cycle-test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# funny sine graph\n",
    "using Plots\n",
    "x = range(0, 10, length=100)\n",
    "y = sin.(x)\n",
    "plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# funny bar graph\n",
    "using Plots\n",
    "x = [\"hi\", \"hello\", \"whats up\"]\n",
    "y = rand(3)\n",
    "p = bar(x, y)\n",
    "ylabel!(\"y\")\n",
    "xlabel!(\"greetings\")\n",
    "title!(\"odds of me saying something\")\n",
    "display(p)\n",
    "#savefig(p, \"barplot.png\")"
   ]
  },
  {
   "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": [
    "# setup code\n",
    "using Graphs: DiGraph\n",
    "using StatsPlots\n",
    "using Plots, GraphRecipes\n",
    "include(\"Source/datasets.jl\")\n",
    "include(\"Source/QuasiStableCardinalityEstimator.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished coloring"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "UndefVarError",
     "evalue": "UndefVarError: i not defined",
     "output_type": "error",
     "traceback": [
      "UndefVarError: i not defined\n",
      "\n",
      "Stacktrace:\n",
      " [1] get_start_color_cycle_likelihoods(max_cycle_size::Int64, data::DataGraph, color_hash::Dict{Int64, UInt64})\n",
      "   @ Main c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\Source\\QuasiStableCardinalityEstimator.jl:275\n",
      " [2] generate_color_summary(g::DataGraph, numColors::Int64; weighting::Bool, verbose::Bool, max_size::Int64, num_sample_nodes::Int64)\n",
      "   @ Main c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\Source\\QuasiStableCardinalityEstimator.jl:49\n",
      " [3] macro expansion\n",
      "   @ .\\timing.jl:463 [inlined]\n",
      " [4] top-level scope\n",
      "   @ c:\\Users\\diand\\OneDrive\\Desktop\\Cardinality-with-Colors\\cycle-test.ipynb:9"
     ]
    }
   ],
   "source": [
    "aids_data_file_path = \"dataset/aids/aids.txt\"\n",
    "aids_data = load_dataset(aids_data_file_path)\n",
    "datasets = [aids_data]\n",
    "dataset_names = [\"aids\"]\n",
    "build_time = []\n",
    "summary_size = []\n",
    "color_summaries = []\n",
    "for dataset in datasets\n",
    "    results = @timed generate_color_summary(dataset, 32, verbose=true)\n",
    "    push!(build_time, results[2])\n",
    "    push!(summary_size,  get_color_summary_size(results[1]))\n",
    "    push!(color_summaries, results[1])\n",
    "end"
   ]
  },
  {
   "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[1])\n",
    "println(\"Summary Build Time: \", build_time[1])\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",
    "    bound_results = @timed get_cardinality_bounds(query, color_summaries[1], usingStoredStats=false)\n",
    "    bound_results_with_stats = @timed get_cardinality_bounds(query, color_summaries[1], 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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load human data\n",
    "human_data_file_path = \"dataset/human/human.txt\"\n",
    "human_data = load_dataset(human_data_file_path)\n",
    "datasets = [human_data]\n",
    "dataset_names = [\"human\"]\n",
    "\n",
    "# generate color summary\n",
    "build_time = []\n",
    "summary_size = []\n",
    "color_summaries = []\n",
    "for dataset in datasets\n",
    "    results = @timed generate_color_summary(dataset, 64, verbose=true)\n",
    "    push!(build_time, results[2])\n",
    "    push!(summary_size,  get_color_summary_size(results[1]))\n",
    "    push!(color_summaries, results[1])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# graph stats about cycle probability given direction (HUMAN)\n",
    "path_probability_pairs = color_summaries[1].cycle_probabilities\n",
    "paths = keys(path_probability_pairs)\n",
    "println(\"Number of paths: \", length(paths))\n",
    "current_probabilities::Vector{Float64} = []\n",
    "path_types::Vector{String} = []\n",
    "current_index = 0\n",
    "for path in paths\n",
    "    # [f, b, f, b, ..]\n",
    "    current_path=\"\"\n",
    "    for direction in path\n",
    "        current_path = current_path * ((direction) ? \"f\" : \"b\")\n",
    "    end\n",
    "    push!(path_types, current_path)\n",
    "    push!(current_probabilities, path_probability_pairs[path])\n",
    "    println(path_probability_pairs[path])\n",
    "end\n",
    "p = bar(path_types, current_probabilities)\n",
    "xlabel!(\"Edge directions within path\")\n",
    "display(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "making cycles"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "making paths"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "making cycles\n",
      "making paths\n"
     ]
    },
    {
     "data": {
      "image/png": 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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "making cycles\n",
      "making paths"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# graph stats about cycle probability given distance\n",
    "# steps:\n",
    "# generate paths and cycles for each distance\n",
    "# find the odds that those paths close\n",
    "# graph (there should be only 3 bars)\n",
    "likelihoods::Dict{Int, Float64} = Dict()\n",
    "for i in 2:4\n",
    "    likelihoods[i] = approximate_cycle_likelihood(i, aids_data)\n",
    "end\n",
    "cycle_sizes::Vector{Int} = []\n",
    "cycle_likelihoods::Vector{Float64} = []\n",
    "for size in keys(likelihoods)\n",
    "    push!(cycle_sizes, size)\n",
    "    push!(cycle_likelihoods, likelihoods[size])\n",
    "end\n",
    "p = bar(cycle_sizes, cycle_likelihoods)\n",
    "xlabel!(\"Cycle size\")\n",
    "display(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_exact_size (generic function with 1 method)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setup code\n",
    "using Graphs: DiGraph\n",
    "using StatsPlots\n",
    "using Plots, GraphRecipes\n",
    "include(\"Source/datasets.jl\")\n",
    "include(\"Source/QuasiStableCardinalityEstimator.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_property = DataGraph(4)\n",
    "update_node_labels!(g_property, 1, [1])\n",
    "update_node_labels!(g_property, 2, [1])\n",
    "update_node_labels!(g_property, 3, [1])\n",
    "update_node_labels!(g_property, 4, [1])\n",
    "add_labeled_edge!(g_property, (1, 2), 1)\n",
    "add_labeled_edge!(g_property, (2, 3), 1)\n",
    "add_labeled_edge!(g_property, (2, 4), 1)\n",
    "add_labeled_edge!(g_property, (3, 1), 1)\n",
    "add_labeled_edge!(g_property, (4, 1), 1)\n",
    "display_graph!(g_property)\n",
    "likelihoods::Dict{Int, Float64} = Dict()\n",
    "for i in 2:4\n",
    "        likelihoods[i] = approximate_cycle_likelihood(i, g_property)\n",
    "end\n",
    "cycle_sizes::Vector{Int} = []\n",
    "cycle_likelihoods::Vector{Float64} = []\n",
    "for size in keys(likelihoods)\n",
    "        push!(cycle_sizes, size)\n",
    "        push!(cycle_likelihoods, likelihoods[size])\n",
    "end\n",
    "p = bar(cycle_sizes, cycle_likelihoods)\n",
    "xlabel!(\"Cycle size\")\n",
    "display(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# graph stats about cycle probability given direction\n",
    "path_probability_pairs = color_summaries[1].cycle_probabilities\n",
    "paths = keys(path_probability_pairs)\n",
    "println(\"Number of paths: \", length(paths))\n",
    "current_probabilities::Vector{Float64} = []\n",
    "path_types::Vector{String} = []\n",
    "current_index = 0\n",
    "for path in paths\n",
    "    current_path=\"\"\n",
    "    for direction in path\n",
    "        current_path = current_path * ((direction) ? \"f\" : \"b\")\n",
    "    end\n",
    "    push!(path_types, current_path)\n",
    "    push!(current_probabilities, path_probability_pairs[path])\n",
    "    println(path_probability_pairs[path])\n",
    "end\n",
    "p = bar(path_types, current_probabilities)\n",
    "xlabel!(\"Edge directions within path\")\n",
    "display(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# see if cycles have an effect on the results\n",
    "\n",
    "# initialize the aids dataset\n",
    "\n",
    "# generate the color summary\n",
    "\n",
    "# only do a couple of the exact sizes with/without cycles\n",
    "\n",
    "# do the queries with/without cycles\n",
    "\n",
    "# compare the results for with/without cycles\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_exact_size (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\")\n",
    "include(\"Source/ExactSizeCalculator.jl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "aids_data_file_path = \"dataset/aids/aids.txt\"\n",
    "aids_data = load_dataset(aids_data_file_path)\n",
    "datasets = Dict(\"aids\"=>aids_data)\n",
    "dataset_names = [\"aids\"]\n",
    "nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started coloring\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished coloring"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating Cycles of Size: 2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating Cycles of Size: 3"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Generating Cycles of Size: 4"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "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",
      "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",
    "for dataset in dataset_names\n",
    "    results = @timed generate_color_summary(datasets[dataset], 32, verbose=true, max_size = 4, num_sample_nodes=1000)\n",
    "    build_time[dataset] = results[2]\n",
    "    summary_size[dataset] = get_color_summary_size(results[1])\n",
    "    color_summaries[dataset] = results[1]\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "aids_query_directories = [\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",
    "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_exact_sizes_without_cycles = []\n",
    "aids_bounds = []\n",
    "aids_bounds_without_cycles = []\n",
    "aids_relative_errors = []\n",
    "aids_relative_errors_without_cycles = []\n",
    "aids_query_types = []\n",
    "println(\"Summary Size: \", summary_size[\"aids\"])\n",
    "println(\"Summary Build Time: \", build_time[\"aids\"])\n",
    "num_completed = 0\n",
    "for query_path in aids_query_paths\n",
    "    if num_completed > 100\n",
    "        break\n",
    "    end\n",
    "    num_completed += 1\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",
    "    bound_results = @timed get_cardinality_bounds(query, color_summaries[\"aids\"], usingStoredStats=true, include_cycles = true)\n",
    "    bound_results_without_cycles = @timed get_cardinality_bounds(query, color_summaries[\"aids\"], usingStoredStats=true, include_cycles = false)\n",
    "    exact_size = get_exact_size(query, aids_data)\n",
    "    exact_size_without_cycles = get_exact_size(query, aids_data, include_cycles = false)\n",
    "    bound_results[1][2] = max(1, bound_results[1][2])\n",
    "    bound_results_without_cycles[1][2] = max(1, bound_results_without_cycles[1][2])\n",
    "    push!(aids_exact_sizes, exact_size)\n",
    "    push!(aids_bounds, bound_results[1])\n",
    "    push!(aids_exact_sizes_without_cycles, exact_size_without_cycles)\n",
    "    push!(aids_bounds_without_cycles, bound_results_without_cycles[1])\n",
    "    push!(aids_relative_errors, bound_results[1] ./ exact_size)\n",
    "    push!(aids_relative_errors_without_cycles, bound_results_without_cycles[1] ./ exact_size_without_cycles)\n",
    "    push!(aids_query_types, query_type)\n",
    "end"
   ]
  }
 ],
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