Queries Generated Per Level.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 10 7\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import brickschema as bs\n",
    "import pymortar\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "from relaxation_graphs.full_relaxation_graph import get_relaxed_graph\n",
    "from utils import generate_brick_query_from_node, extract_triples\n",
    "\n",
    "\n",
    "from queries.mortar import queries as q1\n",
    "from queries.cbe import queries as q2\n",
    "from queries.nrel import queries as q3\n",
    "print(len(q1), len(q2), len(q3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pymortar.Client(\"https://beta-api.mortardata.org\")\n",
    "def get_non_zero(q):\n",
    "    \"\"\"Returns the # of buildings that return results on the given query\"\"\"\n",
    "    print(q)\n",
    "    df = c.qualify([q]).df\n",
    "    return df[df > 0].dropna().count().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query_bank = q1 + q2 + q3\n",
    "max_level = 5\n",
    "\n",
    "rows = []\n",
    "\n",
    "for query in query_bank:\n",
    "    relaxed_queries = get_relaxed_graph(query=query, max_level=max_level)\n",
    "    for level in range(max_level+1):\n",
    "        queries = []\n",
    "        data = relaxed_queries.nodes().data()\n",
    "        for node in relaxed_queries.nodes():\n",
    "            if data[node]['level'] == level:\n",
    "                brick_query = generate_brick_query_from_node(data[node]['query'], select_statement=\"SELECT * WHERE {\")\n",
    "                queries.append(brick_query)\n",
    "#         num_bldgs = sum([get_non_zero(q) for q in queries])\n",
    "#         print('level', level, 'has', len(queries), 'queries')\n",
    "        rows.append({'level': level, 'num_queries': len(queries), 'size': len(extract_triples(query))})\n",
    "\n",
    "rowdf = pd.DataFrame.from_records(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level</th>\n",
       "      <th>num_queries</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   level  num_queries  size\n",
       "0      0            1     2\n",
       "1      1            1     2\n",
       "2      2            0     2\n",
       "3      3            0     2\n",
       "4      4            0     2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rowdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rowdf.to_csv(\"queries_generated_per_level.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='level', ylabel='num_queries'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rowdf.plot.scatter(x='level', y='num_queries', logy=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'num_queries'}, xlabel='level'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rowdf.boxplot(column='num_queries', by='level')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'num_queries'}, xlabel='size'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rowdf.boxplot(column='num_queries', by='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}