g-care-kyle.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## G-CARE Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ArgumentError",
     "evalue": "ArgumentError: Package Graphs [86223c79-3864-5bf0-83f7-82e725a168b6] is required but does not seem to be installed:\n - Run `Pkg.instantiate()` to install all recorded dependencies.\n",
     "output_type": "error",
     "traceback": [
      "ArgumentError: Package Graphs [86223c79-3864-5bf0-83f7-82e725a168b6] is required but does not seem to be installed:\n",
      " - Run `Pkg.instantiate()` to install all recorded dependencies.\n",
      "\n",
      "\n",
      "Stacktrace:\n",
      "  [1] _require(pkg::Base.PkgId, env::String)\n",
      "    @ Base ./loading.jl:1774\n",
      "  [2] _require_prelocked(uuidkey::Base.PkgId, env::String)\n",
      "    @ Base ./loading.jl:1660\n",
      "  [3] macro expansion\n",
      "    @ ./loading.jl:1648 [inlined]\n",
      "  [4] macro expansion\n",
      "    @ ./lock.jl:267 [inlined]\n",
      "  [5] require(into::Module, mod::Symbol)\n",
      "    @ Base ./loading.jl:1611\n",
      "  [6] eval\n",
      "    @ ./boot.jl:370 [inlined]\n",
      "  [7] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)\n",
      "    @ Base ./loading.jl:1903\n",
      "  [8] #invokelatest#2\n",
      "    @ ./essentials.jl:816 [inlined]\n",
      "  [9] invokelatest\n",
      "    @ ./essentials.jl:813 [inlined]\n",
      " [10] (::VSCodeServer.var\"#202#203\"{VSCodeServer.NotebookRunCellArguments, String})()\n",
      "    @ VSCodeServer ~/.vscode-server/extensions/julialang.language-julia-1.51.2/scripts/packages/VSCodeServer/src/serve_notebook.jl:19\n",
      " [11] withpath(f::VSCodeServer.var\"#202#203\"{VSCodeServer.NotebookRunCellArguments, String}, path::String)\n",
      "    @ VSCodeServer ~/.vscode-server/extensions/julialang.language-julia-1.51.2/scripts/packages/VSCodeServer/src/repl.jl:274\n",
      " [12] notebook_runcell_request(conn::VSCodeServer.JSONRPC.JSONRPCEndpoint{Base.PipeEndpoint, Base.PipeEndpoint}, params::VSCodeServer.NotebookRunCellArguments)\n",
      "    @ VSCodeServer ~/.vscode-server/extensions/julialang.language-julia-1.51.2/scripts/packages/VSCodeServer/src/serve_notebook.jl:13\n",
      " [13] dispatch_msg(x::VSCodeServer.JSONRPC.JSONRPCEndpoint{Base.PipeEndpoint, Base.PipeEndpoint}, dispatcher::VSCodeServer.JSONRPC.MsgDispatcher, msg::Dict{String, Any})\n",
      "    @ VSCodeServer.JSONRPC ~/.vscode-server/extensions/julialang.language-julia-1.51.2/scripts/packages/JSONRPC/src/typed.jl:67\n",
      " [14] serve_notebook(pipename::String, outputchannel_logger::Base.CoreLogging.SimpleLogger; crashreporting_pipename::String)\n",
      "    @ VSCodeServer ~/.vscode-server/extensions/julialang.language-julia-1.51.2/scripts/packages/VSCodeServer/src/serve_notebook.jl:139\n",
      " [15] top-level scope\n",
      "    @ ~/.vscode-server/extensions/julialang.language-julia-1.51.2/scripts/notebook/notebook.jl:32"
     ]
    }
   ],
   "source": [
    "using Graphs: DiGraph\n",
    "using StatsPlots\n",
    "include(\"Source/datasets.jl\")\n",
    "include(\"Source/QuasiStableCardinalityEstimator.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "aids_data_file_path = \"dataset/aids/aids.txt\"\n",
    "human_data_file_path = \"dataset/human/human.txt\"\n",
    "lubm80_data_file_path = \"dataset/lubm80/lubm80.txt\"\n",
    "yago_data_file_path = \"dataset/yago/yago.txt\"\n",
    "yeast_data_file_path = \"dataset/yeast/yeast.graph\"\n",
    "hprd_data_file_path = \"dataset/hprd/hprd.graph\"\n",
    "wordnet_data_file_path = \"dataset/wordnet/wordnet.graph\"\n",
    "dblp_data_file_path = \"dataset/dblp/dblp.graph\"\n",
    "\n",
    "human_data = load_dataset(human_data_file_path)\n",
    "aids_data = load_dataset(aids_data_file_path)\n",
    "yeast_data = load_dataset(yeast_data_file_path, subgraph_matching_data=true)\n",
    "hprd_data = load_dataset(hprd_data_file_path, subgraph_matching_data=true)\n",
    "wordnet_data = load_dataset(wordnet_data_file_path, subgraph_matching_data=true)\n",
    "dblp_data = load_dataset(dblp_data_file_path, subgraph_matching_data=true)\n",
    "#lubm80_data = load_dataset(lubm80_data_file_path)\n",
    "#yago_data = load_dataset(yago_data_file_path)\n",
    "nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Int64} with 5 entries:\n",
       "  \"yeast\"   => 6\n",
       "  \"wordnet\" => 3\n",
       "  \"hprd\"    => -1\n",
       "  \"aids\"    => 6\n",
       "  \"human\"   => -1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#datasets = [aids_data, human_data, lubm80_data, yago_data]\n",
    "#dataset_names = [\"aids\", \"human\", \"lubm80\", \"yago\"]\n",
    "datasets = Dict(\"aids\"=>aids_data, \"human\"=>human_data, \"yeast\"=>yeast_data, \"hprd\" => hprd_data, \"wordnet\" => wordnet_data)\n",
    "#dataset_names = [\"aids\", \"human\", \"yeast\"]\n",
    "dataset_names = [\"wordnet\"]\n",
    "num_sample_nodes = Dict(\"aids\"=>10000, \"human\"=>100, \"yeast\"=>50, \"hprd\"=>100, \"wordnet\"=>100)\n",
    "gcare_dataset = Dict(\"aids\"=>true, \"human\"=>true, \"yeast\"=>false, \"hprd\"=>false, \"wordnet\"=>false)\n",
    "max_cycle_size = Dict(\"aids\"=>6, \"human\"=>-1, \"yeast\"=>6, \"hprd\"=>-1, \"wordnet\"=>3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating Cycles of Size: 2\n",
      "Generating Cycles of Size: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n",
      "Generating Cycles of Size: 2\n",
      "Generating Cycles of Size: 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n",
      "Generating Cycles of Size: 2\n",
      "Generating Cycles of Size: 3\n",
      "Started coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished coloring\n",
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n",
      "Generating Cycles of Size: 2\n",
      "Generating Cycles of Size: 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n",
      "Generating Cycles of Size: 2\n",
      "Generating Cycles of Size: 3\n",
      "Started coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished coloring\n",
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n"
     ]
    }
   ],
   "source": [
    "build_time = Dict()\n",
    "summary_size = Dict()\n",
    "color_summaries = Dict()\n",
    "hash_color_summaries = Dict()\n",
    "no_color_summaries = Dict()\n",
    "degree_color_summaries = Dict()\n",
    "directed_degree_color_summaries = Dict()\n",
    "num_colors = 64\n",
    "for dataset in dataset_names\n",
    "    directed_degree_color_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], num_sample_nodes=num_sample_nodes[dataset], partitioner=\"DirectedDegree\")\n",
    "    degree_color_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], num_sample_nodes=num_sample_nodes[dataset], partitioner=\"Degree\")\n",
    "    results = @timed generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], num_sample_nodes=num_sample_nodes[dataset])\n",
    "    build_time[dataset] = results[2]\n",
    "    summary_size[dataset] = get_color_summary_size(results[1])\n",
    "    color_summaries[dataset] = results[1]\n",
    "    hash_color_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], num_sample_nodes=num_sample_nodes[dataset], partitioner=\"Hash\")\n",
    "    no_color_summaries[dataset] = generate_color_summary(datasets[dataset], 1, verbose=true, max_size = max_cycle_size[dataset], num_sample_nodes=num_sample_nodes[dataset])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------\n",
      "Running wordnet\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_107.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_116.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_125.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_134.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_143.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_152.graph\n",
      "Query: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_161.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_170.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_18.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_189.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_198.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_26.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_35.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_44.graph\n",
      "Query: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_53.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_62.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_71.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_80.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_9.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_dense_4_99.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_sparse_12_107.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_sparse_12_116.graph\n",
      "Query: /home/kylebd99/Cardinality-with-Colors/queryset/wordnet/query_sparse_12_125.graph\n"
     ]
    },
    {
     "ename": "InterruptException",
     "evalue": "InterruptException:",
     "output_type": "error",
     "traceback": [
      "InterruptException:\n",
      "\n",
      "Stacktrace:\n",
      "  [1] Array\n",
      "    @ ./boot.jl:459 [inlined]\n",
      "  [2] Array\n",
      "    @ ./boot.jl:468 [inlined]\n",
      "  [3] similar\n",
      "    @ ./abstractarray.jl:841 [inlined]\n",
      "  [4] similar\n",
      "    @ ./abstractarray.jl:840 [inlined]\n",
      "  [5] _array_for\n",
      "    @ ./array.jl:676 [inlined]\n",
      "  [6] _array_for\n",
      "    @ ./array.jl:679 [inlined]\n",
      "  [7] vect\n",
      "    @ ./array.jl:126 [inlined]\n",
      "  [8] get_cardinality_bounds(query::QueryGraph, summary::ColorSummary; use_partial_sums::Bool, verbose::Bool, usingStoredStats::Bool)\n",
      "    @ Main ~/Cardinality-with-Colors/Source/QuasiStableCardinalityEstimator.jl:951\n",
      "  [9] macro expansion\n",
      "    @ ./timing.jl:463 [inlined]\n",
      " [10] top-level scope\n",
      "    @ ~/Cardinality-with-Colors/g-care.ipynb:82"
     ]
    }
   ],
   "source": [
    "query_directories = Dict()\n",
    "query_directories[\"human\"] = [\"/queryset/human/Chain_3/\",\n",
    "                                \"/queryset/human/Graph_3/\",\n",
    "                                \"/queryset/human/Star_3/\",\n",
    "                                \"/queryset/human/Tree_3/\"]\n",
    "query_directories[\"aids\"] = [\"/queryset/aids/Chain_3/\",\n",
    "                                \"/queryset/aids/Chain_6/\",\n",
    "                                \"/queryset/aids/Chain_9/\",\n",
    "                                \"/queryset/aids/Chain_12/\",\n",
    "                                \"/queryset/aids/Cycle_3/\",\n",
    "                                \"/queryset/aids/Cycle_6/\",\n",
    "                                \"/queryset/aids/Flower_6/\",\n",
    "                                \"/queryset/aids/Flower_9/\",\n",
    "                                \"/queryset/aids/Flower_12/\",\n",
    "                                \"/queryset/aids/Graph_3/\",\n",
    "                                \"/queryset/aids/Graph_6/\",\n",
    "                                \"/queryset/aids/Graph_9/\",\n",
    "                                \"/queryset/aids/Graph_12/\",\n",
    "                                \"/queryset/aids/Petal_6/\",\n",
    "                                \"/queryset/aids/Petal_9/\",\n",
    "                                \"/queryset/aids/Petal_12/\",\n",
    "                                \"/queryset/aids/Star_3/\",\n",
    "                                \"/queryset/aids/Star_6/\",\n",
    "                                \"/queryset/aids/Star_9/\",\n",
    "                                \"/queryset/aids/Tree_3/\",\n",
    "                                \"/queryset/aids/Tree_6/\",\n",
    "                                \"/queryset/aids/Tree_9/\",\n",
    "                                \"/queryset/aids/Tree_12/\"]\n",
    "\n",
    "query_directories[\"yeast\"] = [\"/queryset/yeast\"]\n",
    "query_directories[\"hprd\"] = [\"/queryset/hprd\"]\n",
    "query_directories[\"wordnet\"] = [\"/queryset/wordnet\"]\n",
    "dataset_names = [\"wordnet\"]\n",
    "                                \n",
    "\n",
    "query_paths = Dict()\n",
    "for dataset in dataset_names\n",
    "    query_paths[dataset] = [readdir(pwd() * dir, join=true) for dir in query_directories[dataset]]\n",
    "    query_paths[dataset] = [(query_paths[dataset]...)...]\n",
    "end\n",
    "exact_sizes = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_stats = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_hash = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_no_color = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_degree = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_directed_degree = Dict(dataset => [] for dataset in dataset_names)\n",
    "query_types = Dict(dataset => [] for dataset in dataset_names)\n",
    "count = 0\n",
    "for dataset in dataset_names\n",
    "    println(\"---------------------------------------------------------------------------------------------\")\n",
    "    println(\"Running \", dataset)\n",
    "    for query_path in query_paths[dataset]\n",
    "        occursin(\"dense_32\", query_path) && continue\n",
    "        occursin(\"dense_24\", query_path) && continue\n",
    "        occursin(\"dense_20\", query_path) && continue\n",
    "        occursin(\"dense_16\", query_path) && continue\n",
    "        occursin(\"dense_12\", query_path) && continue\n",
    "        occursin(\"dense_8\", query_path) && continue\n",
    "        occursin(\"sparse_32\", query_path) && continue\n",
    "        occursin(\"sparse_24\", query_path) && continue\n",
    "        count += 1\n",
    "        count % 10 != 0 && continue\n",
    "        println(\"Query: \", query_path)\n",
    "        if gcare_dataset[dataset]\n",
    "            query_type = match(r\".*/queryset/.*/(.*)_.*/.*\", query_path).captures[1]\n",
    "            exact_size = load_true_cardinality(replace(query_path, \"queryset\"=>\"TrueCardinalities\"))\n",
    "            id_and_query = load_query(query_path)\n",
    "            id = id_and_query[1]\n",
    "            query = id_and_query[2]\n",
    "        else\n",
    "            query_type = match(r\".*/queryset/.*/query_(.*)_.*\", query_path).captures[1]\n",
    "            if isfile(replace(query_path, \"queryset\"=>\"TrueCardinalities\"))\n",
    "                exact_size = load_true_cardinality(replace(query_path, \"queryset\"=>\"TrueCardinalities\"))\n",
    "            else\n",
    "                continue\n",
    "            end\n",
    "            id_and_query = load_query(query_path, subgraph_matching_data=true)\n",
    "            id = id_and_query[1]\n",
    "            query = id_and_query[2]\n",
    "        end\n",
    "        # add sampling parameters here\n",
    "        bound_results = @timed get_cardinality_bounds(query, color_summaries[dataset], usingStoredStats=false)\n",
    "        bound_results_with_stats = get_cardinality_bounds(query, color_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results_with_hash = get_cardinality_bounds(query, hash_color_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results_with_no_color = get_cardinality_bounds(query, no_color_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results_with_degree = get_cardinality_bounds(query, degree_color_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results_with_directed_degree = get_cardinality_bounds(query, directed_degree_color_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results[1][2] = max(1, bound_results[1][2])\n",
    "        bound_results_with_stats[2] = max(1, bound_results_with_stats[2])\n",
    "        bound_results_with_hash[2] = max(1, bound_results_with_hash[2])\n",
    "        bound_results_with_no_color[2] = max(1, bound_results_with_no_color[2])\n",
    "        bound_results_with_degree[2] = max(1, bound_results_with_degree[2])\n",
    "        bound_results_with_directed_degree[2] = max(1, bound_results_with_directed_degree[2])\n",
    "        push!(exact_sizes[dataset], exact_size)\n",
    "        push!(bounds[dataset], bound_results[1])\n",
    "        push!(bounds_with_stats[dataset], bound_results_with_stats)\n",
    "        push!(bounds_with_hash[dataset], bound_results_with_hash)\n",
    "        push!(bounds_with_no_color[dataset], bound_results_with_no_color)\n",
    "        push!(bounds_with_degree[dataset], bound_results_with_degree)\n",
    "        push!(bounds_with_directed_degree[dataset], bound_results_with_directed_degree)\n",
    "        push!(query_types[dataset], query_type)\n",
    "    end\n",
    "end\n",
    "relative_errors = Dict(dataset => bounds[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_stats = Dict(dataset => bounds_with_stats[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_hash = Dict(dataset => bounds_with_hash[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_no_color = Dict(dataset => bounds_with_no_color[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_degree = Dict(dataset => bounds_with_degree[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_directed_degree = Dict(dataset => bounds_with_directed_degree[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Vector{Vector{Float64}}} with 1 entry:\n",
       "  \"wordnet\" => [[0.0, 0.0167066, 635.503], [0.0, 0.072889, 641.376], [0.0, 0.01…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "relative_errors = Dict(dataset => bounds[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_stats = Dict(dataset => bounds_with_stats[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_hash = Dict(dataset => bounds_with_hash[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_no_color = Dict(dataset => bounds_with_no_color[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_degree = Dict(dataset => bounds_with_degree[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_directed_degree = Dict(dataset => bounds_with_directed_degree[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum([x[2] != 1 for x in values(bounds)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum([x[2] != 1 for x in values(bounds)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exact_sizes[\"yeast\"][exact_sizes[\"yeast\"] .> 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_errors = vec([[x[2] for x in relative_errors_with_stats[\"human\"]]... [x[2] for x in relative_errors_with_hash[\"human\"]]...  [x[2] for x in relative_errors_with_no_color[\"human\"]]...  [x[2] for x in relative_errors_with_degree[\"human\"]]... [x[2] for x in relative_errors_with_directed_degree[\"human\"]]... ])\n",
    "grouped_query_types = vec([query_types[\"human\"]... query_types[\"human\"]... query_types[\"human\"]... query_types[\"human\"]... query_types[\"human\"]...])\n",
    "grouped_datasets = vec([[\"Quasi-Stable\" for _ in 1:length(query_types[\"human\"])]... [\"Hash\" for _ in 1:length(query_types[\"human\"])]... [\"No Partition\" for _ in 1:length(query_types[\"human\"])]... [\"Degree\" for _ in 1:length(query_types[\"human\"])]... [\"Directed Degree\" for _ in 1:length(query_types[\"human\"])]...])\n",
    "groupedboxplot(grouped_query_types, grouped_errors, group = grouped_datasets, yscale =:log10, ylims=[10^-4, 10^7], yticks=[10^-4, 10^-2, 1, 10^2, 10^4, 10^6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_errors = vec([[x[2] for x in relative_errors_with_stats[\"aids\"]]... [x[2] for x in relative_errors_with_hash[\"aids\"]]...  [x[2] for x in relative_errors_with_no_color[\"aids\"]]...  [x[2] for x in relative_errors_with_degree[\"aids\"]]... [x[2] for x in relative_errors_with_directed_degree[\"aids\"]]... ])\n",
    "grouped_query_types = vec([query_types[\"aids\"]... query_types[\"aids\"]... query_types[\"aids\"]... query_types[\"aids\"]... query_types[\"aids\"]...])\n",
    "grouped_datasets = vec([[\"Quasi-Stable\" for _ in 1:length(query_types[\"aids\"])]... [\"Hash\" for _ in 1:length(query_types[\"aids\"])]... [\"No Partition\" for _ in 1:length(query_types[\"aids\"])]... [\"Degree\" for _ in 1:length(query_types[\"aids\"])]... [\"Directed Degree\" for _ in 1:length(query_types[\"aids\"])]...])\n",
    "groupedboxplot(grouped_query_types, grouped_errors, group = grouped_datasets, yscale =:log10,  ylims=[10^-6, 10^11], yticks=[10^-5, 1, 10^5, 10^10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_errors = vec([[x[2] for x in relative_errors_with_stats[\"yeast\"]]... [x[2] for x in relative_errors_with_hash[\"yeast\"]]...  [x[2] for x in relative_errors_with_no_color[\"yeast\"]]...  [x[2] for x in relative_errors_with_degree[\"yeast\"]]... [x[2] for x in relative_errors_with_directed_degree[\"yeast\"]]... ])\n",
    "grouped_query_types = vec([query_types[\"yeast\"]... query_types[\"yeast\"]... query_types[\"yeast\"]... query_types[\"yeast\"]... query_types[\"yeast\"]...])\n",
    "grouped_datasets = vec([[\"Quasi-Stable\" for _ in 1:length(query_types[\"yeast\"])]... [\"Hash\" for _ in 1:length(query_types[\"yeast\"])]... [\"No Partition\" for _ in 1:length(query_types[\"yeast\"])]... [\"Degree\" for _ in 1:length(query_types[\"yeast\"])]... [\"Directed Degree\" for _ in 1:length(query_types[\"yeast\"])]...])\n",
    "groupedboxplot(grouped_query_types, grouped_errors, group = grouped_datasets, yscale =:log10,  ylims=[10^-6, 10^11], yticks=[10^-5, 1, 10^5, 10^10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_errors = vec([[x[2] for x in relative_errors_with_stats[\"hprd\"]]... [x[2] for x in relative_errors_with_hash[\"hprd\"]]...  [x[2] for x in relative_errors_with_no_color[\"hprd\"]]...  [x[2] for x in relative_errors_with_degree[\"hprd\"]]... [x[2] for x in relative_errors_with_directed_degree[\"hprd\"]]... ])\n",
    "grouped_query_types = vec([query_types[\"hprd\"]... query_types[\"hprd\"]... query_types[\"hprd\"]... query_types[\"hprd\"]... query_types[\"hprd\"]...])\n",
    "grouped_datasets = vec([[\"Quasi-Stable\" for _ in 1:length(query_types[\"hprd\"])]... [\"Hash\" for _ in 1:length(query_types[\"hprd\"])]... [\"No Partition\" for _ in 1:length(query_types[\"hprd\"])]... [\"Degree\" for _ in 1:length(query_types[\"hprd\"])]... [\"Directed Degree\" for _ in 1:length(query_types[\"hprd\"])]...])\n",
    "groupedboxplot(grouped_query_types, grouped_errors, group = grouped_datasets, yscale =:log10,  ylims=[10^-6, 10^11], yticks=[10^-5, 1, 10^5, 10^10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_errors = vec([[x[2] for x in relative_errors_with_stats[\"wordnet\"]]... [x[2] for x in relative_errors_with_hash[\"wordnet\"]]...  [x[2] for x in relative_errors_with_no_color[\"wordnet\"]]...  [x[2] for x in relative_errors_with_degree[\"wordnet\"]]... [x[2] for x in relative_errors_with_directed_degree[\"wordnet\"]]... ])\n",
    "grouped_query_types = vec([query_types[\"wordnet\"]... query_types[\"wordnet\"]... query_types[\"wordnet\"]... query_types[\"wordnet\"]... query_types[\"wordnet\"]...])\n",
    "grouped_datasets = vec([[\"Quasi-Stable\" for _ in 1:length(query_types[\"wordnet\"])]... [\"Hash\" for _ in 1:length(query_types[\"wordnet\"])]... [\"No Partition\" for _ in 1:length(query_types[\"wordnet\"])]... [\"Degree\" for _ in 1:length(query_types[\"wordnet\"])]... [\"Directed Degree\" for _ in 1:length(query_types[\"wordnet\"])]...])\n",
    "groupedboxplot(grouped_query_types, grouped_errors, group = grouped_datasets, yscale =:log10,  ylims=[10^-7, 10^11], yticks=[10^-5, 1, 10^5, 10^10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "query_directories = Dict()\n",
    "query_directories[\"yeast\"] = [\"/queryset/yeast\"]\n",
    "query_directories[\"hprd\"] = [\"/queryset/hprd\"]\n",
    "query_directories[\"wordnet\"] = [\"/queryset/wordnet\"]\n",
    "dataset_names = [\"yeast\"]\n",
    "\n",
    "query_paths = Dict()\n",
    "for dataset in dataset_names\n",
    "    query_paths[dataset] = [readdir(pwd() * dir, join=true) for dir in query_directories[dataset]]\n",
    "    query_paths[dataset] = [(query_paths[dataset]...)...]\n",
    "end\n",
    "count = 0\n",
    "timeout = 120\n",
    "query = 0\n",
    "for dataset in dataset_names\n",
    "    println(\"---------------------------------------------------------------------------------------------\")\n",
    "    println(\"Running \", dataset)\n",
    "    for query_path in query_paths[dataset]\n",
    "        count += 1\n",
    "        true_card_path = replace(query_path, \"queryset\"=>\"TrueCardinalities\")\n",
    "        id_and_query = load_query(query_path, subgraph_matching_data=true)\n",
    "        if isfile(true_card_path)  || !occursin(\"sparse_8_\", query_path) \n",
    "            continue\n",
    "        end\n",
    "        println(\"Query: \", query_path)\n",
    "        id = id_and_query[1]\n",
    "        query = id_and_query[2]\n",
    "        exact_size = get_exact_size(query, datasets[dataset], timeout=timeout)\n",
    "        if exact_size < 0\n",
    "            println(\"Timed Out!\")\n",
    "            continue\n",
    "        elseif exact_size == 0\n",
    "            println(\"Zero Matches!\")\n",
    "            continue\n",
    "        end\n",
    "        true_card_file = open(true_card_path, \"w\")\n",
    "        show(true_card_file , exact_size)\n",
    "        close(true_card_file)\n",
    "    end\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics\n",
    "println(maximum([log10(x[2]) for x in human_relative_errors]))\n",
    "println(mean([abs(log10(x[2])) for x in human_relative_errors]))\n",
    "println(minimum([log10(x[2]) for x in human_relative_errors]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics\n",
    "println(maximum([log10(x[2]) for x in human_relative_errors_with_stats]))\n",
    "println(mean([abs(log10(x[2])) for x in human_relative_errors_with_stats]))\n",
    "println(minimum([log10(x[2]) for x in human_relative_errors_with_stats]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "StatsPlots.boxplot(human_query_type, [x[2] for x in human_relative_errors_with_stats],  yscale=:log10, fillalpha=0.5, linewidth=2, label=\"With Coloring\", ylims=[10^-4, 10^7], yticks=[10^-4,10^-2, 1, 10^2, 10^4, 10^6])\n",
    "StatsPlots.boxplot!(human_query_type, [x[2] for x in human_relative_errors_with_hash],  yscale=:log10, fillalpha=0.5, linewidth=2, label=\"With Hash\")\n",
    "StatsPlots.boxplot!(human_query_type, [x[2] for x in human_relative_errors_with_no_color],  yscale=:log10, fillalpha=0.5, linewidth=2, label=\"1 Color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"Source/QuasiStableCardinalityEstimator.jl\")\n",
    "\n",
    "aids_query_directories = [\"/queryset/aids/Chain_3/\",\n",
    "\"/queryset/aids/Chain_6/\",\n",
    "\"/queryset/aids/Chain_9/\",\n",
    "\"/queryset/aids/Chain_12/\",\n",
    "\"/queryset/aids/Cycle_3/\",\n",
    "\"/queryset/aids/Cycle_6/\",\n",
    "\"/queryset/aids/Flower_6/\",\n",
    "\"/queryset/aids/Flower_9/\",\n",
    "\"/queryset/aids/Flower_12/\",\n",
    "\"/queryset/aids/Graph_3/\",\n",
    "\"/queryset/aids/Graph_6/\",\n",
    "\"/queryset/aids/Graph_9/\",\n",
    "\"/queryset/aids/Graph_12/\",\n",
    "\"/queryset/aids/Petal_6/\",\n",
    "\"/queryset/aids/Petal_9/\",\n",
    "\"/queryset/aids/Petal_12/\",\n",
    "\"/queryset/aids/Star_3/\",\n",
    "\"/queryset/aids/Star_6/\",\n",
    "\"/queryset/aids/Star_9/\",\n",
    "\"/queryset/aids/Tree_3/\",\n",
    "\"/queryset/aids/Tree_6/\",\n",
    "\"/queryset/aids/Tree_9/\",\n",
    "\"/queryset/aids/Tree_12/\"]\n",
    "aids_query_paths = [readdir(pwd() * dir, join=true) for dir in aids_query_directories]\n",
    "aids_query_paths = [(aids_query_paths...)...]\n",
    "aids_exact_sizes = []\n",
    "aids_bounds = []\n",
    "aids_bounds_with_stats = []\n",
    "aids_relative_errors = []\n",
    "aids_relative_errors_with_stats = []\n",
    "aids_query_types = []\n",
    "println(\"Summary Size: \", summary_size[\"aids\"])\n",
    "println(\"Summary Build Time: \", build_time[\"aids\"])\n",
    "for query_path in aids_query_paths\n",
    "    println(\"Query: \", query_path)\n",
    "    id_and_query = load_query(query_path)\n",
    "    id = id_and_query[1]\n",
    "    query = id_and_query[2]\n",
    "    query_type = match(r\".*/queryset/aids/(.*)_.*/.*\", query_path).captures[1]\n",
    "    if query_type != \"Cycle\"\n",
    "        continue\n",
    "    end\n",
    "    bound_results = @timed get_cardinality_bounds(query, color_summaries[\"aids\"], usingStoredStats=false)\n",
    "    bound_results_with_stats = @timed get_cardinality_bounds(query, color_summaries[\"aids\"], usingStoredStats=true)\n",
    "    gcare_size = load_true_cardinality(replace(query_path, \"queryset\"=>\"TrueCardinalities\"))\n",
    "#    println(\"Bound Time: \", bound_results[2])\n",
    "#    println(\"Exact Size: \", gcare_size)\n",
    "#    println(\"Bound [Lower, Avg, Upper]: \", bound_results[1])\n",
    "#    println(\"Relative Error [Lower, Avg, Upper]: \", bound_results[1]./gcare_size)\n",
    "    bound_results[1][2] = max(1, bound_results[1][2])\n",
    "    bound_results_with_stats[1][2] = max(1, bound_results_with_stats[1][2])\n",
    "    push!(aids_exact_sizes, gcare_size)\n",
    "    push!(aids_bounds, bound_results[1])\n",
    "    push!(aids_bounds_with_stats, bound_results_with_stats[1])\n",
    "    push!(aids_relative_errors, bound_results[1] ./ gcare_size)\n",
    "    push!(aids_relative_errors_with_stats, bound_results_with_stats[1] ./ gcare_size)\n",
    "    push!(aids_query_types, query_type)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics\n",
    "println(maximum([log10(x[2]) for x in aids_relative_errors]))\n",
    "println(mean([log10(x[2]) for x in aids_relative_errors]))\n",
    "println(mean([abs(log10(x[2])) for x in aids_relative_errors]))\n",
    "println(minimum([log10(x[2]) for x in aids_relative_errors]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Statistics\n",
    "println(maximum([log10(x[2]) for x in aids_relative_errors_with_stats]))\n",
    "println(mean([log10(x[2]) for x in aids_relative_errors_with_stats]))\n",
    "println(mean([abs(log10(x[2])) for x in aids_relative_errors_with_stats]))\n",
    "println(minimum([log10(x[2]) for x in aids_relative_errors_with_stats]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "StatsPlots.boxplot(aids_query_types, [x[2] for x in aids_relative_errors_with_stats],  yscale=:log10, fillalpha=0.5, linewidth=2, label=\"With Cycle Stats\", ylims=[10^-6, 10^11], yticks=[10^-5, 1, 10^5, 10^10])\n",
    "StatsPlots.boxplot!(aids_query_types, [x[2] for x in aids_relative_errors],  yscale=:log10, fillalpha=0.5, linewidth=2, label=\"Without Cycle Stats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(data[!,\"QueryType\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lubm80_query_paths = [\"lubm80/lubm80_Q2.txt\",\n",
    "# \"lubm80/lubm80_Q4.txt\",\n",
    "# \"lubm80/lubm80_Q7.txt\",\n",
    "# \"lubm80/lubm80_Q8.txt\",\n",
    "# \"lubm80/lubm80_Q9.txt\",\n",
    "# \"lubm80/lubm80_Q12.txt\"\n",
    "                    ]\n",
    "exactSizes = []\n",
    "bounds = []\n",
    "relativeErrors = []\n",
    "println(\"Summary Size: \", summary_size[1])\n",
    "println(\"Summary Build Time: \", build_time[1])\n",
    "for query_path in lubm80_query_paths\n",
    "    println(\"Query: \", query_path)\n",
    "    id_and_query = load_query(\"queryset/\" * query_path)\n",
    "    id = id_and_query[1]\n",
    "    query = id_and_query[2]\n",
    "    bound_results = @timed get_cardinality_bounds(query, color_summaries[1])\n",
    "    exact_size_results = @timed get_exact_size(query, datasets[1])\n",
    "    gcare_size = load_true_cardinality(\"TrueCardinalities/\" * query_path)\n",
    "    println(\"Bound Time: \", bound_results[2])\n",
    "    println(\"Exact Size Time: \", exact_size_results[2])\n",
    "    println(\"Exact Size: \", exact_size_results[1])\n",
    "    println(\"GCare Exact Size: \", gcare_size)\n",
    "    if gcare_size != exact_size_results[1]\n",
    "        println(\"Bad Exact Size Estimate!!\")\n",
    "        break\n",
    "    end\n",
    "    println(\"Bound [Lower, Avg, Upper]: \", bound_results[1])\n",
    "    println(\"Relative Error [Lower, Avg, Upper]: \", (bound_results[1])./exact_size_results[1])\n",
    "    push!(exactSizes, gcare_size)\n",
    "    push!(bounds, bound_results[1])\n",
    "    push!(relativeErrors, bound_results[1] ./ gcare_size)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exactSizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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