{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data analysis for the paper \"Automated Generation of Consistent Models with Structural and Attribute Constraints\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's load some packages." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Loading required package: tidyverse\n", "\n", "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.3.0 ──\n", "\n", "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.2 \u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 0.3.4\n", "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.0.3 \u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.0.0\n", "\u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.1.0 \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n", "\u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 1.3.1 \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.0\n", "\n", "Warning message:\n", "“package ‘ggplot2’ was built under R version 4.0.1”\n", "Warning message:\n", "“package ‘tibble’ was built under R version 4.0.2”\n", "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n", "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n", "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n", "\n", "Warning message in dir.create(\"plots\"):\n", "“'plots' already exists”\n" ] } ], "source": [ "require(tidyverse)\n", "dir.create('plots')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": false }, "outputs": [], "source": [ "ProcessDetailedStatistics <- function(str) {\n", " str <- sub('TransformationExecutionTime', 'TransformationExecutionTime:', str)\n", " str <- sub('Backtrackingtime', 'BacktrackingTime', str)\n", " str <- gsub('\\\\(|\\\\)', '', str)\n", " str <- lapply(strsplit(str, '\\\\||:'), function (v) {\n", " dim(v) <- c(2, 12)\n", " values <- as.double(v[2,])\n", " names(values) <- v[1,]\n", " as.data.frame(t(values))\n", " })\n", " str\n", "}\n", "Load10Log <- function(filename, size) {\n", " read_csv(filename, col_types = cols(\n", " .default = col_double(),\n", " Result = col_character(),\n", " Solution1DetailedStatistics = col_character(),\n", " Solution2DetailedStatistics = col_character(),\n", " Solution3DetailedStatistics = col_character(),\n", " Solution4DetailedStatistics = col_character(),\n", " Solution5DetailedStatistics = col_character(),\n", " Solution6DetailedStatistics = col_character(),\n", " Solution7DetailedStatistics = col_character(),\n", " Solution8DetailedStatistics = col_character(),\n", " Solution9DetailedStatistics = col_character(),\n", " Solution10DetailedStatistics = col_character()\n", " )) %>% transmute(\n", " n = size,\n", " Run = Run,\n", " preprocessingTime = get('Domain to logic transformation time') + get('Logic to solver transformation time') + ExplorationInitializationTime,\n", " Solution0FoundAt = Solution0FoundAt,\n", " additionalTime = Solution9FoundAt - Solution0FoundAt,\n", " Solution1DetailedStatistics = ProcessDetailedStatistics(Solution1DetailedStatistics)\n", " ) %>% unnest() %>% mutate(\n", " # (Logical) constraint evluation should count as refinement.\n", " ForwardTime = ForwardTime + GlobalConstraintEvaluationTime + FitnessCalculationTime,\n", " preprocessingTime = preprocessingTime,\n", " BacktrackingTime = Solution0FoundAt - (StateCoderTime + ForwardTime + NumericalSolverSumTime)\n", " ) %>% select(n, Run, preprocessingTime, StateCoderTime, ForwardTime, BacktrackingTime, NumericalSolverSumTime, additionalTime)\n", "}\n", "Load1Log <- function(filename, size) {\n", " read_csv(filename, col_types = cols(\n", " .default = col_double(),\n", " Result = col_character(),\n", " Solution1DetailedStatistics = col_character()\n", " )) %>% filter(Result == \"ModelResultImpl\") %>% transmute(\n", " n = size,\n", " Run = Run,\n", " preprocessingTime = get('Domain to logic transformation time') + get('Logic to solver transformation time') + ExplorationInitializationTime,\n", " Solution0FoundAt = Solution0FoundAt,\n", " Solution1DetailedStatistics = ProcessDetailedStatistics(Solution1DetailedStatistics)\n", " ) %>% unnest(cols = c(Solution1DetailedStatistics)) %>% mutate(\n", " ForwardTime = ForwardTime + GlobalConstraintEvaluationTime + FitnessCalculationTime,\n", " BacktrackingTime = Solution0FoundAt - (StateCoderTime + ForwardTime + NumericalSolverSumTime)\n", " ) %>% select(n, Run, preprocessingTime, StateCoderTime, ForwardTime, BacktrackingTime, NumericalSolverSumTime)\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "ProcessRQ1 <- function(df) {\n", " df %>% group_by(n) %>% summarize(\n", " .groups = 'drop',\n", " time = median(preprocessingTime + StateCoderTime + ForwardTime + BacktrackingTime + NumericalSolverSumTime) / 1000.0\n", " )\n", "}\n", "ProcessRQ2 <- function(df) {\n", " df %>% group_by(n) %>% summarize(\n", " .groups = 'drop',\n", " preprocessingTime = median(preprocessingTime) / 1000.0,\n", " StateCoderTime = median(StateCoderTime) / 1000.0,\n", " ForwardTime = median(ForwardTime) / 1000.0,\n", " BacktrackingTime = median(BacktrackingTime) / 1000.0,\n", " NumericalSolverSumTime = median(NumericalSolverSumTime) / 1000.0,\n", " additionalTime = median(additionalTime) / 1000.0\n", " )\n", "}\n", "ProcessRQ3 <- ProcessRQ1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "RQ2Plot <- function(df, name) {\n", " df <- df %>% gather(name, value, -n) %>% filter(name != \"preprocessingTime\")\n", " df$name <- factor(df$name, levels=rev(c('ForwardTime', 'BacktrackingTime', 'StateCoderTime', 'NumericalSolverSumTime', 'additionalTime')))\n", " plot <- df %>% ggplot(aes(x=n, y=value, fill=name)) +\n", " geom_bar(stat='identity') +\n", " scale_fill_brewer(palette='Set2',\n", " labels=rev(c('Refinement', 'Backtracking', 'State Coding', 'SMT Solver Calls', 'Additional Model Generation')),\n", " guide=FALSE) +\n", " scale_x_continuous(breaks=c(20, 40, 60, 80, 100), name=\"Model Size (# nodes)\") +\n", " scale_y_continuous(name=\"Runtime (s)\") +\n", " theme_bw()\n", " ggsave(plot=plot, filename=paste0('plots/plot_RQ2_', name, '.pdf'), width=3.5, height=2.5)\n", " plot\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Fam domain" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", "“`cols` is now required when using unnest().\n", "Please use `cols = c(Solution1DetailedStatistics)`”\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 8
nRunpreprocessingTimeStateCoderTimeForwardTimeBacktrackingTimeNumericalSolverSumTimeadditionalTime
<dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2011473599418901272439114750
\n" ], "text/latex": [ "A tibble: 1 × 8\n", "\\begin{tabular}{llllllll}\n", " n & Run & preprocessingTime & StateCoderTime & ForwardTime & BacktrackingTime & NumericalSolverSumTime & additionalTime\\\\\n", " & & & & & & & \\\\\n", "\\hline\n", "\t 20 & 1 & 14735 & 994 & 1890 & 1272 & 4391 & 14750\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 8\n", "\n", "| n <dbl> | Run <dbl> | preprocessingTime <dbl> | StateCoderTime <dbl> | ForwardTime <dbl> | BacktrackingTime <dbl> | NumericalSolverSumTime <dbl> | additionalTime <dbl> |\n", "|---|---|---|---|---|---|---|---|\n", "| 20 | 1 | 14735 | 994 | 1890 | 1272 | 4391 | 14750 |\n", "\n" ], "text/plain": [ " n Run preprocessingTime StateCoderTime ForwardTime BacktrackingTime\n", "1 20 1 14735 994 1890 1272 \n", " NumericalSolverSumTime additionalTime\n", "1 4391 14750 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\n", "\n", "\t\n", "\n", "
A tibble: 1 × 7
npreprocessingTimeStateCoderTimeForwardTimeBacktrackingTimeNumericalSolverSumTimeadditionalTime
<dbl><dbl><dbl><dbl><dbl><dbl><dbl>
2014.7350.9941.891.2724.39114.75
\n" ], "text/latex": [ "A tibble: 1 × 7\n", "\\begin{tabular}{lllllll}\n", " n & preprocessingTime & StateCoderTime & ForwardTime & BacktrackingTime & NumericalSolverSumTime & additionalTime\\\\\n", " & & & & & & \\\\\n", "\\hline\n", "\t 20 & 14.735 & 0.994 & 1.89 & 1.272 & 4.391 & 14.75\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A tibble: 1 × 7\n", "\n", "| n <dbl> | preprocessingTime <dbl> | StateCoderTime <dbl> | ForwardTime <dbl> | BacktrackingTime <dbl> | NumericalSolverSumTime <dbl> | additionalTime <dbl> |\n", "|---|---|---|---|---|---|---|\n", "| 20 | 14.735 | 0.994 | 1.89 | 1.272 | 4.391 | 14.75 |\n", "\n" ], "text/plain": [ " n preprocessingTime StateCoderTime ForwardTime BacktrackingTime\n", "1 20 14.735 0.994 1.89 1.272 \n", " NumericalSolverSumTime additionalTime\n", "1 4.391 14.75 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "14.735" ], "text/latex": [ "14.735" ], "text/markdown": [ "14.735" ], "text/plain": [ "[1] 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"text/plain": [ "plot without title" ] }, "metadata": { "image/png": { "height": 420, "width": 420 }, "text/plain": { "height": 420, "width": 420 } }, "output_type": "display_data" } ], "source": [ "FamilyTreeRQ2Raw <- rbind(\n", "# Load10Log(\"measurements/stats/FamilyTree//size010to-1r10n10rt300nsdrealstats_06-0249.csv\", 10),\n", " Load10Log(\"measurements1/stats.csv\", 20)\n", ")\n", "FamilyTreeRQ2Raw\n", "FamilyTreeRQ2 <- FamilyTreeRQ2Raw %>% ProcessRQ2\n", "FamilyTreeRQ2\n", "median(FamilyTreeRQ2Raw$preprocessingTime) / 1000.0\n", "FamilyTreeRQ2 %>% RQ2Plot('FamilyTree')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.0.0" } }, "nbformat": 4, "nbformat_minor": 2 }