\n",
       "   '47': 0,\n",
       "   '48': 0,\n",
       "   '49': 0}},\n",
       " 'size_relaxed_graph': 1,\n",
       " 'max_edits': 0,\n",
       " 'query_size': 7,\n",
       " 'relaxation_time': 0.001142}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics[ 'SELECT * WHERE {\\n        ?vlv        rdf:type   brick:Valve_Command .\\n        ?vlv        rdf:type                    ?vlv_type .\\n        ?equip      brick:hasPoint                 ?vlv .\\n        ?equip      rdf:type   brick:Air_Handling_Unit .\\n        ?air_temps  rdf:type   brick:Supply_Air_Temperature_Sensor .\\n        ?equip      brick:hasPoint                 ?air_temps .\\n        ?air_temps  rdf:type                    ?temp_type\\n    }']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'graph': <networkx.classes.graph.Graph at 0x7fb299281940>,\n",
       " 'relaxation_time': 0.547987,\n",
       " 'query_size': 9,\n",
       " 'level_building_num_nodes_with_data': {0: {0: 0,\n",
       "   1: 0,\n",
       "   2: 0,\n",
       "   3: 0,\n",
       "   4: 0,\n",
       "   5: 0},\n",
       "  1: {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},\n",
       "  2: {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},\n",
       "  3: {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},\n",
       "  4: {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},\n",
       "  5: {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}},\n",
       " 'size_relaxed_graph': 3466,\n",
       " 'max_edits': 5}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query_metrics['SELECT * WHERE {\\n    ?equip        rdf:type   brick:VAV .\\n    ?equip        brick:isFedBy+                 ?ahu .\\n    ?vlv          rdf:type                    brick:Valve .\\n    ?ahu          brick:hasPoint                 ?upstream_ta .\\n    ?equip        brick:hasPoint                 ?dnstream_ta .\\n    ?upstream_ta  rdf:type   brick:Supply_Air_Temperature_Sensor .\\n    ?dnstream_ta  rdf:type   brick:Supply_Air_Temperature_Sensor .\\n    ?equip        brick:hasPoint                 ?vlv .\\n    ?vlv          rdf:type   brick:Valve_Command\\n}']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\toprule\n",
      "Query & Percentage of buildings that qualified before relaxation & Percentage of buildings that qualified after relaxation & Average number of edits to qualify \\\\ \\midrule\n",
      "Q0 & 62.0% & 100.0% & 0.52 \\\\ \n",
      "Q1 & 0.0% & 0.0% & N/A \\\\ \n",
      "Q2 & 2.0% & 98.0% & 3.36 \\\\ \n",
      "Q3 & 0.0% & 0.0% & N/A \\\\ \n",
      "Q4 & 6.0% & 0.0% & 0.0 \\\\ \n",
      "Q5 & 34.0% & 98.0% & 2.7 \\\\ \n",
      "Q6 & 60.0% & 100.0% & 0.8 \\\\ \n",
      "\\bottomrule\n"
     ]
    }
   ],
   "source": [
    "max_level = 5\n",
    "print(\"\\\\toprule\")\n",
    "print(\"Query & Percentage of buildings that qualified before relaxation & Percentage of buildings that qualified after relaxation & Average number of edits to qualify \\\\\\ \\midrule\")\n",
    "# % of buildings that qualify during relaxation\n",
    "for q_idx in range(len(useful_queries)):\n",
    "    query = q1[useful_queries[q_idx]]\n",
    "    qualified_building_list = []\n",
    "    count = 0\n",
    "    found = False\n",
    "    building_first_level_qualified = {}\n",
    "    for level in range(0, max_level+1):\n",
    "        for building in metrics.get(query).get('level_building_num_nodes_with_data').get(str(level)):\n",
    "            num_nodes_not_null_results = metrics.get(query).get('level_building_num_nodes_with_data').get(str(level)).get(building)\n",
    "            if num_nodes_not_null_results != 0:\n",
    "                found = True\n",
    "                if level == 0:\n",
    "                    count+=1\n",
    "                else:\n",
    "                    if building not in qualified_building_list:\n",
    "                        qualified_building_list.append(building)\n",
    "                if building not in building_first_level_qualified:\n",
    "                    building_first_level_qualified[building] = level\n",
    "                    \n",
    "    if not found:\n",
    "        print(\"Q{0} & {1}% & {2}% & {3} \\\\\\ \".format(q_idx, count/50*100, len(qualified_building_list)/50*100, \"N/A\"))\n",
    "    else:\n",
    "        print(\"Q{0} & {1}% & {2}% & {3} \\\\\\ \".format(q_idx, count/50*100, len(qualified_building_list)/50*100, sum(building_first_level_qualified.values())/50))\n",
    "print(\"\\\\bottomrule\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_metrics = pd.read_csv('similarity_metrics.csv')\n",
    "q1 = \"\"\"SELECT * WHERE {\n",
    " ?sen   rdf:type   brick:Supply_Air_Temperature_Sensor .\n",
    " ?sp    rdf:type   brick:Supply_Air_Temperature_Setpoint .\n",
    " ?ahu   rdf:type   brick:AHU .\n",
    " ?ahu   brick:feeds ?thing .\n",
    " ?thing  brick:hasPoint ?sen .\n",
    " ?thing  brick:hasPoint ?sp \n",
    "}\"\"\"\n",
    "\n",
    "file=\"Data - Brick models/acad.ttl\"\n",
    "\n",
    "g = brickschema.Graph(load_brick=True)\n",
    "g.load_file(file)\n",
    "g.expand(profile=\"owlrl\")\n",
    "select_statement = query.split(\"{\")[0] + \"{\\n\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_level=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-07-23 08:32:25.028384\n"
     ]
    }
   ],
   "source": [
    "relaxation_start_time = datetime.datetime.now()\n",
    "print(relaxation_start_time)\n",
    "G = get_fast_relaxed_graph(query=q1, max_level=5)\n",
    "relaxation_end_time = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1660\n"
     ]
    }
   ],
   "source": [
    "print(len(G.nodes()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1920"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_instances =  get_total_num_instances(g, 'brick:Equipment') + get_total_num_instances(g, 'brick:Location') + get_total_num_instances(g, 'brick:Point')\n",
    "total_instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_uuid = list(G.nodes())[0]\n",
    "original_query = G.nodes()[original_uuid]['query']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_id = 0\n",
    "sim_scores = []\n",
    "sim_score_dict = {}\n",
    "sim_score_dict2 = {}\n",
    "for idx, node in G.nodes().data():\n",
    "    relaxed_triple = node['query']\n",
    "    ss = sim_score(original_query, relaxed_triple, g, total_instances)\n",
    "    sim_scores.append(ss)\n",
    "    level = node['level']\n",
    "    levels.append(level)\n",
    "    sim_score_dict[node_id] = {'node': node, 'sim_score': ss, 'level': level}\n",
    "    if level > 0:\n",
    "        if ss in sim_score_dict2:\n",
    "            sim_score_dict2[ss].append(node)\n",
    "        else:\n",
    "            sim_score_dict2[ss] = [node]\n",
    "    node_id+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([0, 1, 0.7681412256571158, 0.6617400035965336, 0.8193389185022003, 0.6293680010868559, 0.542189338876426, 0.7816289042500114, 0.6004013845196322, 0.6996116495131994, 0.5374005499410656, 0.8703953925243878, 0.6685865836199897, 0.5759754501794947, 0.71314881958023, 0.5477990083482832, 0.47191910243388513, 0.6803261969230958, 0.5225865987511512, 0.6089387562926757, 0.8691185533597107, 0.6676057908190676, 0.575130514626069, 0.7121026555599422, 0.5469954066355011, 0.6793281825259058, 0.8017951543953568, 0.6158919126231858, 0.6569419746625963, 0.5046242136029326, 0.7385963121445775, 0.5673462764765616, 0.6051607036022517, 0.6559101539170759])\n"
     ]
    }
   ],
   "source": [
    "print(sim_score_dict2.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1,\n",
       " 0.8703953925243878,\n",
       " 0.8691185533597107,\n",
       " 0.8193389185022003,\n",
       " 0.8017951543953568,\n",
       " 0.7816289042500114,\n",
       " 0.7681412256571158,\n",
       " 0.7385963121445775,\n",
       " 0.71314881958023,\n",
       " 0.7121026555599422,\n",
       " 0.6996116495131994,\n",
       " 0.6803261969230958,\n",
       " 0.6793281825259058,\n",
       " 0.6685865836199897,\n",
       " 0.6676057908190676,\n",
       " 0.6617400035965336,\n",
       " 0.6569419746625963,\n",
       " 0.6559101539170759,\n",
       " 0.6293680010868559,\n",
       " 0.6158919126231858,\n",
       " 0.6089387562926757,\n",
       " 0.6051607036022517,\n",
       " 0.6004013845196322,\n",
       " 0.5759754501794947,\n",
       " 0.575130514626069,\n",
       " 0.5673462764765616,\n",
       " 0.5477990083482832,\n",
       " 0.5469954066355011,\n",
       " 0.542189338876426,\n",
       " 0.5374005499410656,\n",
       " 0.5225865987511512,\n",
       " 0.5046242136029326,\n",
       " 0.47191910243388513,\n",
       " 0]"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(sim_score_dict2.keys(), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint', '?sen'], ['?thing', 'brick:hasPoint+', '?sp']), 'node_id': 19, 'level': 1} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint+', '?sen'], ['?thing', 'brick:hasPoint', '?sp']), 'node_id': 25, 'level': 1} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint+', '?sen'], ['?thing', 'brick:hasPoint+', '?sp']), 'node_id': 27, 'level': 2} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds+', '?thing'], ['?thing', 'brick:hasPoint', '?sen'], ['?thing', 'brick:hasPoint', '?sp']), 'node_id': 96, 'level': 1} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds+', '?thing'], ['?thing', 'brick:hasPoint', '?sen'], ['?thing', 'brick:hasPoint+', '?sp']), 'node_id': 98, 'level': 2} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds+', '?thing'], ['?thing', 'brick:hasPoint+', '?sen'], ['?thing', 'brick:hasPoint', '?sp']), 'node_id': 104, 'level': 2} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds+', '?thing'], ['?thing', 'brick:hasPoint+', '?sen'], ['?thing', 'brick:hasPoint+', '?sp']), 'node_id': 106, 'level': 3} 1\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint', '?sen'], ['?thing', 'brick:hasPoint', '?sp']), 'node_id': 916, 'level': 1} 0.8703953925243878\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint', '?sen'], ['?thing', 'brick:hasPoint+', '?sp']), 'node_id': 918, 'level': 2} 0.8703953925243878\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint+', '?sen'], ['?thing', 'brick:hasPoint', '?sp']), 'node_id': 924, 'level': 2} 0.8703953925243878\n",
      "{'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'], ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'], ['?ahu', 'rdf:type', 'brick:AHU'], ['?ahu', 'brick:feeds', '?thing'], ['?thing', 'brick:hasPoint+', '?sen'], ['?thing', 'brick:hasPoint+', '?sp']), 'node_id': 926, 'level': 3} 0.8703953925243878\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sp']),\n",
       "  'node_id': 19,\n",
       "  'level': 1},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint', '?sp']),\n",
       "  'node_id': 25,\n",
       "  'level': 1},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sp']),\n",
       "  'node_id': 27,\n",
       "  'level': 2},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds+', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint', '?sp']),\n",
       "  'node_id': 96,\n",
       "  'level': 1},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds+', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sp']),\n",
       "  'node_id': 98,\n",
       "  'level': 2},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds+', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint', '?sp']),\n",
       "  'node_id': 104,\n",
       "  'level': 2},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Supply_Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds+', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sp']),\n",
       "  'node_id': 106,\n",
       "  'level': 3},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint', '?sp']),\n",
       "  'node_id': 916,\n",
       "  'level': 1},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sp']),\n",
       "  'node_id': 918,\n",
       "  'level': 2},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint', '?sp']),\n",
       "  'node_id': 924,\n",
       "  'level': 2},\n",
       " {'query': (['?sen', 'rdf:type', 'brick:Air_Temperature_Sensor'],\n",
       "   ['?sp', 'rdf:type', 'brick:Supply_Air_Temperature_Setpoint'],\n",
       "   ['?ahu', 'rdf:type', 'brick:AHU'],\n",
       "   ['?ahu', 'brick:feeds', '?thing'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sen'],\n",
       "   ['?thing', 'brick:hasPoint+', '?sp']),\n",
       "  'node_id': 926,\n",
       "  'level': 3}]"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_nodes = []\n",
    "limit = 10\n",
    "for score in sorted(sim_score_dict2.keys(), reverse=True):\n",
    "    for node in sim_score_dict2[score]:\n",
    "        if len(top_nodes) <= limit:\n",
    "            print(node, score)\n",
    "            top_nodes.append(node)\n",
    "        else:\n",
    "            break\n",
    "            \n",
    "top_nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for node in top_nodes:\n",
    "    q = generate_brick_query_from_node(node['query'], select_statement)\n",
    "    results.append(run_brick_query(building_model=g, query=q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for res in results:\n",
    "    print(len(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "# files = os.listdir('Data - Brick models/')\n",
    "# brick_models = []\n",
    "\n",
    "# for file in files:\n",
    "#     if file.endswith(\".ttl\"):\n",
    "#         g = brickschema.Graph()\n",
    "#         g.load_file('Data - Brick models/'+file)\n",
    "#         brick_models.append(g)\n",
    "\n",
    "# with open(\"brick_models_without_reasoning.pkl\", 'wb') as fp:\n",
    "#     pickle.dump(brick_models, fp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "# files = os.listdir('Data - Brick models/')\n",
    "# brick_models_without_reasoning = []\n",
    "\n",
    "# for file in files:\n",
    "#     if file.endswith(\".ttl\"):\n",
    "#         g = brickschema.Graph()\n",
    "#         g.load_file('Data - Brick models/'+file)\n",
    "#         brick_models_without_reasoning.append(g)\n",
    "\n",
    "# with open(\"brick_models_without_reasoning.pkl\", 'wb') as fp:\n",
    "#     pickle.dump(brick_models_without_reasoning, fp)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir('Data - Brick models/')\n",
    "brick_models = []\n",
    "\n",
    "with open(\"brick_models.pkl\", 'rb') as fp:\n",
    "    brick_models = pickle.load(fp)\n",
    "    \n",
    "with open(\"brick_models_without_reasoning.pkl\", 'rb') as fp:\n",
    "    brick_models_without_reasoning = pickle.load(fp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "building_id = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: {'total_instances': 17, 'num_unique_classes': 15, 'num_triples': 35, 'num_relationships': 16}, 1: {'total_instances': 406, 'num_unique_classes': 37, 'num_triples': 853, 'num_relationships': 442}, 2: {'total_instances': 118, 'num_unique_classes': 15, 'num_triples': 254, 'num_relationships': 130}, 3: {'total_instances': 964, 'num_unique_classes': 33, 'num_triples': 2032, 'num_relationships': 1058}, 4: {'total_instances': 127, 'num_unique_classes': 28, 'num_triples': 269, 'num_relationships': 138}, 5: {'total_instances': 29, 'num_unique_classes': 19, 'num_triples': 60, 'num_relationships': 29}, 6: {'total_instances': 930, 'num_unique_classes': 36, 'num_triples': 1986, 'num_relationships': 1052}, 7: {'total_instances': 1920, 'num_unique_classes': 34, 'num_triples': 4072, 'num_relationships': 2136}, 8: {'total_instances': 27, 'num_unique_classes': 8, 'num_triples': 48, 'num_relationships': 21}, 9: {'total_instances': 95, 'num_unique_classes': 19, 'num_triples': 198, 'num_relationships': 94}, 10: {'total_instances': 9, 'num_unique_classes': 7, 'num_triples': 16, 'num_relationships': 7}, 11: {'total_instances': 364, 'num_unique_classes': 29, 'num_triples': 753, 'num_relationships': 388}, 12: {'total_instances': 130, 'num_unique_classes': 27, 'num_triples': 270, 'num_relationships': 136}, 13: {'total_instances': 47, 'num_unique_classes': 25, 'num_triples': 95, 'num_relationships': 45}, 14: {'total_instances': 37, 'num_unique_classes': 17, 'num_triples': 76, 'num_relationships': 36}, 15: {'total_instances': 241, 'num_unique_classes': 38, 'num_triples': 509, 'num_relationships': 266}, 16: {'total_instances': 67, 'num_unique_classes': 26, 'num_triples': 142, 'num_relationships': 67}, 17: {'total_instances': 14, 'num_unique_classes': 13, 'num_triples': 29, 'num_relationships': 13}, 18: {'total_instances': 2564, 'num_unique_classes': 36, 'num_triples': 5533, 'num_relationships': 2949}, 19: {'total_instances': 8, 'num_unique_classes': 9, 'num_triples': 14, 'num_relationships': 5}, 20: {'total_instances': 138, 'num_unique_classes': 29, 'num_triples': 291, 'num_relationships': 146}, 21: {'total_instances': 41, 'num_unique_classes': 23, 'num_triples': 85, 'num_relationships': 40}, 22: {'total_instances': 1056, 'num_unique_classes': 27, 'num_triples': 2225, 'num_relationships': 1155}, 23: {'total_instances': 14, 'num_unique_classes': 14, 'num_triples': 28, 'num_relationships': 13}, 24: {'total_instances': 4, 'num_unique_classes': 5, 'num_triples': 8, 'num_relationships': 3}, 25: {'total_instances': 271, 'num_unique_classes': 34, 'num_triples': 586, 'num_relationships': 312}, 26: {'total_instances': 25, 'num_unique_classes': 11, 'num_triples': 55, 'num_relationships': 24}, 27: {'total_instances': 183, 'num_unique_classes': 20, 'num_triples': 382, 'num_relationships': 197}, 28: {'total_instances': 164, 'num_unique_classes': 25, 'num_triples': 350, 'num_relationships': 183}, 29: {'total_instances': 239, 'num_unique_classes': 32, 'num_triples': 498, 'num_relationships': 247}, 30: {'total_instances': 96, 'num_unique_classes': 18, 'num_triples': 222, 'num_relationships': 94}, 31: {'total_instances': 221, 'num_unique_classes': 35, 'num_triples': 462, 'num_relationships': 239}, 32: {'total_instances': 929, 'num_unique_classes': 30, 'num_triples': 2007, 'num_relationships': 1068}, 33: {'total_instances': 13, 'num_unique_classes': 11, 'num_triples': 25, 'num_relationships': 12}, 34: {'total_instances': 919, 'num_unique_classes': 31, 'num_triples': 1962, 'num_relationships': 1031}, 35: {'total_instances': 1412, 'num_unique_classes': 39, 'num_triples': 2953, 'num_relationships': 1537}, 36: {'total_instances': 1053, 'num_unique_classes': 33, 'num_triples': 2255, 'num_relationships': 1186}, 37: {'total_instances': 52, 'num_unique_classes': 24, 'num_triples': 108, 'num_relationships': 50}, 38: {'total_instances': 66, 'num_unique_classes': 23, 'num_triples': 141, 'num_relationships': 65}, 39: {'total_instances': 478, 'num_unique_classes': 23, 'num_triples': 1004, 'num_relationships': 525}, 40: {'total_instances': 238, 'num_unique_classes': 17, 'num_triples': 515, 'num_relationships': 277}, 41: {'total_instances': 342, 'num_unique_classes': 37, 'num_triples': 719, 'num_relationships': 371}, 42: {'total_instances': 3870, 'num_unique_classes': 42, 'num_triples': 8030, 'num_relationships': 4147}, 43: {'total_instances': 3, 'num_unique_classes': 3, 'num_triples': 5, 'num_relationships': 2}, 44: {'total_instances': 168, 'num_unique_classes': 33, 'num_triples': 353, 'num_relationships': 183}, 45: {'total_instances': 3, 'num_unique_classes': 3, 'num_triples': 5, 'num_relationships': 2}, 46: {'total_instances': 70, 'num_unique_classes': 16, 'num_triples': 146, 'num_relationships': 69}, 47: {'total_instances': 19, 'num_unique_classes': 9, 'num_triples': 43, 'num_relationships': 18}, 48: {'total_instances': 607, 'num_unique_classes': 37, 'num_triples': 1301, 'num_relationships': 690}, 49: {'total_instances': 265, 'num_unique_classes': 32, 'num_triples': 553, 'num_relationships': 286}}\n"
     ]
    }
   ],
   "source": [
    "building_data = {}\n",
    "\n",
    "for building_id in range(50):\n",
    "    total_instances =  get_total_num_instances(brick_models[building_id], 'brick:Equipment') + get_total_num_instances(brick_models[building_id], 'brick:Location') + get_total_num_instances(brick_models[building_id], 'brick:Point')\n",
    "    g = brick_models_without_reasoning[building_id]\n",
    "    triple_count = 0\n",
    "    unique_classes = []\n",
    "    relationship_count = 0\n",
    "    for triple in list(g.triples((None, None, None))):\n",
    "        if isinstance(triple[2], URIRef):\n",
    "            triple_count+=1\n",
    "            if triple[1] == RDF['type'] or triple[1] == 'a':\n",
    "                if triple[2] not in unique_classes:\n",
    "                    unique_classes.append(triple[2])\n",
    "            else:\n",
    "                relationship_count+=1\n",
    "\n",
    "    building_data[building_id] = {'total_instances': total_instances, 'num_unique_classes': len(unique_classes), 'num_triples': triple_count, 'num_relationships': relationship_count}\n",
    "\n",
    "print(building_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>building_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_instances</th>\n",
       "      <td>17</td>\n",
       "      <td>406</td>\n",
       "      <td>118</td>\n",
       "      <td>964</td>\n",
       "      <td>127</td>\n",
       "      <td>29</td>\n",
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       "      <td>1920</td>\n",
       "      <td>27</td>\n",
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       "      <td>238</td>\n",
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       "      <td>168</td>\n",
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       "      <td>70</td>\n",
       "      <td>19</td>\n",
       "      <td>607</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_unique_classes</th>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "      <td>15</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>8</td>\n",
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       "      <td>42</td>\n",
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       "      <td>16</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_triples</th>\n",
       "      <td>35</td>\n",
       "      <td>853</td>\n",
       "      <td>254</td>\n",
       "      <td>2032</td>\n",
       "      <td>269</td>\n",
       "      <td>60</td>\n",
       "      <td>1986</td>\n",
       "      <td>4072</td>\n",
       "      <td>48</td>\n",
       "      <td>198</td>\n",
       "      <td>...</td>\n",
       "      <td>515</td>\n",
       "      <td>719</td>\n",
       "      <td>8030</td>\n",
       "      <td>5</td>\n",
       "      <td>353</td>\n",
       "      <td>5</td>\n",
       "      <td>146</td>\n",
       "      <td>43</td>\n",
       "      <td>1301</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_relationships</th>\n",
       "      <td>16</td>\n",
       "      <td>442</td>\n",
       "      <td>130</td>\n",
       "      <td>1058</td>\n",
       "      <td>138</td>\n",
       "      <td>29</td>\n",
       "      <td>1052</td>\n",
       "      <td>2136</td>\n",
       "      <td>21</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>277</td>\n",
       "      <td>371</td>\n",
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       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
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       "      <td>690</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0    1    2     3    4   5     6     7   8    9   ...  \\\n",
       "building_id                                                           ...   \n",
       "total_instances     17  406  118   964  127  29   930  1920  27   95  ...   \n",
       "num_unique_classes  15   37   15    33   28  19    36    34   8   19  ...   \n",
       "num_triples         35  853  254  2032  269  60  1986  4072  48  198  ...   \n",
       "num_relationships   16  442  130  1058  138  29  1052  2136  21   94  ...   \n",
       "\n",
       "                     40   41    42  43   44  45   46  47    48   49  \n",
       "building_id                                                          \n",
       "total_instances     238  342  3870   3  168   3   70  19   607  265  \n",
       "num_unique_classes   17   37    42   3   33   3   16   9    37   32  \n",
       "num_triples         515  719  8030   5  353   5  146  43  1301  553  \n",
       "num_relationships   277  371  4147   2  183   2   69  18   690  286  \n",
       "\n",
       "[4 rows x 50 columns]"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(building_data)\n",
    "df.index.name = 'building_id'\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_instances</th>\n",
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       "      <td>265</td>\n",
       "      <td>3</td>\n",
       "      <td>3870</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_unique_classes</th>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
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       "      <td>33</td>\n",
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       "      <td>8</td>\n",
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       "      <td>33</td>\n",
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       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_triples</th>\n",
       "      <td>35</td>\n",
       "      <td>853</td>\n",
       "      <td>254</td>\n",
       "      <td>2032</td>\n",
       "      <td>269</td>\n",
       "      <td>60</td>\n",
       "      <td>1986</td>\n",
       "      <td>4072</td>\n",
       "      <td>48</td>\n",
       "      <td>198</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>353</td>\n",
       "      <td>5</td>\n",
       "      <td>146</td>\n",
       "      <td>43</td>\n",
       "      <td>1301</td>\n",
       "      <td>553</td>\n",
       "      <td>5</td>\n",
       "      <td>8030</td>\n",
       "      <td>1011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_relationships</th>\n",
       "      <td>16</td>\n",
       "      <td>442</td>\n",
       "      <td>130</td>\n",
       "      <td>1058</td>\n",
       "      <td>138</td>\n",
       "      <td>29</td>\n",
       "      <td>1052</td>\n",
       "      <td>2136</td>\n",
       "      <td>21</td>\n",
       "      <td>94</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>2</td>\n",
       "      <td>69</td>\n",
       "      <td>18</td>\n",
       "      <td>690</td>\n",
       "      <td>286</td>\n",
       "      <td>2</td>\n",
       "      <td>4147</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0    1    2     3    4   5     6     7   8    9  ...  43  \\\n",
       "building_id                                                           ...       \n",
       "total_instances     17  406  118   964  127  29   930  1920  27   95  ...   3   \n",
       "num_unique_classes  15   37   15    33   28  19    36    34   8   19  ...   3   \n",
       "num_triples         35  853  254  2032  269  60  1986  4072  48  198  ...   5   \n",
       "num_relationships   16  442  130  1058  138  29  1052  2136  21   94  ...   2   \n",
       "\n",
       "                     44  45   46  47    48   49  min   max  mean  \n",
       "building_id                                                       \n",
       "total_instances     168   3   70  19   607  265    3  3870   480  \n",
       "num_unique_classes   33   3   16   9    37   32    3    42    24  \n",
       "num_triples         353   5  146  43  1301  553    5  8030  1011  \n",
       "num_relationships   183   2   69  18   690  286    2  4147   526  \n",
       "\n",
       "[4 rows x 53 columns]"
      ]
     },
     "execution_count": 454,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['min'] = df.min(axis=1).astype(int)\n",
    "df['max'] = df.max(axis=1).astype(int)\n",
    "df['mean'] = round(df.mean(axis=1), 0).astype(int)\n",
    "df.to_csv('results/building_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric & Minimum & Mean & Maximum \\\\ \\midrule\n",
      "Total number of instances defined & 3 & 480 & 3870 \\\\ \n",
      "Number of unique classes & 3 & 24 & 42 \\\\ \n",
      "Number of triples & 5 & 1011 & 8030 \\\\ \n",
      "Number of relationships & 2 & 526 & 4147 \\\\ \n",
      "\\bottomrule\n"
     ]
    }
   ],
   "source": [
    "row_labels = ['Total number of instances defined', 'Number of unique classes', 'Number of triples', 'Number of relationships']\n",
    "print(\"Metric & Min & Mean & Max \\\\\\ \\midrule\")\n",
    "for i, row in df[['min', 'mean', 'max']].reset_index().iterrows():\n",
    "    print(\"{0} & {1} & {2} & {3} \\\\\\ \".format(row_labels[i], row['min'], row['mean'], row['max']))\n",
    "print(\"\\\\bottomrule\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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