gold_query_writing_tool.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gold Query Writing Helper\n",
    "\n",
    "The purpose of this notebook is to provide analysis of existing gold queries for a given database. This analysis will include the identifier coverage (amount of tables and columns covered by queries), and the clause types within the queries.\n",
    "\n",
    "In addition to database-specific statistics, as we perform this analysis for multiple databases, it will also include high-level statistics of other analyzed database queries to compare across databases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "go"
    }
   },
   "source": [
    "```\n",
    "Copyright 2024 Kyle Luoma\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports and Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import query_profiler as qp\n",
    "from load_nl_questions import load_nlq_into_df\n",
    "from db_util import *\n",
    "from ipytree import Tree, Node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_dict = {\n",
    "    \"database\": \"NorthernPlainsFireManagement\",\n",
    "    \"database_subname\": \"\", # Use for schemas that should be divided into smaller modules (e.g. the SBODemo database). Set to empty string if not applicable.\n",
    "    \"query_dir\": \"./queries/\",\n",
    "    \"load_label_df\": False, # Set to true if up-to-date stats excel file is available\n",
    "}\n",
    "if config_dict[\"database_subname\"] != \"\":\n",
    "    config_dict[\"full_database_name\"] = config_dict[\"database\"] + \"-\" + config_dict[\"database_subname\"]\n",
    "else:\n",
    "    config_dict[\"full_database_name\"] = config_dict[\"database\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Loading and Query Stats Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gold_queries = load_nlq_into_df(\n",
    "    \"{}.sql\".format(config_dict[\"full_database_name\"]), \n",
    "    config_dict[\"query_dir\"]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'table_list' not in locals():\n",
    "    table_list = None\n",
    "    if config_dict[\"database_subname\"] != \"\":\n",
    "        sbod_df = pd.read_excel('./schema-misc/SBODemoUS/SBOD_table_descriptions_combined_reduced.xlsx')\n",
    "        sbod_df = sbod_df[sbod_df.module == config_dict['database_subname']]\n",
    "        table_list = sbod_df.table.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if 'tables_and_columns_df' not in locals():\n",
    "    tables_and_columns = get_tables_and_columns_from_sql_server_db(\n",
    "        config_dict[\"database\"],\n",
    "        append_col_types=False\n",
    "        )\n",
    "    tables_and_columns_df = get_tables_and_columns_df(tables_and_columns)\n",
    "    if table_list != None:\n",
    "        tables_and_columns_df = tables_and_columns_df[tables_and_columns_df['table'].isin(table_list)]\n",
    "    tables_and_columns_df['table'] = tables_and_columns_df['table'].str.lower()\n",
    "    tables_and_columns_df['column'] = tables_and_columns_df['column'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "if config_dict['load_label_df'] == False:\n",
    "    profiler = qp.QueryProfiler()\n",
    "\n",
    "    df_list = []\n",
    "    already_processed = []\n",
    "\n",
    "    if 'label_df' in locals():\n",
    "        already_processed = label_df['query_num'].unique().tolist()\n",
    "\n",
    "    print(already_processed)\n",
    "\n",
    "    for row in gold_queries.itertuples():\n",
    "        if row.number not in already_processed:\n",
    "            new_label_df = profiler.get_identifiers_and_labels_df(row.query_gold, row.number, include_brackets=False)\n",
    "            df_list.append(new_label_df)\n",
    "\n",
    "    if 'label_df' in locals() and len(df_list) > 0:\n",
    "        new_label_df = pd.concat(df_list)\n",
    "        new_label_df = new_label_df.reset_index(drop=True)\n",
    "        new_label_df['stat_value'] = new_label_df['stat_value'].str.lower()\n",
    "        label_df = pd.concat([label_df, new_label_df])\n",
    "    elif len(df_list) > 0:\n",
    "        label_df = pd.concat(df_list)\n",
    "        label_df = label_df.reset_index(drop=True)\n",
    "        label_df['stat_value'] = label_df['stat_value'].str.lower()\n",
    "else:\n",
    "    label_df = pd.read_excel(\n",
    "        config_dict['query_dir'] + 'gold_stats/' + config_dict['database'] + '-stats.xlsx'\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'label_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mlabel_df\u001b[49m\u001b[38;5;241m.\u001b[39mquery_num\u001b[38;5;241m.\u001b[39munique()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'label_df' is not defined"
     ]
    }
   ],
   "source": [
    "label_df.query_num.unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_stats = {\n",
    "    \"database\": config_dict[\"full_database_name\"],\n",
    "}\n",
    "export_label_stats = label_df\n",
    "stat_cols = export_label_stats.columns.tolist()\n",
    "if 'database' in stat_cols:\n",
    "    stat_cols.remove('database')\n",
    "export_label_stats['database'] = config_dict[\"full_database_name\"]\n",
    "export_label_stats[['database'] + stat_cols].to_excel(\n",
    "    \"./queries/gold_stats/{}-stats.xlsx\".format(config_dict[\"full_database_name\"]), index=False\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Schema coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema_tables = tables_and_columns_df.table.unique()\n",
    "schema_tables = [x.lower() for x in schema_tables]\n",
    "schema_columns = tables_and_columns_df.column.unique()\n",
    "schema_columns = [x.lower() for x in schema_columns]\n",
    "\n",
    "query_tables = label_df.query(\"stat_key == 'table'\").stat_value.unique()\n",
    "query_columns = label_df.query(\"stat_key == 'column'\").stat_value.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table coverage: 0.7777777777777778\n",
      "Column coverage: 0.5174825174825175\n"
     ]
    }
   ],
   "source": [
    "table_coverage = len(set(query_tables).intersection(set(schema_tables))) / len(schema_tables)\n",
    "column_coverage = len(set(query_columns).intersection(set(schema_columns))) / len(schema_columns)\n",
    "print(\"Table coverage: {}\".format(table_coverage))\n",
    "print(\"Column coverage: {}\".format(column_coverage))\n",
    "export_stats['table_coverage'] = table_coverage\n",
    "export_stats['column_coverage'] = column_coverage"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unused tables and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unused columns by table:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0915ae36865c406b90b1be21a585a298",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tree(nodes=(Node(icon='table', icon_style='success', name='tbl_deadwood', nodes=(Node(icon='leaf', name='data_…"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unused_tables = set(schema_tables).difference(set(query_tables))\n",
    "unused_columns = list(set(schema_columns).difference(set(query_columns)))\n",
    "print(\"Unused columns by table:\")\n",
    "unused_tree = Tree()\n",
    "for table in schema_tables:\n",
    "    table_columns = tables_and_columns_df.query(\"table == @table\").column.unique()\n",
    "    if table in unused_tables:\n",
    "        table_style = \"danger\"\n",
    "    else:\n",
    "        table_style = \"success\"\n",
    "    unused_tree.add_node(\n",
    "        Node(\n",
    "            table, \n",
    "            [\n",
    "                Node(\n",
    "                    column, \n",
    "                    children=[],\n",
    "                    icon=\"leaf\"\n",
    "                ) for column in table_columns if column.lower() in unused_columns\n",
    "            ] + [\n",
    "                Node(\n",
    "                    column, \n",
    "                    children=[],\n",
    "                    icon=\"leaf\",\n",
    "                    icon_style=\"success\"\n",
    "                ) for column in table_columns if column.lower() not in unused_columns\n",
    "            ],\n",
    "            opened=False,\n",
    "            icon=\"table\",\n",
    "            icon_style=table_style\n",
    "        )\n",
    "    )\n",
    "unused_tree\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Query Clause Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x22f1b4d1490>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x900 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = label_df.query(\"stat_key != 'table' & stat_key != 'column'\")\n",
    "\n",
    "g = sns.FacetGrid(plot_df, col=\"stat_value\", col_wrap=6, \n",
    "                  sharex=True, sharey=True, hue=\"query_num\"\n",
    "                  )\n",
    "g.map(sns.histplot, \"query_num\", discrete=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a count of stats per query then wrangle the data into submission in \n",
    "# order to create a sorting column by count of stats per query\n",
    "# that we use as the x axis on the histogram below\n",
    "plot_df = label_df.query(\"stat_key != 'table' & stat_key != 'column'\")\n",
    "stat_counts = plot_df.groupby(['query_num']).count().reset_index()[['query_num', 'stat_key']]\n",
    "stat_counts.rename(columns={'stat_key': 'num_stats'}, inplace=True)\n",
    "stat_counts.sort_values(by=['num_stats'], inplace=True)\n",
    "stat_counts.reset_index(inplace=True, drop=True)\n",
    "stat_counts.reset_index(inplace=True, drop=False)\n",
    "stat_counts.rename(columns={'index': 'query_sort'}, inplace=True)\n",
    "stat_counts.set_index('query_num', inplace=True, drop=True)\n",
    "plot_df = plot_df.merge(stat_counts, on='query_num')\n",
    "plot_df.reset_index(inplace=True, drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1170x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(11.7, 6))\n",
    "hist_plot = sns.histplot(\n",
    "    plot_df,\n",
    "    x=\"query_sort\", \n",
    "    hue=\"stat_value\", \n",
    "    multiple=\"stack\", \n",
    "    discrete=True,\n",
    "    stat=\"count\",\n",
    "    hue_order=plot_df.stat_value.value_counts().index,\n",
    "    )\n",
    "sns.move_legend(ax, \"upper right\", bbox_to_anchor=(1.2, 1))\n",
    "fig = hist_plot.get_figure()\n",
    "fig.savefig(\"./queries/gold_stats/{}-stats-clause-counts.png\".format(config_dict[\"database\"]), dpi=300)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.12.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}