g-care-diandre.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## G-CARE Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_cardinality_bounds (generic function with 1 method)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Graphs: DiGraph\n",
    "using StatsPlots\n",
    "include(\"Source/datasets.jl\")\n",
    "include(\"Source/QuasiStableCardinalityEstimator.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "youtube_data_file_path = \"dataset/youtube/youtube.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",
    "youtube_data = load_dataset(youtube_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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Int64} with 6 entries:\n",
       "  \"yeast\"   => 6\n",
       "  \"wordnet\" => 3\n",
       "  \"hprd\"    => -1\n",
       "  \"youtube\" => -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, \"youtube\" => youtube_data)\n",
    "#dataset_names = [\"aids\", \"human\", \"yeast\"]\n",
    "dataset_names = [\"youtube\"]\n",
    "num_sample_nodes = Dict(\"aids\"=>100, \"human\"=>100, \"yeast\"=>50, \"hprd\"=>100, \"wordnet\"=>10, \"youtube\"=>10)\n",
    "gcare_dataset = Dict(\"aids\"=>true, \"human\"=>true, \"yeast\"=>false, \"hprd\"=>false, \"wordnet\"=>false, \"youtube\"=>false)\n",
    "max_cycle_size = Dict(\"aids\"=>6, \"human\"=>-1, \"yeast\"=>6, \"hprd\"=>-1, \"wordnet\"=>3, \"youtube\"=>-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished bloom filters\n",
      "Started cardinality counts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished cardinality counts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started tracking statistics\n",
      "Finished tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n",
      "Finished tracking statistics"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Started coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorted labels"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started bloom filters\n",
      "Finished bloom filters\n",
      "Started cardinality counts"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished cardinality counts\n",
      "Started tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished tracking statistics\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running youtube"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "started graphing\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using Graphs: DiGraph\n",
    "using StatsPlots\n",
    "include(\"Source/datasets.jl\")\n",
    "include(\"Source/QuasiStableCardinalityEstimator.jl\")\n",
    "\n",
    "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",
    "color_label_summaries = Dict()\n",
    "label_in_out_summaries = Dict()\n",
    "most_neighbors_summaries = Dict()\n",
    "\n",
    "num_colors = 16\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], max_partial_paths=num_sample_nodes[dataset], partitioner=\"DirectedDegree\", precolor=false)\n",
    "    degree_color_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], max_partial_paths=num_sample_nodes[dataset], partitioner=\"Degree\", precolor=false)\n",
    "    results = @timed generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], max_partial_paths=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], max_partial_paths=num_sample_nodes[dataset], partitioner=\"Hash\", precolor=false)\n",
    "    no_color_summaries[dataset] = generate_color_summary(datasets[dataset], 1, verbose=true, max_size = max_cycle_size[dataset], max_partial_paths=num_sample_nodes[dataset])\n",
    "    # println(no_color_summaries[dataset])\n",
    "    color_label_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], max_partial_paths=num_sample_nodes[dataset], partitioner=\"Simple Label\", precolor=false)\n",
    "    label_in_out_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], max_partial_paths=num_sample_nodes[dataset], partitioner=\"Label In Out Ratio\", precolor=false)\n",
    "    most_neighbors_summaries[dataset] = generate_color_summary(datasets[dataset], num_colors, verbose=true, max_size = max_cycle_size[dataset], max_partial_paths=num_sample_nodes[dataset], partitioner=\"Most Neighbors\", precolor=false)\n",
    "\n",
    "    # println(no_color_summaries[dataset])\n",
    "end\n",
    "\n",
    "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",
    "query_directories[\"youtube\"] = [\"/queryset/youtube\"]\n",
    "dataset_names = [\"youtube\"]\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",
    "bounds_with_label_color = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_label_in_out = Dict(dataset => [] for dataset in dataset_names)\n",
    "bounds_with_most_neighbors = Dict(dataset => [] for dataset in dataset_names)\n",
    "\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",
    "\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(\"dense_4\", query_path) && continue\n",
    "        occursin(\"sparse_32\", query_path) && continue\n",
    "        occursin(\"sparse_24\", query_path) && continue\n",
    "        occursin(\"sparse_20\", query_path) && continue\n",
    "        occursin(\"sparse_16\", query_path) && continue\n",
    "        occursin(\"sparse_12\", query_path) && continue\n",
    "        occursin(\"sparse_8\", 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",
    "            thing = match(r\".*/queryset/.*\\\\query_(.*)_.*\", query_path)\n",
    "            if (thing === nothing)\n",
    "                println(\"missed\")\n",
    "                continue\n",
    "            end\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",
    "        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_label_color = get_cardinality_bounds(query, color_label_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results_with_label_in_out = get_cardinality_bounds(query, label_in_out_summaries[dataset], usingStoredStats=true)\n",
    "        bound_results_with_most_neighbors = get_cardinality_bounds(query, most_neighbors_summaries[dataset], usingStoredStats=true)\n",
    "\n",
    "        # println(bound_results_with_no_color)\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_label_color[2] = max(1, bound_results_with_label_color[2])\n",
    "        bound_results_with_label_in_out[2] = max(1, bound_results_with_label_in_out[2])\n",
    "        bound_results_with_most_neighbors[2] = max(1, bound_results_with_most_neighbors[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_label_color[dataset], bound_results_with_label_color)\n",
    "        push!(bounds_with_label_in_out[dataset], bound_results_with_label_in_out)\n",
    "        push!(bounds_with_most_neighbors[dataset], bound_results_with_most_neighbors)\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_label_color = Dict(dataset => bounds_with_label_color[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_label_in_out = Dict(dataset => bounds_with_label_in_out[dataset] ./ exact_sizes[dataset] for dataset in dataset_names)\n",
    "relative_errors_with_most_neighbors = Dict(dataset => bounds_with_most_neighbors[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)\n",
    "\n",
    "\n",
    "current_dataset = \"youtube\"\n",
    "\n",
    "# '...' takes something that was a vector and turning each element into an argument\n",
    "# the 'y' value'\n",
    "grouped_errors = vec([[x[2] for x in relative_errors_with_stats[current_dataset]]... [x[2] for x in relative_errors_with_hash[current_dataset]]...  [x[2] for x in relative_errors_with_no_color[current_dataset]]...  [x[2] for x in relative_errors_with_degree[current_dataset]]... [x[2] for x in relative_errors_with_directed_degree[current_dataset]]... [x[2] for x in relative_errors_with_label_color[current_dataset]]... [x[2] for x in relative_errors_with_label_in_out[current_dataset]]... [x[2] for x in relative_errors_with_most_neighbors[current_dataset]]...])\n",
    "# the 'x' value\n",
    "grouped_query_types = vec([query_types[current_dataset]... query_types[current_dataset]... query_types[current_dataset]... query_types[current_dataset]... query_types[current_dataset]... query_types[current_dataset]... query_types[current_dataset]... query_types[current_dataset]...])\n",
    "# 'grouped methods'\n",
    "grouped_datasets = vec([[\"Quasi-Stable\" for _ in 1:length(query_types[current_dataset])]... [\"Hash\" for _ in 1:length(query_types[current_dataset])]... [\"No Color\" for _ in 1:length(query_types[current_dataset])]... [\"Degree\" for _ in 1:length(query_types[current_dataset])]... [\"Directed Degree\" for _ in 1:length(query_types[current_dataset])]... [\"Label Color\" for _ in 1:length(query_types[current_dataset])]... [\"Label In Out\" for _ in 1:length(query_types[current_dataset])]... [\"Most Neighbors\" for _ in 1:length(query_types[current_dataset])]...])\n",
    "println(\"started graphing\")\n",
    "# args: x value, y value, labels\n",
    "groupedboxplot(grouped_query_types, grouped_errors, group = grouped_datasets, yscale =:log10,  ylims=[10^-11, 10^11], yticks=[10^-5, 1, 10^5, 10^10])\n",
    "# png(\"C:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\figures\\plot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Vector{Vector{Float64}}} with 1 entry:\n",
       "  \"wordnet\" => [[0.0, 0.144002, 8718.27], [0.0, 0.0571729, 9.02499e5], [0.0, 0.…"
      ]
     },
     "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)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "using WAV\n",
    "y, fs = wavread(raw\"C:\\Windows\\Media\\Alarm01.wav\")\n",
    "wavplay(y, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum([x[2] != 1 for x in values(bounds)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sum([x[2] != 1 for x in values(bounds)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "KeyError: key \"yeast\" not found",
     "output_type": "error",
     "traceback": [
      "KeyError: key \"yeast\" not found\n",
      "\n",
      "Stacktrace:\n",
      " [1] getindex(h::Dict{String, Vector{Any}}, key::String)\n",
      "   @ Base .\\dict.jl:498\n",
      " [2] top-level scope\n",
      "   @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\g-care-kyle.ipynb:1"
     ]
    }
   ],
   "source": [
    "exact_sizes[\"yeast\"][exact_sizes[\"yeast\"] .> 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "KeyError: key \"human\" not found",
     "output_type": "error",
     "traceback": [
      "KeyError: key \"human\" not found\n",
      "\n",
      "Stacktrace:\n",
      " [1] getindex(h::Dict{String, Vector{Any}}, key::String)\n",
      "   @ Base .\\dict.jl:498\n",
      " [2] top-level scope\n",
      "   @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\g-care-kyle.ipynb:1"
     ]
    }
   ],
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "KeyError: key \"aids\" not found",
     "output_type": "error",
     "traceback": [
      "KeyError: key \"aids\" not found\n",
      "\n",
      "Stacktrace:\n",
      " [1] getindex(h::Dict{String, Vector{Any}}, key::String)\n",
      "   @ Base .\\dict.jl:498\n",
      " [2] top-level scope\n",
      "   @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\g-care-kyle.ipynb:1"
     ]
    }
   ],
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "KeyError: key \"yeast\" not found",
     "output_type": "error",
     "traceback": [
      "KeyError: key \"yeast\" not found\n",
      "\n",
      "Stacktrace:\n",
      " [1] getindex(h::Dict{String, Vector{Any}}, key::String)\n",
      "   @ Base .\\dict.jl:498\n",
      " [2] top-level scope\n",
      "   @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\g-care-kyle.ipynb:1"
     ]
    }
   ],
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "KeyError: key \"hprd\" not found",
     "output_type": "error",
     "traceback": [
      "KeyError: key \"hprd\" not found\n",
      "\n",
      "Stacktrace:\n",
      " [1] getindex(h::Dict{String, Vector{Any}}, key::String)\n",
      "   @ Base .\\dict.jl:498\n",
      " [2] top-level scope\n",
      "   @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\g-care-kyle.ipynb:1"
     ]
    }
   ],
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "ename": "ArgumentError",
     "evalue": "ArgumentError: quantiles are undefined in presence of NaNs or missing values",
     "output_type": "error",
     "traceback": [
      "ArgumentError: quantiles are undefined in presence of NaNs or missing values\n",
      "\n",
      "Stacktrace:\n",
      "  [1] _quantilesort!(v::Vector{Float64}, sorted::Bool, minp::Float64, maxp::Float64)\n",
      "    @ Statistics C:\\Users\\diand\\AppData\\Local\\Programs\\Julia-1.8.5\\share\\julia\\stdlib\\v1.8\\Statistics\\src\\Statistics.jl:980\n",
      "  [2] #quantile!#49\n",
      "    @ C:\\Users\\diand\\AppData\\Local\\Programs\\Julia-1.8.5\\share\\julia\\stdlib\\v1.8\\Statistics\\src\\Statistics.jl:957 [inlined]\n",
      "  [3] quantile(v::Vector{Float64}, p::StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}; sorted::Bool, alpha::Float64, beta::Float64)\n",
      "    @ Statistics C:\\Users\\diand\\AppData\\Local\\Programs\\Julia-1.8.5\\share\\julia\\stdlib\\v1.8\\Statistics\\src\\Statistics.jl:1073\n",
      "  [4] quantile(v::Vector{Float64}, p::StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64})\n",
      "    @ Statistics C:\\Users\\diand\\AppData\\Local\\Programs\\Julia-1.8.5\\share\\julia\\stdlib\\v1.8\\Statistics\\src\\Statistics.jl:1073\n",
      "  [5] macro expansion\n",
      "    @ C:\\Users\\diand\\.julia\\packages\\StatsPlots\\vq5FV\\src\\boxplot.jl:40 [inlined]\n",
      "  [6] apply_recipe(plotattributes::AbstractDict{Symbol, Any}, #unused#::Type{Val{:boxplot}}, x::Any, y::Any, z::Any)\n",
      "    @ StatsPlots C:\\Users\\diand\\.julia\\packages\\RecipesBase\\BRe07\\src\\RecipesBase.jl:300\n",
      "  [7] _process_seriesrecipe(plt::Any, plotattributes::Any)\n",
      "    @ RecipesPipeline C:\\Users\\diand\\.julia\\packages\\RecipesPipeline\\BGM3l\\src\\series_recipe.jl:50\n",
      "  [8] _process_seriesrecipes!(plt::Any, kw_list::Any)\n",
      "    @ RecipesPipeline C:\\Users\\diand\\.julia\\packages\\RecipesPipeline\\BGM3l\\src\\series_recipe.jl:27\n",
      "  [9] recipe_pipeline!(plt::Any, plotattributes::Any, args::Any)\n",
      "    @ RecipesPipeline C:\\Users\\diand\\.julia\\packages\\RecipesPipeline\\BGM3l\\src\\RecipesPipeline.jl:99\n",
      " [10] _plot!(plt::Plots.Plot, plotattributes::Any, args::Any)\n",
      "    @ Plots C:\\Users\\diand\\.julia\\packages\\Plots\\tDI0A\\src\\plot.jl:223\n",
      " [11] #plot#186\n",
      "    @ C:\\Users\\diand\\.julia\\packages\\Plots\\tDI0A\\src\\plot.jl:102 [inlined]\n",
      " [12] groupedboxplot(::Vector{SubString{String}}, ::Vararg{Any}; kw::Base.Pairs{Symbol, Any, NTuple{4, Symbol}, NamedTuple{(:group, :yscale, :ylims, :yticks), Tuple{Vector{String}, Symbol, Vector{Float64}, Vector{Float64}}}})\n",
      "    @ StatsPlots C:\\Users\\diand\\.julia\\packages\\RecipesBase\\BRe07\\src\\RecipesBase.jl:380\n",
      " [13] top-level scope\n",
      "    @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\g-care-kyle.ipynb:4"
     ]
    }
   ],
   "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\"])]... [\"Color Label\" 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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