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{
"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": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning message in dir.create(\"plots\"):\n",
"“'plots' already exists”\n"
]
}
],
"source": [
"require(tidyverse)\n",
"dir.create('plots')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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, 11)\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()\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": 13,
"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": 14,
"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": 29,
"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": [
"<table>\n",
"<caption>A tibble: 10 × 16</caption>\n",
"<thead>\n",
"\t<tr><th scope=col>n</th><th scope=col>Run</th><th scope=col>preprocessingTime</th><th scope=col>Solution0FoundAt</th><th scope=col>additionalTime</th><th scope=col>TransformationExecutionTime</th><th scope=col>ForwardTime</th><th scope=col>BacktrackingTime</th><th scope=col>GlobalConstraintEvaluationTime</th><th scope=col>FitnessCalculationTime</th><th scope=col>ActivationSelectionTime</th><th scope=col>SolutionCopyTime</th><th scope=col>NumericalSolverSumTime</th><th scope=col>NumericalSolverProblemFormingTime</th><th scope=col>NumericalSolverSolvingTime</th><th scope=col>NumericalSolverInterpretingSolution</th></tr>\n",
"\t<tr><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th><th scope=col><dbl></th></tr>\n",
"</thead>\n",
"<tbody>\n",
"\t<tr><td>20</td><td> 1</td><td>1038</td><td>5248</td><td>3980</td><td>31</td><td>225</td><td>108</td><td>10</td><td>1</td><td>0</td><td>1</td><td>4675</td><td>2239</td><td>2239</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 2</td><td> 724</td><td>4132</td><td>3628</td><td>20</td><td>155</td><td> 76</td><td> 6</td><td>1</td><td>0</td><td>0</td><td>3780</td><td>2019</td><td>2019</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 3</td><td> 735</td><td>5329</td><td>4437</td><td>20</td><td>195</td><td>106</td><td> 6</td><td>2</td><td>0</td><td>0</td><td>4876</td><td>2588</td><td>2588</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 4</td><td> 694</td><td>4261</td><td>4920</td><td>16</td><td>150</td><td> 69</td><td> 5</td><td>1</td><td>0</td><td>0</td><td>3925</td><td>2085</td><td>2085</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 5</td><td> 888</td><td>5959</td><td>5086</td><td>18</td><td>200</td><td> 93</td><td> 8</td><td>1</td><td>0</td><td>0</td><td>5490</td><td>2946</td><td>2946</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 6</td><td> 665</td><td>6310</td><td>4320</td><td>17</td><td>195</td><td> 90</td><td> 6</td><td>1</td><td>0</td><td>0</td><td>5870</td><td>3167</td><td>3167</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 7</td><td> 604</td><td>5024</td><td>5738</td><td>14</td><td>165</td><td> 74</td><td> 4</td><td>1</td><td>0</td><td>0</td><td>4662</td><td>2464</td><td>2464</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 8</td><td> 589</td><td>5733</td><td>3917</td><td>15</td><td>181</td><td> 81</td><td> 4</td><td>1</td><td>0</td><td>0</td><td>5337</td><td>2827</td><td>2827</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td> 9</td><td> 705</td><td>4719</td><td>4259</td><td>14</td><td>156</td><td> 71</td><td> 4</td><td>1</td><td>0</td><td>2</td><td>4367</td><td>2390</td><td>2390</td><td>0</td></tr>\n",
"\t<tr><td>20</td><td>10</td><td> 554</td><td>4061</td><td>3990</td><td>13</td><td>141</td><td> 68</td><td> 3</td><td>1</td><td>0</td><td>0</td><td>3741</td><td>1991</td><td>1991</td><td>0</td></tr>\n",
"</tbody>\n",
"</table>\n"
],
"text/latex": [
"A tibble: 10 × 16\n",
"\\begin{tabular}{llllllllllllllll}\n",
" n & Run & preprocessingTime & Solution0FoundAt & additionalTime & TransformationExecutionTime & ForwardTime & BacktrackingTime & GlobalConstraintEvaluationTime & FitnessCalculationTime & ActivationSelectionTime & SolutionCopyTime & NumericalSolverSumTime & NumericalSolverProblemFormingTime & NumericalSolverSolvingTime & NumericalSolverInterpretingSolution\\\\\n",
" <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
"\\hline\n",
"\t 20 & 1 & 1038 & 5248 & 3980 & 31 & 225 & 108 & 10 & 1 & 0 & 1 & 4675 & 2239 & 2239 & 0\\\\\n",
"\t 20 & 2 & 724 & 4132 & 3628 & 20 & 155 & 76 & 6 & 1 & 0 & 0 & 3780 & 2019 & 2019 & 0\\\\\n",
"\t 20 & 3 & 735 & 5329 & 4437 & 20 & 195 & 106 & 6 & 2 & 0 & 0 & 4876 & 2588 & 2588 & 0\\\\\n",
"\t 20 & 4 & 694 & 4261 & 4920 & 16 & 150 & 69 & 5 & 1 & 0 & 0 & 3925 & 2085 & 2085 & 0\\\\\n",
"\t 20 & 5 & 888 & 5959 & 5086 & 18 & 200 & 93 & 8 & 1 & 0 & 0 & 5490 & 2946 & 2946 & 0\\\\\n",
"\t 20 & 6 & 665 & 6310 & 4320 & 17 & 195 & 90 & 6 & 1 & 0 & 0 & 5870 & 3167 & 3167 & 0\\\\\n",
"\t 20 & 7 & 604 & 5024 & 5738 & 14 & 165 & 74 & 4 & 1 & 0 & 0 & 4662 & 2464 & 2464 & 0\\\\\n",
"\t 20 & 8 & 589 & 5733 & 3917 & 15 & 181 & 81 & 4 & 1 & 0 & 0 & 5337 & 2827 & 2827 & 0\\\\\n",
"\t 20 & 9 & 705 & 4719 & 4259 & 14 & 156 & 71 & 4 & 1 & 0 & 2 & 4367 & 2390 & 2390 & 0\\\\\n",
"\t 20 & 10 & 554 & 4061 & 3990 & 13 & 141 & 68 & 3 & 1 & 0 & 0 & 3741 & 1991 & 1991 & 0\\\\\n",
"\\end{tabular}\n"
],
"text/markdown": [
"\n",
"A tibble: 10 × 16\n",
"\n",
"| n <dbl> | Run <dbl> | preprocessingTime <dbl> | Solution0FoundAt <dbl> | additionalTime <dbl> | TransformationExecutionTime <dbl> | ForwardTime <dbl> | BacktrackingTime <dbl> | GlobalConstraintEvaluationTime <dbl> | FitnessCalculationTime <dbl> | ActivationSelectionTime <dbl> | SolutionCopyTime <dbl> | NumericalSolverSumTime <dbl> | NumericalSolverProblemFormingTime <dbl> | NumericalSolverSolvingTime <dbl> | NumericalSolverInterpretingSolution <dbl> |\n",
"|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
"| 20 | 1 | 1038 | 5248 | 3980 | 31 | 225 | 108 | 10 | 1 | 0 | 1 | 4675 | 2239 | 2239 | 0 |\n",
"| 20 | 2 | 724 | 4132 | 3628 | 20 | 155 | 76 | 6 | 1 | 0 | 0 | 3780 | 2019 | 2019 | 0 |\n",
"| 20 | 3 | 735 | 5329 | 4437 | 20 | 195 | 106 | 6 | 2 | 0 | 0 | 4876 | 2588 | 2588 | 0 |\n",
"| 20 | 4 | 694 | 4261 | 4920 | 16 | 150 | 69 | 5 | 1 | 0 | 0 | 3925 | 2085 | 2085 | 0 |\n",
"| 20 | 5 | 888 | 5959 | 5086 | 18 | 200 | 93 | 8 | 1 | 0 | 0 | 5490 | 2946 | 2946 | 0 |\n",
"| 20 | 6 | 665 | 6310 | 4320 | 17 | 195 | 90 | 6 | 1 | 0 | 0 | 5870 | 3167 | 3167 | 0 |\n",
"| 20 | 7 | 604 | 5024 | 5738 | 14 | 165 | 74 | 4 | 1 | 0 | 0 | 4662 | 2464 | 2464 | 0 |\n",
"| 20 | 8 | 589 | 5733 | 3917 | 15 | 181 | 81 | 4 | 1 | 0 | 0 | 5337 | 2827 | 2827 | 0 |\n",
"| 20 | 9 | 705 | 4719 | 4259 | 14 | 156 | 71 | 4 | 1 | 0 | 2 | 4367 | 2390 | 2390 | 0 |\n",
"| 20 | 10 | 554 | 4061 | 3990 | 13 | 141 | 68 | 3 | 1 | 0 | 0 | 3741 | 1991 | 1991 | 0 |\n",
"\n"
],
"text/plain": [
" n Run preprocessingTime Solution0FoundAt additionalTime\n",
"1 20 1 1038 5248 3980 \n",
"2 20 2 724 4132 3628 \n",
"3 20 3 735 5329 4437 \n",
"4 20 4 694 4261 4920 \n",
"5 20 5 888 5959 5086 \n",
"6 20 6 665 6310 4320 \n",
"7 20 7 604 5024 5738 \n",
"8 20 8 589 5733 3917 \n",
"9 20 9 705 4719 4259 \n",
"10 20 10 554 4061 3990 \n",
" TransformationExecutionTime ForwardTime BacktrackingTime\n",
"1 31 225 108 \n",
"2 20 155 76 \n",
"3 20 195 106 \n",
"4 16 150 69 \n",
"5 18 200 93 \n",
"6 17 195 90 \n",
"7 14 165 74 \n",
"8 15 181 81 \n",
"9 14 156 71 \n",
"10 13 141 68 \n",
" GlobalConstraintEvaluationTime FitnessCalculationTime\n",
"1 10 1 \n",
"2 6 1 \n",
"3 6 2 \n",
"4 5 1 \n",
"5 8 1 \n",
"6 6 1 \n",
"7 4 1 \n",
"8 4 1 \n",
"9 4 1 \n",
"10 3 1 \n",
" ActivationSelectionTime SolutionCopyTime NumericalSolverSumTime\n",
"1 0 1 4675 \n",
"2 0 0 3780 \n",
"3 0 0 4876 \n",
"4 0 0 3925 \n",
"5 0 0 5490 \n",
"6 0 0 5870 \n",
"7 0 0 4662 \n",
"8 0 0 5337 \n",
"9 0 2 4367 \n",
"10 0 0 3741 \n",
" NumericalSolverProblemFormingTime NumericalSolverSolvingTime\n",
"1 2239 2239 \n",
"2 2019 2019 \n",
"3 2588 2588 \n",
"4 2085 2085 \n",
"5 2946 2946 \n",
"6 3167 3167 \n",
"7 2464 2464 \n",
"8 2827 2827 \n",
"9 2390 2390 \n",
"10 1991 1991 \n",
" NumericalSolverInterpretingSolution\n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"5 0 \n",
"6 0 \n",
"7 0 \n",
"8 0 \n",
"9 0 \n",
"10 0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "ERROR",
"evalue": "Error: Problem with `summarise()` input `StateCoderTime`.\n\u001b[31m✖\u001b[39m object 'StateCoderTime' not found\n\u001b[34mℹ\u001b[39m Input `StateCoderTime` is `median(StateCoderTime)/1000`.\n\u001b[34mℹ\u001b[39m The error occurred in group 1: n = 20.\n",
"output_type": "error",
"traceback": [
"Error: Problem with `summarise()` input `StateCoderTime`.\n\u001b[31m✖\u001b[39m object 'StateCoderTime' not found\n\u001b[34mℹ\u001b[39m Input `StateCoderTime` is `median(StateCoderTime)/1000`.\n\u001b[34mℹ\u001b[39m The error occurred in group 1: n = 20.\nTraceback:\n",
"1. FamilyTreeRQ2Raw %>% ProcessRQ2",
"2. ProcessRQ2(.)",
"3. df %>% group_by(n) %>% summarize(.groups = \"drop\", preprocessingTime = median(preprocessingTime)/1000, \n . StateCoderTime = median(StateCoderTime)/1000, ForwardTime = median(ForwardTime)/1000, \n . BacktrackingTime = median(BacktrackingTime)/1000, NumericalSolverSumTime = median(NumericalSolverSumTime)/1000, \n . additionalTime = median(additionalTime)/1000) # at line 8-16 of file <text>",
"4. summarize(., .groups = \"drop\", preprocessingTime = median(preprocessingTime)/1000, \n . StateCoderTime = median(StateCoderTime)/1000, ForwardTime = median(ForwardTime)/1000, \n . BacktrackingTime = median(BacktrackingTime)/1000, NumericalSolverSumTime = median(NumericalSolverSumTime)/1000, \n . additionalTime = median(additionalTime)/1000)",
"5. summarise.grouped_df(., .groups = \"drop\", preprocessingTime = median(preprocessingTime)/1000, \n . StateCoderTime = median(StateCoderTime)/1000, ForwardTime = median(ForwardTime)/1000, \n . BacktrackingTime = median(BacktrackingTime)/1000, NumericalSolverSumTime = median(NumericalSolverSumTime)/1000, \n . additionalTime = median(additionalTime)/1000)",
"6. summarise_cols(.data, ...)",
"7. withCallingHandlers({\n . for (i in seq_along(dots)) {\n . quo <- dots[[i]]\n . chunks[[i]] <- mask$eval_all_summarise(quo)\n . mask$across_cache_reset()\n . result_type <- types[[i]] <- withCallingHandlers(vec_ptype_common(!!!chunks[[i]]), \n . vctrs_error_incompatible_type = function(cnd) {\n . abort(class = \"dplyr:::error_summarise_incompatible_combine\", \n . parent = cnd)\n . })\n . if ((is.null(dots_names) || dots_names[i] == \"\") && is.data.frame(result_type)) {\n . map2(seq_along(result_type), names(result_type), \n . function(j, nm) {\n . mask$add(nm, pluck(chunks[[i]], j))\n . })\n . }\n . else {\n . mask$add(auto_named_dots[i], chunks[[i]])\n . }\n . }\n . recycle_info <- .Call(dplyr_summarise_recycle_chunks, chunks, \n . mask$get_rows(), types)\n . chunks <- recycle_info$chunks\n . sizes <- recycle_info$sizes\n . for (i in seq_along(dots)) {\n . result <- vec_c(!!!chunks[[i]], .ptype = types[[i]])\n . if ((is.null(dots_names) || dots_names[i] == \"\") && is.data.frame(result)) {\n . cols[names(result)] <- result\n . }\n . else {\n . cols[[auto_named_dots[i]]] <- result\n . }\n . }\n . }, error = function(e) {\n . local_call_step(dots = dots, .index = i, .fn = \"summarise\", \n . .dot_data = inherits(e, \"rlang_error_data_pronoun_not_found\"))\n . call_step <- peek_call_step()\n . error_name <- call_step$error_name\n . if (inherits(e, \"dplyr:::error_summarise_incompatible_combine\")) {\n . bullets <- c(x = glue(\"Input `{error_name}` must return compatible vectors across groups\", \n . .envir = peek_call_step()), i = cnd_bullet_combine_details(e$parent$x, \n . e$parent$x_arg), i = cnd_bullet_combine_details(e$parent$y, \n . e$parent$y_arg))\n . }\n . else if (inherits(e, \"dplyr:::summarise_unsupported_type\")) {\n . bullets <- c(x = glue(\"Input `{error_name}` must be a vector, not {friendly_type_of(result)}.\", \n . result = e$result), i = cnd_bullet_rowwise_unlist())\n . }\n . else if (inherits(e, \"dplyr:::summarise_incompatible_size\")) {\n . peek_mask()$set_current_group(e$group)\n . bullets <- c(x = glue(\"Input `{error_name}` must be size {or_1(expected_size)}, not {size}.\", \n . expected_size = e$expected_size, size = e$size), \n . i = glue(\"An earlier column had size {expected_size}.\", \n . expected_size = e$expected_size))\n . }\n . else {\n . bullets <- c(x = conditionMessage(e))\n . }\n . bullets <- c(cnd_bullet_header(), bullets, i = cnd_bullet_input_info())\n . if (!inherits(e, \"dplyr:::error_summarise_incompatible_combine\")) {\n . bullets <- c(bullets, i = cnd_bullet_cur_group_label())\n . }\n . abort(bullets, class = \"dplyr_error\")\n . })",
"8. mask$eval_all_summarise(quo)",
"9. median(StateCoderTime)",
"10. .handleSimpleError(function (e) \n . {\n . local_call_step(dots = dots, .index = i, .fn = \"summarise\", \n . .dot_data = inherits(e, \"rlang_error_data_pronoun_not_found\"))\n . call_step <- peek_call_step()\n . error_name <- call_step$error_name\n . if (inherits(e, \"dplyr:::error_summarise_incompatible_combine\")) {\n . bullets <- c(x = glue(\"Input `{error_name}` must return compatible vectors across groups\", \n . .envir = peek_call_step()), i = cnd_bullet_combine_details(e$parent$x, \n . e$parent$x_arg), i = cnd_bullet_combine_details(e$parent$y, \n . e$parent$y_arg))\n . }\n . else if (inherits(e, \"dplyr:::summarise_unsupported_type\")) {\n . bullets <- c(x = glue(\"Input `{error_name}` must be a vector, not {friendly_type_of(result)}.\", \n . result = e$result), i = cnd_bullet_rowwise_unlist())\n . }\n . else if (inherits(e, \"dplyr:::summarise_incompatible_size\")) {\n . peek_mask()$set_current_group(e$group)\n . bullets <- c(x = glue(\"Input `{error_name}` must be size {or_1(expected_size)}, not {size}.\", \n . expected_size = e$expected_size, size = e$size), \n . i = glue(\"An earlier column had size {expected_size}.\", \n . expected_size = e$expected_size))\n . }\n . else {\n . bullets <- c(x = conditionMessage(e))\n . }\n . bullets <- c(cnd_bullet_header(), bullets, i = cnd_bullet_input_info())\n . if (!inherits(e, \"dplyr:::error_summarise_incompatible_combine\")) {\n . bullets <- c(bullets, i = cnd_bullet_cur_group_label())\n . }\n . abort(bullets, class = \"dplyr_error\")\n . }, \"object 'StateCoderTime' not found\", base::quote(median(StateCoderTime)))",
"11. h(simpleError(msg, call))",
"12. abort(bullets, class = \"dplyr_error\")",
"13. signal_abort(cnd)"
]
}
],
"source": [
"FamilyTreeRQ2Raw <- rbind(\n",
"# Load10Log(\"measurements/stats/FamilyTree//size010to-1r10n10rt300nsdrealstats_06-0249.csv\", 10),\n",
" Load10Log(\"measurements/stats/FamilyTree//size020to-1r10n10rt3600nsz3stats_06-0205.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": []
}
],
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|
