62 lines
2.3 KiB
R
62 lines
2.3 KiB
R
# Compairson of differend dimension reduction (dr) methods over all datasets
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# with default parametrs.
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#
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# Results are written to a CSV file with a dataset column and its simulation nr.
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#
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# Note:
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# All methods are called via interface functions defined in
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# 'simulation_interfaces.R'. In addition this file provides a few convenience
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# functions, namely: 'progress_logger' and 'subspace_dist'.
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#
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# Dependencies (list of packages loaded):
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# - MASS
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# - dr
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# - MAVE
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# - CVE
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source('simulation_interfaces.R') # depends on MASS, dr, MAVE and CVE
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# Number of simulations
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NR.SIM <- 100L
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DS <- 1L:7L # datasets run from 1 to 7.
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# Methods for comparison, see 'simulation_interfaces.R' for their definitions.
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methods <- c('CVE', 'wCVE', 'rCVE', # CVE variants
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'meanOPG', 'rOPG', # OPG variants
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'meanMAVE', 'rmave', # MAVE variants
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'phdy', 'sir', 'save') # Methods from dr package
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# Build result data matrix (repeat each dataset simulation times).
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result <- matrix(NA, nrow = length(DS) * NR.SIM,
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ncol = length(methods) + 1) # +1 for dataset column.
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result[, 1L] <- rep(paste0("M", DS), NR.SIM)
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colnames(result) <- c("dataset", methods)
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# Create a progress logger for length many reports.
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logger <- progress_logger(length(DS) * NR.SIM * length(methods))
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for (sim in seq_len(nrow(result))) {
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# Create new dataset.
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with(dataset(result[sim, 1L]), { X <<- X; Y <<- Y; B <<- B })
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# And each reference method.
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for (method in methods) {
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# Report simulation progress to user.
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logger(method)
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# Try/Catch to avoid simulation stop on error, this should not
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# accure for CVE but may be the case for others like OPG.
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tryCatch({
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# Call dimension reduction method.
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dr <- eval(call(method, X = X, Y = Y, k = ncol(B)))
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# Compute distance of estimated `B` to true `B`.
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result[sim, method] <- subspace_dist(B, coef(dr, ncol(B)))
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}, error = function(e) {}) # No-Operation error handler!
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}
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# explicit call to the garbage collector.
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gc()
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}
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path <- paste0(getwd(),
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format(Sys.time(), '/results/method_compair_%Y-%m-%dT%H%M%S'))
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# Write entire simulation results into a single file.
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write.csv(result, file = paste0(path, ".csv"), row.names = FALSE)
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