individual.R

library(data.table)
library(reshape2)
library(plyr)
library(ggplot2)
library(ggrepel)
library(arules)
library(ggforce)

source('util.R')

# constants
workloads = c(
  "PosLength",  "SwitchMonitored",
  "RouteSensor", "SwitchSet",
  "ConnectedSegments", "SemaphoreNeighbor"
)
phases = c("Read", "Check", "Read.and.Check", "Transformation", "Recheck", "Transformation.and.Recheck")
phasesPrettified = c("Read", "Check", "Read and Check", "Transformation", "Recheck", "Transformation and Recheck")

sizes = list()      # 1     2      4      8      16      32      64      128     256     512     1024    2048   4096
sizes[["Repair"]] = c("8k", "15k", "33k", "66k", "135k", "271k", "566k", "1.1M", "2.2M", "4.6M", "9.3M", "18M", "37M")

toolList = read.csv("tool-list.csv", colClasses=c(rep("character",1)))

# load the data
tsvs = list.files("../results/recent", pattern = "times-.*\\.csv", full.names = T, recursive = T)

l = lapply(tsvs, read.csv)
times = rbindlist(l)

# preprocess the data
times$Tool = factor(times$Tool, levels = toolList$Tool)
keep_descriptions_first_char(times)

times$Model = gsub("\\D+", "", times$Model)
times$Model = as.numeric(times$Model)
times$Time = times$Time / 10^6
# make the phases a factor with a fixed set of values to help dcasting
# (e.g. Batch measurements do not have Transformation and Recheck attributes,
# hence accessing the "Transformation" attribute would throw an error)
times$Phase = factor(times$Phase, levels = c("Read", "Check", "Transformation", "Recheck"))

times.wide = dcast(data = times,
                   formula = Tool + Workload + Description + Model + Run ~ Phase,
                   value.var = "Time",
                   drop = T,
                   fun.aggregate = mean
)

# calculate aggregated values
times.derived = times.wide
times.derived$Read.and.Check = times.derived$Read + times.derived$Check
times.derived$Transformation.and.Recheck = times.derived$Transformation + times.derived$Recheck

# calculate the median value of runs
times.aggregated.runs = ddply(
  .data = times.derived,
  .variables = c("Tool", "Workload", "Description", "Model"),
  .fun = colwise(median),
  .progress = "text"
)
# drop the "Run" column
times.aggregated.runs = subset(times.aggregated.runs, select = -c(Run))

times.processed = melt(
  data = times.aggregated.runs,
  id.vars = c("Tool", "Workload", "Description", "Model"),
  measure.vars = phases,
  variable.name = "Phase",
  value.name = "Time"
)

# beautify plotted record:
# 1. change dots to spaces
# 2. make sure that the phases are still factors
times.plot = times.processed
times.plot$Phase = gsub('\\.', ' ', times.plot$Phase)
times.plot$Phase = factor(times.plot$Phase, levels = phasesPrettified)
times.plot$Workload = factor(times.plot$Workload, levels = workloads)

### line charts
for (phase in phasesPrettified) {
  phase.filename = gsub(' ', '-', phase)
  workloadSizes = sizes[["Repair"]]

  # filter the dataframe to the current phase
  df = times.plot[times.plot$Phase == phase, ]

  # do not visualize empty data sets
  if (nrow(df) == 0) {
    print(paste("No rows to visualize for phase", phase))
    next
  }

  # x axis labels
  xbreaks = unique(df$Model)
  currentWorkloadSizes = head(workloadSizes, n=length(xbreaks))
  xlabels = paste(xbreaks, "\n", currentWorkloadSizes, sep = "")

  # drop every other models size
  maxLabel = max(log2(max(df$Model)), 2)
  if (maxLabel %% 2) {
    start = 3
  } else {
    start = 2
  }
  filter = seq(start, maxLabel, by=2)

  xlabels[filter] = ""

  # y axis labels
  yaxis = nice_y_axis()
  ybreaks = yaxis$ybreaks
  yminor_breaks = yaxis$yminor_breaks
  ylabels = yaxis$ylabels

  for (cpage in 1:6) {
    p = ggplot(df) + #na.omit(df)) +
      aes(x = as.factor(Model), y = Time) +
      labs(title = paste("Individual query execution time,", phase, "phase"), x = "Model size\n#Elements", y = "Execution times [ms]") +
      geom_point(aes(col = Tool, shape = Tool), size = 2.0) +
      # scale_shape_manual(values = seq(0, 15)) +
      scale_shape_manual(values = c(0, 15, 1, 16, 2, 17, 4, 7)) +
      scale_colour_manual(values = c(
          "#56B4E9", "#56B4E9", # Sky blue - JastAdd NameLookup
          "#009E73", "#009E73", # Bluish green - JastAdd Optimized
          "#0072B2", "#0072B2", # Blue - JastAdd Specialized
          "#E69F00", # Orange - TinkerGraph
          "#D55E00", # Vermillion - VIATRA
          "#F0E442", # Yellow
          "#CC79A7")) +    
      geom_line(aes(col = Tool, group = Tool), size = 0.5) +
      scale_x_discrete(breaks = xbreaks, labels = xlabels) +
      scale_y_log10(breaks = ybreaks, minor_breaks=yminor_breaks, labels = ylabels) +
      facet_grid_paginate(~ Workload, nrow=1, ncol = 1, page=cpage, scale = "free") +
      guides(color = guide_legend(ncol = 4)) +
      theme_bw() +
      theme(
        plot.title = element_text(hjust = 0.5),
        text = element_text(size = 10),
        legend.key = element_blank(),
        legend.title = element_blank(),
        legend.position = "bottom",
        axis.text = element_text(size = 9)
	#, panel.grid.minor = element_blank()
      )
    print(p)
    ggsave(
      plot = p,
      filename = paste("../diagrams/recent/individual-", phase.filename, "-",workloads[cpage], ".pdf", sep=""),
      width = 280, height = 210, units = "mm"
    )
  }

}