93 lines
3.5 KiB
R
93 lines
3.5 KiB
R
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library(ggplot2)
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################################################################################
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### General Helpers ###
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################################################################################
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# Read/Combine simulation data logs
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read.logs <- function(pattern) {
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# Folder containing log files
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path <- if (file.exists('results/')) './' else './simulations/'
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path <- paste0(path, 'results/')
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# Read all log files and augment with meta-parameters
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file.names <- list.files(path, pattern, full.names = TRUE)
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sim <- do.call(rbind, lapply(file.names, function(path) {
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# Read simulation log (one file)
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sim <- read.csv(path, comment.char = '#')
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# Add simulation arguments as columns
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args <- Filter(function(line) startsWith(line, '#'), readLines(path))
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args <- sub('# ?', '', args)
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args <- regmatches(args, regexpr(' ', args), invert = TRUE)
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# Try to convert meta-parameters from string into int/num/bool/...
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for (arg in args) {
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val <- tryCatch(
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eval(parse(text = arg[2])),
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error = function(err) arg[2]
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)
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if (length(val) > 1) val <- paste(val, collapse = ',')
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sim[[arg[1]]] <- val
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}
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sim
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}))
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# Convert methods to factors
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sim$method <- factor(sim$method,
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levels = c("opg", "mave", "cve", "sir", "save", "phdy", "nn.opg", "nn.ref"),
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labels = c("OPG", "MAVE", "CVE", "SIR", "SAVE", "PHD", "NN-OPG", "NN-Ref")
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)
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sim
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}
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################################################################################
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### Bit Data ###
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################################################################################
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# Read/Combine big data simulation logs
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sim <- read.logs('sim_big_.*csv')
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# Compute repetition mean and standard deviation over replications
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(aggr <- merge(
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aggregate(dist.subspace ~ dataset + n + p + method, sim, mean),
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aggregate(dist.subspace ~ dataset + n + p + method, sim, sd),
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by = c("dataset", "n", "p", "method"),
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suffixes = c(".mean", ".sd")
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))
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# plots and tables
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ggplot(aggr, aes(x = n, y = dist.subspace.mean,
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group = interaction(dataset, method),
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color = method, linetype = dataset)) +
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geom_line() +
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geom_errorbar(aes(
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ymin = dist.subspace.mean - dist.subspace.sd,
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ymax = dist.subspace.mean + dist.subspace.sd
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), width = 0.2) +
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scale_x_continuous(trans = 'log2')
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################################################################################
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### Binary Response ###
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################################################################################
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# Read/Combine binary data simulation logs
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sim <- read.logs('sim_binary_[0-9_]*\\.csv')
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# Aggregated Tables
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aggr.formula <- cbind(dist.subspace, accuracy) ~ dataset + method
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aggr <- merge(
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aggregate(aggr.formula, sim, mean,
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na.action = na.pass),
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aggregate(aggr.formula, sim, sd,
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na.action = na.pass),
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by = attr(terms(aggr.formula), "term.labels"),
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suffixes = c(".mean", ".sd")
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)
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print(aggr[with(aggr, order(dataset, dist.subspace.mean)), ], digits = 3)
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# box-plot subspace comparison
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ggplot(sim, aes(x = method, y = dist.subspace,
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group = interaction(dataset, method),
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color = dataset)) +
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geom_boxplot() +
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labs(title = "Sim. Binary", x = "Methods", y = "Subspace Dist.", color = "Datasets")
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