R/OmicSelector_OmicSelector.R
In kstawiski/OmicSelector: OmicSelector - a package for biomarker selection based on high-throughput experiments.
Defines functions
OmicSelector_OmicSelector
Documented in
OmicSelector_OmicSelector
#' OmicSelector_OmicSelector
#'
#' Main function of the package. The aim of this function is to perform feature selection using multiple methods and to create formulas for benchmarking.
#' It loads the data from working directory. The output is mainly created in files in working directory. Log and temporary files are placed in created `temp` subfolder.
#' This package offers about 60 feature selection methods. Which methods will be check by this function is defined by `m` parameter.
#' Pearls about the methods:
#'
#' - Sig = miRNAs with p-value <0.05 after BH correction (DE using t-test)
#' - Fcsig = sig + absolute log2FC filter (included if abs. log2FC>1)
#' - Cfs = Correlation-based Feature Selection for Machine Learning (more: https://www.cs.waikato.ac.nz/~mhall/thesis.pdf)
#' - Classloop = Classification using different classification algorithms (classifiers) with the embedded feature selection and using the different schemes for the performance validation (more: https://rdrr.io/cran/Biocomb/man/classifier.loop.html)
#' - Fcfs = CFS algorithm with forward search (https://rdrr.io/cran/Biocomb/man/select.forward.Corr.html)
#' - MDL methods = minimal description length (MDL) discretization algorithm with different a method of feature ranking or feature selection (AUC, SU, CorrSF) (more: https://rdrr.io/cran/Biocomb/man/select.process.html)
#' - bounceR = genetic algorithm with componentwise boosting (more: https://www.statworx.com/ch/blog/automated-feature-selection-using-bouncer/)
#' - RandomForestRFE = recursive feature elimination using random forest with resampling to assess the performance. (more: https://topepo.github.io/caret/recursive-feature-elimination.html#resampling-and-external-validation)
#' - GeneticAlgorithmRF (more: https://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html)
#' - SimulatedAnnealing = makes small random changes (i.e. perturbations) to an initial candidate solution (more: https://topepo.github.io/caret/feature-selection-using-simulated-annealing.html)
#' - Boruta (more: https://www.jstatsoft.org/article/view/v036i11/v36i11.pdf)
#' - spFSR = simultaneous perturbation stochastic approximation (SPSA-FSR) (more: https://arxiv.org/abs/1804.05589)
#' - varSelRF = using the out-of-bag error as minimization criterion, carry out variable elimination from random forest, by successively eliminating the least important variables (with importance as returned from random forest). (more: https://www.ncbi.nlm.nih.gov/pubmed/16398926)
#' - WxNet = a neural network-based feature selection algorithm for transcriptomic data (more: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642261/)
#' - Step = backward stepwise method of feature selection based on logistic regression (GLM, family = binomial) using AIC criteria (stepAIC) and functions from My.stepwise package (https://cran.r-project.org/web/packages/My.stepwise/index.html)
#'
#' For more detailed defitions please see the tutorial vignette.
#'
#' @param wd Working directory with data (`mixed_train.csv`, `mixed_test.csv` and `mixed_validation.csv` as created by `OmicSelector_prepare_split` have to be present).
#' @param m Methods of feature selection to be performed. This has to be a vector of integers with minimum of 1 and maximum of 70. For the definition of numbers please see the vignette.
#' @param max_iterations Maximum number of iterations in selected methods. Setting this too high may results in very long comupting time.
#' @param code_path A folder where the python external scripts are placed (especially for WxNet method). By default the additional code is provided in the package.
#' @param register_parallel Where to use parallel processing to speed up computing time. Seting it to FALSE may aid in debuging.
#' @param clx This parameter may be used for passing the already register computing cluster (created and registered with `doParallel` tools). This may lower the computing time by saving the time to register new cluster.
#' @param stamp A character vector or timestamp used for marking the output files.
#' @param prefer_no_features Maximum number of miRNAs that can be selected by the tools if the method allows for that.
#' @param conda_path Patch to "conda" bindary used for executing python scripts.
#' @param debug Gives additional debug information (saves .rdata after feature selection is completed, prints formulas to log)
#' @param timeout_sec Timeout after the method is terminated if not finished. It may be useful to keep the long methods limited, not to wait ethernity for the results.
#' @param type Parameter 'mode' forwarded to OmicSelector_differential_expression_ttest which is essential in many feature selection methods. Note that if 'var_type.txt' file exists in the working directory it is superior to the value set directly in function - as designed for GUI. Please refer to OmicSelector_differential_expression_ttest manual to understand how 'mode' worOmicSelector_
#'
#' @return The list of selected formulas. Note that, due to purpose of this package `OmicSelector_merge_formulas` may be a better option to get the output of processes run by this function.
#' @examples
#' # NOT RUN: (to speed up check, but this is a valid example for your real time projects)
#' # suppressMessages(library(foreach))
#' # suppressMessages(library(doParallel))
#' # suppressMessages(library(parallel))
#' # suppressMessages(library(doParallel))
#' # m = 1:56 # which methods to check?
#' # cl <- makePSOCKcluster(useXDR = TRUE, 5) # 5 threds by default
#' # doParallel:: registerDoParallel(cl)
#' # iterations = length(m)
#' # pb <- txtProgressBar(max = iterations, style = 3)
#' # progress <- function(n) setTxtProgressBar(pb, n)
#' # opts <- list(progress = progress)
#' # foreach(i = m, .verbose = TRUE, .options.snow = opts) %dopar%
#' # {
#' # suppressMessages(library(OmicSelector))
#' # setwd("~/public/Projekty/KS/OmicSelector/vignettes") # change it you to your working directory
#' # OmicSelector_OmicSelector(m = i, max_iterations = 1, stamp = "tutorial", debug = T) # we set debug to get more output
#' # }
#' # stopCluster(cl)
#'
#' @import snow doParallel
#'
#' @export
OmicSelector_OmicSelector = function(wd = getwd(), m = c(1:70),
max_iterations = 10, code_path = system.file("extdata", "", package = "OmicSelector"),
register_parallel = T, clx = NULL, stamp = as.numeric(Sys.time()),
prefer_no_features = 11, conda_path = "/home/konrad/anaconda3/bin/conda", debug = F,
timeout_sec = 172800, type = "auto") {
oldwd = getwd()
setwd(wd)
suppressMessages(library(plyr))
suppressMessages(library(dplyr))
suppressMessages(library(edgeR))
suppressMessages(library(epiDisplay))
suppressMessages(library(rsq))
suppressMessages(library(MASS))
suppressMessages(library(Biocomb))
suppressMessages(library(caret))
suppressMessages(library(dplyr))
suppressMessages(library(epiDisplay))
suppressMessages(library(pROC))
suppressMessages(library(ggplot2))
suppressMessages(library(DMwR))
suppressMessages(library(ROSE))
suppressMessages(library(gridExtra))
suppressMessages(library(gplots))
suppressMessages(library(devtools))
suppressMessages(library(stringr))
suppressMessages(library(data.table))
suppressMessages(library(tidyverse))
suppressMessages(library(R.utils))
suppressMessages(library(doParallel))
if(!dir.exists("temp")) { dir.create("temp") }
run_id = stamp
formulas = list()
times = list()
zz <- file(paste0("temp/",stamp,paste0(m, collapse = "+"),"featureselection.log"), open = "wt")
sink(zz)
#sink(zz, type = "message")
#pdf(paste0("temp/",stamp,paste0(m, collapse = "+"),"featureselection.pdf"))
# not_needed as set to auto
# if(file.exists("var_type.txt")) { type = readLines("var_type.txt", warn = F) }
wynik_finalny = withTimeout({
dane = OmicSelector_load_datamix(); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# n = 1
n= 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Staring method n = ", n, ". Processing...")); OmicSelector_log(logfile = "temp/featureselection.log");
start_time <- Sys.time()
formulas[["all"]] = OmicSelector_create_formula(colnames(trainx))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Standard DE...")
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value` <= 0.05) %>% arrange(`p-value`)
if(length(istotne$miR) == 0) { istotne = wyniki %>% arrange(`p-value`) } # what to do if none significant
istotne_top = wyniki %>% arrange(`p-value`) %>% head(prefer_no_features)
istotne_topBonf = wyniki %>% arrange(`p-value Bonferroni`) %>% head(prefer_no_features)
istotne_topHolm = wyniki %>% arrange(`p-value Holm`) %>% head(prefer_no_features)
istotne_topFC = wyniki %>% arrange(desc(abs(`log2FC`))) %>% head(prefer_no_features)
train_sig = dplyr::select(train, as.character(istotne$miR), Class)
trainx_sig = dplyr::select(train, as.character(istotne$miR))
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Preparing for SMOTE...")
#train_sig_smoted = DMwR::SMOTE(Class ~ ., data = train_sig, perc.over = 10000,perc.under=100, k=10)
train_sig_smoted = dplyr::select(train_smoted, as.character(istotne$miR), Class)
train_sig_smoted$Class = factor(train_sig_smoted$Class, levels = c("Control","Case"))
trainx_sig_smoted = dplyr::select(train_sig_smoted, starts_with("hsa"))
wyniki_smoted = OmicSelector_differential_expression_ttest(trainx_smoted, train_smoted$Class, mode = type)
istotne_smoted = filter(wyniki_smoted, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
if(length(istotne_smoted$miR) == 0) { istotne = wyniki_smoted %>% arrange(`p-value BH`) } # what to do if none significant
istotne_top_smoted = wyniki_smoted %>% arrange(`p-value BH`) %>% head(prefer_no_features)
istotne_topBonf_smoted = wyniki_smoted %>% arrange(`p-value Bonferroni`) %>% head(prefer_no_features)
istotne_topHolm_smoted = wyniki_smoted %>% arrange(`p-value Holm`) %>% head(prefer_no_features)
istotne_topFC_smoted = wyniki_smoted %>% arrange(desc(abs(`log2FC`))) %>% head(prefer_no_features)
# Caret prep
suppressMessages(library(doParallel))
registerDoSEQ()
if (register_parallel) {
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Getting cluster ready for parallel computations...")
if(is.null(clx)) {
cl <- makePSOCKcluster(useXDR = TRUE, detectCores())
registerDoParallel(cl)
#on.exit(stopCluster(cl))
}
else { registerDoParallel(clx)
#on.exit(stopCluster(clx))
}
}
# 0. All and sig
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Cluster prepared. Moving to method matching...")
# n = 2
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SIG")
start_time <- Sys.time()
formulas[["sig"]] = OmicSelector_create_formula(as.character(istotne$miR))
formulas[["sigtop"]] = OmicSelector_create_formula(as.character(istotne_top$miR))
formulas[["sigtopBonf"]] = OmicSelector_create_formula(as.character(istotne_topBonf$miR))
formulas[["sigtopHolm"]] = OmicSelector_create_formula(as.character(istotne_topHolm$miR))
formulas[["topFC"]] = OmicSelector_create_formula(as.character(istotne_topFC$miR))
formulas[["sigSMOTE"]] = OmicSelector_create_formula(as.character(istotne_smoted$miR))
formulas[["sigtopSMOTE"]] = OmicSelector_create_formula(as.character(istotne_top_smoted$miR))
formulas[["sigtopBonfSMOTE"]] = OmicSelector_create_formula(as.character(istotne_topBonf_smoted$miR))
formulas[["sigtopHolmSMOTE"]] = OmicSelector_create_formula(as.character(istotne_topHolm_smoted$miR))
formulas[["topFCSMOTE"]] = OmicSelector_create_formula(as.character(istotne_topFC_smoted$miR))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 3
# 1. Fold-change and sig. filter
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
start_time <- Sys.time()
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting FC and SIG")
fcsig = as.character(istotne$miR[abs(istotne$log2FC)>1])
formulas[["fcsig"]] = OmicSelector_create_formula(fcsig)
fcsig = as.character(istotne_smoted$miR[abs(istotne_smoted$log2FC)>1])
formulas[["fcsigSMOTE"]] = OmicSelector_create_formula(fcsig)
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 4
# 2. CFS
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
start_time <- Sys.time()
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting CFS")
cfs = select.cfs(train)
formulas[["cfs"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
cfs = select.cfs(train_smoted)
formulas[["cfsSMOTE"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
cfs = select.cfs(train_sig)
formulas[["cfs_sig"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
cfs = select.cfs(train_sig_smoted)
formulas[["cfsSMOTE_sig"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 5
# 3. classifier.loop
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
start_time <- Sys.time()
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting classifier loop")
classloop = classifier.loop(train, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloop"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 6
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
classloop = classifier.loop(train_smoted, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloopSMOTE"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 7
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
classloop = classifier.loop(train_sig, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloop_sig"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 8
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
classloop = classifier.loop(train_sig_smoted, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloopSMOTE_sig"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 9
# 4. select.forward.Corr
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting select.forward.Corr")
fcfs = select.forward.Corr(train, disc.method="MDL")
formulas[["fcfs"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 10
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fcfs = select.forward.Corr(train_smoted, disc.method="MDL")
formulas[["fcfsSMOTE"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 11
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fcfs = select.forward.Corr(train_sig, disc.method="MDL")
formulas[["fcfs_sig"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 12
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fcfs = select.forward.Corr(train_sig_smoted, disc.method="MDL")
formulas[["fcfsSMOTE_sig"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 13
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
# 5. select.forward.wrapper
fwrap = select.forward.wrapper(train)
formulas[["fwrap"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 14
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fwrap = select.forward.wrapper(train_smoted)
formulas[["fwrapSMOTE"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 15
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fwrap = select.forward.wrapper(train_sig)
formulas[["fwrap_sig"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 16
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fwrap = select.forward.wrapper(train_sig_smoted)
formulas[["fwrapSMOTE_sig"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# 6, 7, 8.
#select.process(dattable,method="InformationGain",disc.method="MDL",
# threshold=0.2,threshold.consis=0.05,attrs.nominal=numeric(),
# max.no.features=10)
# n = 17
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting MDL")
formulas[["AUC_MDL"]] = OmicSelector_create_formula(colnames(train)[select.process(train, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 18
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDL"]] = OmicSelector_create_formula(colnames(train)[select.process(train, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 19
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDL"]] = OmicSelector_create_formula(colnames(train)[select.process(train, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 20
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["AUC_MDLSMOTE"]] = OmicSelector_create_formula(colnames(train_smoted)[select.process(train_smoted, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 21
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDLSMOTE"]] = OmicSelector_create_formula(colnames(train_smoted)[select.process(train_smoted, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 22
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDLSMOTE"]] = OmicSelector_create_formula(colnames(train_smoted)[select.process(train_smoted, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 23
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["AUC_MDL_sig"]] = OmicSelector_create_formula(colnames(train_sig)[select.process(train_sig, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 24
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDL_sig"]] = OmicSelector_create_formula(colnames(train_sig)[select.process(train_sig, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 25
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDL_sig"]] = OmicSelector_create_formula(colnames(train_sig)[select.process(train_sig, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 26
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["AUC_MDLSMOTE_sig"]] = OmicSelector_create_formula(colnames(train_sig_smoted)[select.process(train_sig_smoted, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 27
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDLSMOTE_sig"]] = OmicSelector_create_formula(colnames(train_sig_smoted)[select.process(train_sig_smoted, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 28
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDLSMOTE_sig"]] = OmicSelector_create_formula(colnames(train_sig_smoted)[select.process(train_sig_smoted, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 29
# 9. bounceR - genetic
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting bounceR..")
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full"]] = mrmr@opt_formula
formulas[["bounceR-stability"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 30
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train_smoted,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full_SMOTE"]] = mrmr@opt_formula
formulas[["bounceR-stability_SMOTE"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 31
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train_sig,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full_SIG"]] = mrmr@opt_formula
formulas[["bounceR-stability_SIG"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 32
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train_sig_smoted,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full_SIGSMOTE"]] = mrmr@opt_formula
formulas[["bounceR-stability_SIGSMOTE"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 33
# 10. RFE RandomForest
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting RFE RF")
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx, train$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFE"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 34
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx_smoted, train_smoted$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFESMOTE"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 35
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx_sig, train_sig$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFE_sig"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 36
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx_sig_smoted, train_sig_smoted$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFESMOTE_sig"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 37
# 11. Genetic
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting Genetic")
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx, y = train$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRF"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 38
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx_smoted, y = train_smoted$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRFSMOTE"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 39
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx_sig, y = train_sig$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRF_sig"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 40
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx_sig_smoted, y = train_sig_smoted$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRFSMOTE_sig"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 41
# 12. SimulatedAnealing
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx, y = train$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRF"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 42
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx_smoted, y = train_smoted$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRFSMOTE"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 43
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx_sig, y = train_sig$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRF_sig"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 44
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx_sig_smoted, y = train_sig_smoted$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRFSMOTE_sig"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 45
# 14. Boruta (https://www.jstatsoft.org/article/view/v036i11)
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(Boruta))
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting Boruta")
bor = Boruta(trainx, train$Class)
formulas[["Boruta"]] = OmicSelector_create_formula(names(bor$finalDecision)[as.character(bor$finalDecision) == "Confirmed"])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 46
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
bor = Boruta(trainx_smoted, train_smoted$Class)
formulas[["BorutaSMOTE"]] = OmicSelector_create_formula(names(bor$finalDecision)[as.character(bor$finalDecision) == "Confirmed"])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 47
# 15. spFSR - feature selection and ranking by simultaneous perturbation stochastic approximation
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting spFSR")
suppressMessages(library(spFSR))
knnWrapper <- makeLearner("classif.knn", k = 5)
classifTask <- makeClassifTask(data = train, target = "Class")
perf.measure <- acc
spsaMod <- spFeatureSelection(
task = classifTask,
wrapper = knnWrapper,
measure = perf.measure ,
num.features.selected = prefer_no_features,
iters.max = max_iterations,
num.cores = detectCores())
formulas[["spFSR"]] = OmicSelector_create_formula(spsaMod$features)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 48
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting spFSR")
suppressMessages(library(spFSR))
classifTask <- makeClassifTask(data = train_smoted, target = "Class")
perf.measure <- acc
spsaMod <- spFeatureSelection(
task = classifTask,
wrapper = knnWrapper,
measure = perf.measure ,
num.features.selected = prefer_no_features,
iters.max = max_iterations,
num.cores = detectCores())
formulas[["spFSRSMOTE"]] = OmicSelector_create_formula(spsaMod$features)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 49
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
# varSelRF
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting varSelRF")
suppressMessages(library(varSelRF))
var.sel <- varSelRF(trainx, train$Class, ntree = 500, ntreeIterat = max_iterations, vars.drop.frac = 0.05, whole.range = T, keep.forest = T)
formulas[["varSelRF"]] = OmicSelector_create_formula(var.sel$selected.vars)
var.sel <- varSelRF(trainx_smoted, train_smoted$Class, ntree = 500, ntreeIterat = max_iterations, vars.drop.frac = 0.05, whole.range = T, keep.forest = T)
formulas[["varSelRFSMOTE"]] = OmicSelector_create_formula(var.sel$selected.vars)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 50
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
# 13. WxNet (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642261/)
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting WxNet")
suppressMessages(library(plyr))
suppressMessages(library(dplyr))
suppressMessages(library(reticulate))
suppressMessages(library(tidyverse))
suppressMessages(library(data.table))
suppressMessages(library(DMwR))
# Set the path to the Python executale file
#use_python("/anaconda3/bin/python", required = T)
# conda_list()
# try({ conda_create("wxnet", c("tensorflow-gpu","keras","numpy","pandas", "xgboost", "scikit-learn", "tqdm", "pickle5")) })
use_condaenv("base", required = T)
py_config()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
#train_org = train
#train = SMOTE(Class ~ ., data = train_org, perc.over = 10000,perc.under=100)
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
# system(paste0(conda_path," activate base"))
py_run_file("wx_konsta.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["Wx"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
# Wx with SMOTE
dane = OmicSelector_load_datamix(replace_smote = T); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
# setwd(paste0(code_path,"wx/DearWXpub/src/"))
# py_run_file("wx_konsta.py", local = T)
#
# np <- import("numpy")
# w = np$load("wyniki.npy",allow_pickle=T)
# print(w)
# setwd(wd)
# formulas[["WxSMOTE"]] = OmicSelector_create_formula(w)
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
py_run_file("wx_konsta.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["WxSMOTE"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
# Salvage results if SMOTE fails
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
# setwd(paste0(code_path,"wx/DearWXpub/src/"))
# py_run_file("wx_konsta_z.py", local = T)
#
# np <- import("numpy")
# w = np$load("wyniki.npy",allow_pickle=T)
# print(w)
# setwd(wd)
# formulas[["Wx_Zscore"]] = OmicSelector_create_formula(w)
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
py_run_file("wx_konsta_z.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["Wx_Zscore"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
dane = OmicSelector_load_datamix(replace_smote = T); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
# setwd(paste0(code_path,"wx/DearWXpub/src/"))
# py_run_file("wx_konsta_z.py", local = T)
#
# np <- import("numpy")
# w = np$load("wyniki.npy",allow_pickle=T)
# print(w)
# setwd(wd)
# formulas[["Wx_ZscoreSMOTE"]] = OmicSelector_create_formula(w)
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
py_run_file("wx_konsta_z.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["Wx_ZscoreSMOTE"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 51
# My.stepwise
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting My.stepwise")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
suppressMessages(library(My.stepwise))
temp = capture.output(My.stepwise.glm(Y = "Class", colnames(trainx), data = train, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx)))
{
temp6 = colnames(trainx)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_glm_binomial"]] = OmicSelector_create_formula(colnames(trainx)[wybrane])
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
temp = capture.output(My.stepwise.glm(Y = "Class", as.character(istotne$miR), data = train, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx)))
{
temp6 = colnames(trainx)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_sig_glm_binomial"]] = OmicSelector_create_formula(colnames(trainx)[wybrane])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 52
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting My.stepwise SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
suppressMessages(library(My.stepwise))
temp = capture.output(My.stepwise.glm(Y = "Class", colnames(trainx_smoted), data = train_smoted, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx_smoted)))
{
temp6 = colnames(trainx_smoted)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_glm_binomialSMOTE"]] = OmicSelector_create_formula(colnames(trainx_smoted)[wybrane])
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
temp = capture.output(My.stepwise.glm(Y = "Class", as.character(istotne$miR), data = train_smoted, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx_smoted)))
{
temp6 = colnames(trainx_smoted)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_sig_glm_binomialSMOTE"]] = OmicSelector_create_formula(colnames(trainx_smoted)[wybrane])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 53
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting stepAIC")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
temp = glm(Class ~ ., data = train, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAIC"]] = temp2$formula
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
train.sig = dplyr::select(train, as.character(istotne$miR), Class)
temp = glm(Class ~ ., data = train.sig, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAICsig"]] = temp2$formula
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 54
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting stepAIC SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
temp = glm(Class ~ ., data = train_smoted, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAIC_SMOTE"]] = temp2$formula
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
train.sig = dplyr::select(train_smoted, as.character(istotne$miR), Class)
temp = glm(Class ~ ., data = train.sig, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAICsig_SMOTE"]] = temp2$formula
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 55
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting MK method (iterated RFE)")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
selectedMirsCV <- OmicSelector_iteratedRFE(trainSet = train, useCV = T, classLab = 'Class', checkNFeatures = prefer_no_features)$topFeaturesPerN[[prefer_no_features]]
selectedMirsTest <- OmicSelector_iteratedRFE(trainSet = train, testSet = test, classLab = 'Class', checkNFeatures = prefer_no_features)$topFeaturesPerN[[prefer_no_features]]
formulas[["iteratedRFECV"]] = OmicSelector_create_formula(selectedMirsCV$topFeaturesPerN[[prefer_no_features]])
formulas[["iteratedRFETest"]] = OmicSelector_create_formula(selectedMirsTest$topFeaturesPerN[[prefer_no_features]])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 56
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting MK method (iterated RFE) with SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
formulas[["iteratedRFECV_SMOTE"]] = OmicSelector_create_formula(selectedMirsCV$topFeaturesPerN[[prefer_no_features]])
formulas[["iteratedRFETest_SMOTE"]] = OmicSelector_create_formula(selectedMirsTest$topFeaturesPerN[[prefer_no_features]])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 57
# LASSO model with alpha = 1 (Least Absolute Shrinkage and Selection Operator - a type of regularization method that penalizes with L1-norm.). The function cv.glmnet() is used to search for a regularization parameter, namely Lambda, that controls the penalty strength
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting LASSO with and without SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library("glmnet")
lasso_fit <- cv.glmnet(as.matrix(trainx), train$Class, family = "binomial", alpha = 1)
plot(lasso_fit)
coef <- predict(lasso_fit, s = "lambda.min", type = "nonzero")
result <- data.frame(GENE = names(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["LASSO"]] = OmicSelector_create_formula(as.character(result$GENE))
lasso_fit <- cv.glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", alpha = 1)
plot(lasso_fit)
coef <- predict(lasso_fit, s = "lambda.min", type = "nonzero")
result <- data.frame(GENE = names(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["LASSO_SMOTE"]] = OmicSelector_create_formula(as.character(result$GENE))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 58
# Elastic net with tuning the value of Alpha through a line search with the parallelism
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting ElasticNet with and without SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(foreach)
library(glmnet)
a <- seq(0.1, 0.9, 0.05)
search <- foreach(i = a, .combine = rbind) %dopar% {
library(OmicSelector)
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(glmnet)
cv <- cv.glmnet(as.matrix(trainx), train$Class, family = "binomial", nfold = 10, parallel = TRUE, alpha = i)
data.frame(cvm = cv$cvm[cv$lambda == cv$lambda.1se], lambda.1se = cv$lambda.1se, alpha = i)
}
cv3 <- search[search$cvm == min(search$cvm), ]
md3 <- glmnet(as.matrix(trainx), train$Class, family = "binomial", lambda = cv3$lambda.1se, alpha = cv3$alpha)
result <- data.frame(GENE = names(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["ElasticNet"]] = OmicSelector_create_formula(as.character(result$GENE))
# SMOTE:
a <- seq(0.1, 0.9, 0.05)
search <- foreach(i = a, .combine = rbind) %dopar% {
library(OmicSelector)
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(glmnet)
cv <- cv.glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", nfold = 10, parallel = TRUE, alpha = i)
data.frame(cvm = cv$cvm[cv$lambda == cv$lambda.1se], lambda.1se = cv$lambda.1se, alpha = i)
}
cv3 <- search[search$cvm == min(search$cvm), ]
md3 <- glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", lambda = cv3$lambda.1se, alpha = cv3$alpha)
result <- data.frame(GENE = names(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["ElasticNet_SMOTE"]] = OmicSelector_create_formula(as.character(result$GENE))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# # n = 59
# # Ridge regression sorted
# n = n + 1
# if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
# OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting Ridge")
# start_time <- Sys.time()
# dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# library(glmnet)
# fit <- cv.glmnet(as.matrix(trainx), train$Class, family = "binomial", alpha = 0)
# formulas[["Ridge"]] = OmicSelector_create_formula(as.character(names(sort(abs(coef(fit)[-1,]), decreasing = T)[1:prefer_no_features])))
# fit <- cv.glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", alpha = 0)
# formulas[["Ridge_SMOTE"]] = OmicSelector_create_formula(as.character(names(sort(abs(coef(fit)[-1,]), decreasing = T)[1:prefer_no_features])))
# end_time <- Sys.time()
# saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
# saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
# if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
# }
# n = 59
# Stepwise LDA
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting stepLDA")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
tempdb = cbind(`Class` = train$Class, trainx)
library(caret)
slda <- train(Class ~ ., data = tempdb,
method = "stepLDA",
trControl = trainControl(method = "loocv"))
formulas[["stepLDA"]] = OmicSelector_create_formula(predictors(slda))
tempdb = cbind(`Class` = train_smoted$Class, trainx_smoted)
library(caret)
slda <- train(Class ~ ., data = tempdb,
method = "stepLDA",
trControl = trainControl(method = "loocv"))
formulas[["stepLDA_SMOTE"]] = OmicSelector_create_formula(predictors(slda))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 60
# feseR
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting feseR")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(feseR)
features <- as.matrix(scale(trainx, center=TRUE, scale=TRUE))
rownames(features) = 1:nrow(features)
cls <- as.numeric(train$Class)-1
try({ output <- filter.corr(features = features, class = cls, mincorr = 0.2); formulas[["feseR_filter.corr"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.gain.inf(features = features, class = cls, zero.gain.out = TRUE); formulas[["feseR_gain.inf"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.matrix.corr(features = features, maxcorr = 0.75); formulas[["feseR_matrix.corr"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- combineFS(features = features, class = cls,
univariate = 'corr', mincorr = 0.2,
multivariate = 'mcorr', maxcorr = 0.75,
wrapper = 'rfe.rf', number.cv = 10,
group.sizes = 1:prefer_no_features,
extfolds = 20); formulas[["feseR_combineFS_RF"]] = OmicSelector_create_formula(output$opt.variables); }, silent = T)
features <- as.matrix(scale(trainx_smoted, center=TRUE, scale=TRUE))
rownames(features) = 1:nrow(features)
cls <- as.numeric(train_smoted$Class)-1
try({ output <- filter.corr(features = features, class = cls, mincorr = 0.2); formulas[["feseR_filter.corr_SMOTE"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.gain.inf(features = features, class = cls, zero.gain.out = TRUE); formulas[["feseR_gain.inf_SMOTE"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.matrix.corr(features = features, maxcorr = 0.75); formulas[["feseR_matrix.corr_SMOTE"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- combineFS(features = features, class = cls,
univariate = 'corr', mincorr = 0.2,
multivariate = 'mcorr', maxcorr = 0.75,
wrapper = 'rfe.rf', number.cv = 10,
group.sizes = 1:prefer_no_features,
extfolds = 20); formulas[["feseR_combineFS_RF_SMOTE"]] = OmicSelector_create_formula(output$opt.variables); }, silent = T)
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 61
# Asakura2020
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting feseR")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
source(system.file("extdata", "Asakura2020/init.R", package = "readr"))
# TO DO
fisher_pivot_num <- 20
fisher_turn_num <- 3
cancer_type <- "SC"
output_dir <- "output"
include_miRNA <- c()
include_miR_logical <- "and"
exclude_miRNA <- c()
model <- train
test <- test
if (!identical(colnames(model), colnames(test))) {
stop("MODEL/TEST column name mismatched")
}
for (i in seq_len(ncol(model))) {
miR2 <- unlist(strsplit(colnames(model)[i], ","))
if (length(miR2) > 1) {
colnames(model)[i] <- miR2[1]
colnames(test)[i] <- miR2[1]
}
}
for (miR in exclude_miRNA) {
col <- charmatch(miR, colnames(model), 0)
if (col > 0) {
model <- model[-col]
test <- test[-col]
}
}
fisher_miR <- matrix(0, nrow = fisher_pivot_num, ncol = fisher_turn_num + 1)
print_log(paste0("Fisher process turn : ", 1))
cols <- c()
cols_hash <- hash()
miRs <- next_miR(model, fisher_pivot_num, cols, cols_hash)
sortlist <- order(miRs$V2, decreasing = T)
miRs <- miRs[sortlist, ]
for (i in seq_len(nrow(fisher_miR))) {
fisher_miR[i, 1] <- miRs$V1[i]
fisher_miR[i, ncol(fisher_miR)] <- miRs$V2[i]
}
output_turn_fisher(model, test, 1, fisher_miR, process_name)
for (i in seq(2, fisher_turn_num)) {
print_log(paste0("Fisher process turn : ", i))
k <- 0
wk_fisher_miR <- matrix(0,
nrow = fisher_pivot_num * ncol(model),
ncol = ncol(fisher_miR)
)
for (j in seq_len(fisher_pivot_num)) {
if (is.na(fisher_miR[j, 1])) break
cols <- fisher_miR[j, 1:(i - 1)]
miRs <- next_miR(model, fisher_pivot_num, cols, cols_hash)
d <- k * ncol(model)
for (l in seq_len(nrow(miRs))) {
wk_fisher_miR[d + l, 1:(i - 1)] <- cols
wk_fisher_miR[d + l, i] <- miRs$V1[l]
wk_fisher_miR[d + l, ncol(fisher_miR)] <- miRs$V2[l]
}
k <- k + 1
}
sortlist <- order(wk_fisher_miR[, ncol(fisher_miR)], decreasing = T)
wk_fisher_miR <- wk_fisher_miR[sortlist, ]
wk_fisher_miR <- wk_fisher_miR[wk_fisher_miR[, ncol(fisher_miR)] > 0, ]
for (j in seq_len(min(nrow(fisher_miR), nrow(wk_fisher_miR)))) {
fisher_miR[j, ] <- wk_fisher_miR[j, ]
}
output_turn_fisher(model, test, i, fisher_miR, process_name)
clear(cols_hash)
}
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# End
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Ending task. Selected: \n", formulas))
stopCluster(cl)
saveRDS(formulas, paste0("temp/featureselection_formulas",stamp,".RDS"))
#saveRDS(formulas, paste0("featureselection_formulas.RDS"))
#nazwa = paste0("temp/featureselection_data",stamp,".rda")
#save(list = ls(), file = nazwa)
#dev.off()
print(formulas)
setwd(oldwd)
formulas}, timeout = timeout_sec, onTimeout = "silent")
if (is.null(wynik_finalny)) {
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("STOPED BECAUSE OF ERROR OR TIMEOUT REACHED!! Debug result: ", print(wynik_finalny)))
warnings()
save(list = ls(), file = paste0("temp/timeoutdebug_",stamp,"-",m,".rdata"))
}
sink()
#dev.off()
return(wynik_finalny)
}
kstawiski/OmicSelector documentation built on April 10, 2024, 11:11 p.m.
R Package Documentation
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Defines functions OmicSelector_OmicSelector
Documented in OmicSelector_OmicSelector
#' OmicSelector_OmicSelector
#'
#' Main function of the package. The aim of this function is to perform feature selection using multiple methods and to create formulas for benchmarking.
#' It loads the data from working directory. The output is mainly created in files in working directory. Log and temporary files are placed in created `temp` subfolder.
#' This package offers about 60 feature selection methods. Which methods will be check by this function is defined by `m` parameter.
#' Pearls about the methods:
#'
#' - Sig = miRNAs with p-value <0.05 after BH correction (DE using t-test)
#' - Fcsig = sig + absolute log2FC filter (included if abs. log2FC>1)
#' - Cfs = Correlation-based Feature Selection for Machine Learning (more: https://www.cs.waikato.ac.nz/~mhall/thesis.pdf)
#' - Classloop = Classification using different classification algorithms (classifiers) with the embedded feature selection and using the different schemes for the performance validation (more: https://rdrr.io/cran/Biocomb/man/classifier.loop.html)
#' - Fcfs = CFS algorithm with forward search (https://rdrr.io/cran/Biocomb/man/select.forward.Corr.html)
#' - MDL methods = minimal description length (MDL) discretization algorithm with different a method of feature ranking or feature selection (AUC, SU, CorrSF) (more: https://rdrr.io/cran/Biocomb/man/select.process.html)
#' - bounceR = genetic algorithm with componentwise boosting (more: https://www.statworx.com/ch/blog/automated-feature-selection-using-bouncer/)
#' - RandomForestRFE = recursive feature elimination using random forest with resampling to assess the performance. (more: https://topepo.github.io/caret/recursive-feature-elimination.html#resampling-and-external-validation)
#' - GeneticAlgorithmRF (more: https://topepo.github.io/caret/feature-selection-using-genetic-algorithms.html)
#' - SimulatedAnnealing = makes small random changes (i.e. perturbations) to an initial candidate solution (more: https://topepo.github.io/caret/feature-selection-using-simulated-annealing.html)
#' - Boruta (more: https://www.jstatsoft.org/article/view/v036i11/v36i11.pdf)
#' - spFSR = simultaneous perturbation stochastic approximation (SPSA-FSR) (more: https://arxiv.org/abs/1804.05589)
#' - varSelRF = using the out-of-bag error as minimization criterion, carry out variable elimination from random forest, by successively eliminating the least important variables (with importance as returned from random forest). (more: https://www.ncbi.nlm.nih.gov/pubmed/16398926)
#' - WxNet = a neural network-based feature selection algorithm for transcriptomic data (more: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642261/)
#' - Step = backward stepwise method of feature selection based on logistic regression (GLM, family = binomial) using AIC criteria (stepAIC) and functions from My.stepwise package (https://cran.r-project.org/web/packages/My.stepwise/index.html)
#'
#' For more detailed defitions please see the tutorial vignette.
#'
#' @param wd Working directory with data (`mixed_train.csv`, `mixed_test.csv` and `mixed_validation.csv` as created by `OmicSelector_prepare_split` have to be present).
#' @param m Methods of feature selection to be performed. This has to be a vector of integers with minimum of 1 and maximum of 70. For the definition of numbers please see the vignette.
#' @param max_iterations Maximum number of iterations in selected methods. Setting this too high may results in very long comupting time.
#' @param code_path A folder where the python external scripts are placed (especially for WxNet method). By default the additional code is provided in the package.
#' @param register_parallel Where to use parallel processing to speed up computing time. Seting it to FALSE may aid in debuging.
#' @param clx This parameter may be used for passing the already register computing cluster (created and registered with `doParallel` tools). This may lower the computing time by saving the time to register new cluster.
#' @param stamp A character vector or timestamp used for marking the output files.
#' @param prefer_no_features Maximum number of miRNAs that can be selected by the tools if the method allows for that.
#' @param conda_path Patch to "conda" bindary used for executing python scripts.
#' @param debug Gives additional debug information (saves .rdata after feature selection is completed, prints formulas to log)
#' @param timeout_sec Timeout after the method is terminated if not finished. It may be useful to keep the long methods limited, not to wait ethernity for the results.
#' @param type Parameter 'mode' forwarded to OmicSelector_differential_expression_ttest which is essential in many feature selection methods. Note that if 'var_type.txt' file exists in the working directory it is superior to the value set directly in function - as designed for GUI. Please refer to OmicSelector_differential_expression_ttest manual to understand how 'mode' worOmicSelector_
#'
#' @return The list of selected formulas. Note that, due to purpose of this package `OmicSelector_merge_formulas` may be a better option to get the output of processes run by this function.
#' @examples
#' # NOT RUN: (to speed up check, but this is a valid example for your real time projects)
#' # suppressMessages(library(foreach))
#' # suppressMessages(library(doParallel))
#' # suppressMessages(library(parallel))
#' # suppressMessages(library(doParallel))
#' # m = 1:56 # which methods to check?
#' # cl <- makePSOCKcluster(useXDR = TRUE, 5) # 5 threds by default
#' # doParallel:: registerDoParallel(cl)
#' # iterations = length(m)
#' # pb <- txtProgressBar(max = iterations, style = 3)
#' # progress <- function(n) setTxtProgressBar(pb, n)
#' # opts <- list(progress = progress)
#' # foreach(i = m, .verbose = TRUE, .options.snow = opts) %dopar%
#' # {
#' # suppressMessages(library(OmicSelector))
#' # setwd("~/public/Projekty/KS/OmicSelector/vignettes") # change it you to your working directory
#' # OmicSelector_OmicSelector(m = i, max_iterations = 1, stamp = "tutorial", debug = T) # we set debug to get more output
#' # }
#' # stopCluster(cl)
#'
#' @import snow doParallel
#'
#' @export
OmicSelector_OmicSelector = function(wd = getwd(), m = c(1:70),
max_iterations = 10, code_path = system.file("extdata", "", package = "OmicSelector"),
register_parallel = T, clx = NULL, stamp = as.numeric(Sys.time()),
prefer_no_features = 11, conda_path = "/home/konrad/anaconda3/bin/conda", debug = F,
timeout_sec = 172800, type = "auto") {
oldwd = getwd()
setwd(wd)
suppressMessages(library(plyr))
suppressMessages(library(dplyr))
suppressMessages(library(edgeR))
suppressMessages(library(epiDisplay))
suppressMessages(library(rsq))
suppressMessages(library(MASS))
suppressMessages(library(Biocomb))
suppressMessages(library(caret))
suppressMessages(library(dplyr))
suppressMessages(library(epiDisplay))
suppressMessages(library(pROC))
suppressMessages(library(ggplot2))
suppressMessages(library(DMwR))
suppressMessages(library(ROSE))
suppressMessages(library(gridExtra))
suppressMessages(library(gplots))
suppressMessages(library(devtools))
suppressMessages(library(stringr))
suppressMessages(library(data.table))
suppressMessages(library(tidyverse))
suppressMessages(library(R.utils))
suppressMessages(library(doParallel))
if(!dir.exists("temp")) { dir.create("temp") }
run_id = stamp
formulas = list()
times = list()
zz <- file(paste0("temp/",stamp,paste0(m, collapse = "+"),"featureselection.log"), open = "wt")
sink(zz)
#sink(zz, type = "message")
#pdf(paste0("temp/",stamp,paste0(m, collapse = "+"),"featureselection.pdf"))
# not_needed as set to auto
# if(file.exists("var_type.txt")) { type = readLines("var_type.txt", warn = F) }
wynik_finalny = withTimeout({
dane = OmicSelector_load_datamix(); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# n = 1
n= 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Staring method n = ", n, ". Processing...")); OmicSelector_log(logfile = "temp/featureselection.log");
start_time <- Sys.time()
formulas[["all"]] = OmicSelector_create_formula(colnames(trainx))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Standard DE...")
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value` <= 0.05) %>% arrange(`p-value`)
if(length(istotne$miR) == 0) { istotne = wyniki %>% arrange(`p-value`) } # what to do if none significant
istotne_top = wyniki %>% arrange(`p-value`) %>% head(prefer_no_features)
istotne_topBonf = wyniki %>% arrange(`p-value Bonferroni`) %>% head(prefer_no_features)
istotne_topHolm = wyniki %>% arrange(`p-value Holm`) %>% head(prefer_no_features)
istotne_topFC = wyniki %>% arrange(desc(abs(`log2FC`))) %>% head(prefer_no_features)
train_sig = dplyr::select(train, as.character(istotne$miR), Class)
trainx_sig = dplyr::select(train, as.character(istotne$miR))
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Preparing for SMOTE...")
#train_sig_smoted = DMwR::SMOTE(Class ~ ., data = train_sig, perc.over = 10000,perc.under=100, k=10)
train_sig_smoted = dplyr::select(train_smoted, as.character(istotne$miR), Class)
train_sig_smoted$Class = factor(train_sig_smoted$Class, levels = c("Control","Case"))
trainx_sig_smoted = dplyr::select(train_sig_smoted, starts_with("hsa"))
wyniki_smoted = OmicSelector_differential_expression_ttest(trainx_smoted, train_smoted$Class, mode = type)
istotne_smoted = filter(wyniki_smoted, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
if(length(istotne_smoted$miR) == 0) { istotne = wyniki_smoted %>% arrange(`p-value BH`) } # what to do if none significant
istotne_top_smoted = wyniki_smoted %>% arrange(`p-value BH`) %>% head(prefer_no_features)
istotne_topBonf_smoted = wyniki_smoted %>% arrange(`p-value Bonferroni`) %>% head(prefer_no_features)
istotne_topHolm_smoted = wyniki_smoted %>% arrange(`p-value Holm`) %>% head(prefer_no_features)
istotne_topFC_smoted = wyniki_smoted %>% arrange(desc(abs(`log2FC`))) %>% head(prefer_no_features)
# Caret prep
suppressMessages(library(doParallel))
registerDoSEQ()
if (register_parallel) {
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Getting cluster ready for parallel computations...")
if(is.null(clx)) {
cl <- makePSOCKcluster(useXDR = TRUE, detectCores())
registerDoParallel(cl)
#on.exit(stopCluster(cl))
}
else { registerDoParallel(clx)
#on.exit(stopCluster(clx))
}
}
# 0. All and sig
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Cluster prepared. Moving to method matching...")
# n = 2
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SIG")
start_time <- Sys.time()
formulas[["sig"]] = OmicSelector_create_formula(as.character(istotne$miR))
formulas[["sigtop"]] = OmicSelector_create_formula(as.character(istotne_top$miR))
formulas[["sigtopBonf"]] = OmicSelector_create_formula(as.character(istotne_topBonf$miR))
formulas[["sigtopHolm"]] = OmicSelector_create_formula(as.character(istotne_topHolm$miR))
formulas[["topFC"]] = OmicSelector_create_formula(as.character(istotne_topFC$miR))
formulas[["sigSMOTE"]] = OmicSelector_create_formula(as.character(istotne_smoted$miR))
formulas[["sigtopSMOTE"]] = OmicSelector_create_formula(as.character(istotne_top_smoted$miR))
formulas[["sigtopBonfSMOTE"]] = OmicSelector_create_formula(as.character(istotne_topBonf_smoted$miR))
formulas[["sigtopHolmSMOTE"]] = OmicSelector_create_formula(as.character(istotne_topHolm_smoted$miR))
formulas[["topFCSMOTE"]] = OmicSelector_create_formula(as.character(istotne_topFC_smoted$miR))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 3
# 1. Fold-change and sig. filter
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
start_time <- Sys.time()
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting FC and SIG")
fcsig = as.character(istotne$miR[abs(istotne$log2FC)>1])
formulas[["fcsig"]] = OmicSelector_create_formula(fcsig)
fcsig = as.character(istotne_smoted$miR[abs(istotne_smoted$log2FC)>1])
formulas[["fcsigSMOTE"]] = OmicSelector_create_formula(fcsig)
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 4
# 2. CFS
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
start_time <- Sys.time()
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting CFS")
cfs = select.cfs(train)
formulas[["cfs"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
cfs = select.cfs(train_smoted)
formulas[["cfsSMOTE"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
cfs = select.cfs(train_sig)
formulas[["cfs_sig"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
cfs = select.cfs(train_sig_smoted)
formulas[["cfsSMOTE_sig"]] = OmicSelector_create_formula(as.character(cfs$Biomarker))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 5
# 3. classifier.loop
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
start_time <- Sys.time()
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting classifier loop")
classloop = classifier.loop(train, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloop"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 6
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
classloop = classifier.loop(train_smoted, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloopSMOTE"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 7
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
classloop = classifier.loop(train_sig, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloop_sig"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 8
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
classloop = classifier.loop(train_sig_smoted, feature.selection = "auc", method.cross="fold-crossval", classifiers=c("svm","lda","rf","nsc"), no.feat=prefer_no_features)
f_classloop = rownames(classloop$no.selected)[classloop$no.selected[,1]>0]
formulas[["classloopSMOTE_sig"]] = OmicSelector_create_formula(f_classloop)
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 9
# 4. select.forward.Corr
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting select.forward.Corr")
fcfs = select.forward.Corr(train, disc.method="MDL")
formulas[["fcfs"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 10
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fcfs = select.forward.Corr(train_smoted, disc.method="MDL")
formulas[["fcfsSMOTE"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 11
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fcfs = select.forward.Corr(train_sig, disc.method="MDL")
formulas[["fcfs_sig"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 12
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fcfs = select.forward.Corr(train_sig_smoted, disc.method="MDL")
formulas[["fcfsSMOTE_sig"]] = OmicSelector_create_formula(fcfs)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 13
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
# 5. select.forward.wrapper
fwrap = select.forward.wrapper(train)
formulas[["fwrap"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 14
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fwrap = select.forward.wrapper(train_smoted)
formulas[["fwrapSMOTE"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 15
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fwrap = select.forward.wrapper(train_sig)
formulas[["fwrap_sig"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 16
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
fwrap = select.forward.wrapper(train_sig_smoted)
formulas[["fwrapSMOTE_sig"]] = OmicSelector_create_formula(fwrap)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# 6, 7, 8.
#select.process(dattable,method="InformationGain",disc.method="MDL",
# threshold=0.2,threshold.consis=0.05,attrs.nominal=numeric(),
# max.no.features=10)
# n = 17
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting MDL")
formulas[["AUC_MDL"]] = OmicSelector_create_formula(colnames(train)[select.process(train, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 18
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDL"]] = OmicSelector_create_formula(colnames(train)[select.process(train, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 19
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDL"]] = OmicSelector_create_formula(colnames(train)[select.process(train, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 20
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["AUC_MDLSMOTE"]] = OmicSelector_create_formula(colnames(train_smoted)[select.process(train_smoted, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 21
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDLSMOTE"]] = OmicSelector_create_formula(colnames(train_smoted)[select.process(train_smoted, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 22
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDLSMOTE"]] = OmicSelector_create_formula(colnames(train_smoted)[select.process(train_smoted, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 23
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["AUC_MDL_sig"]] = OmicSelector_create_formula(colnames(train_sig)[select.process(train_sig, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 24
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDL_sig"]] = OmicSelector_create_formula(colnames(train_sig)[select.process(train_sig, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 25
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDL_sig"]] = OmicSelector_create_formula(colnames(train_sig)[select.process(train_sig, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 26
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["AUC_MDLSMOTE_sig"]] = OmicSelector_create_formula(colnames(train_sig_smoted)[select.process(train_sig_smoted, method="auc", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 27
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["SU_MDLSMOTE_sig"]] = OmicSelector_create_formula(colnames(train_sig_smoted)[select.process(train_sig_smoted, method="symmetrical.uncertainty", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 28
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
formulas[["CorrSF_MDLSMOTE_sig"]] = OmicSelector_create_formula(colnames(train_sig_smoted)[select.process(train_sig_smoted, method="CorrSF", disc.method = "MDL", max.no.features = prefer_no_features)])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 29
# 9. bounceR - genetic
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting bounceR..")
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full"]] = mrmr@opt_formula
formulas[["bounceR-stability"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 30
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train_smoted,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full_SMOTE"]] = mrmr@opt_formula
formulas[["bounceR-stability_SMOTE"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 31
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train_sig,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full_SIG"]] = mrmr@opt_formula
formulas[["bounceR-stability_SIG"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 32
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(bounceR))
mrmr <- bounceR::featureSelection(data = train_sig_smoted,
target = "Class",
max_time = "5 mins",
selection = selectionControl(n_rounds = NULL,
n_mods = NULL,
p = prefer_no_features,
penalty = 0.5,
reward = 0.2),
bootstrap = "regular",
early_stopping = "aic",
boosting = boostingControl(mstop = 100, nu = 0.1),
cores = parallel::detectCores()-1)
formulas[["bounceR-full_SIGSMOTE"]] = mrmr@opt_formula
formulas[["bounceR-stability_SIGSMOTE"]] = OmicSelector_create_formula(as.character(mrmr@stability[1:prefer_no_features,] %>% pull('feature')))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 33
# 10. RFE RandomForest
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting RFE RF")
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx, train$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFE"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 34
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx_smoted, train_smoted$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFESMOTE"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 35
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx_sig, train_sig$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFE_sig"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 36
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ctrl <- rfeControl(functions =rfFuncs,
method = "cv", number = 10,
saveDetails = TRUE,
allowParallel = TRUE,
returnResamp = "all",
verbose = T)
rfProfile <- rfe(trainx_sig_smoted, train_sig_smoted$Class,
sizes = 3:11,
rfeControl = ctrl)
plot(rfProfile, type=c("g", "o"))
formulas[["RandomForestRFESMOTE_sig"]] = OmicSelector_create_formula(predictors(rfProfile))
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 37
# 11. Genetic
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting Genetic")
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx, y = train$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRF"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 38
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx_smoted, y = train_smoted$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRFSMOTE"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 39
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx_sig, y = train_sig$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRF_sig"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 40
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
ga_ctrl <- gafsControl(functions = rfGA, method = "repeatedcv", number=10, repeats=5, allowParallel=T)
rf_ga <- gafs(x = trainx_sig_smoted, y = train_sig_smoted$Class,
iters = max_iterations,
gafsControl = ga_ctrl)
plot(rf_ga) + theme_bw()
print(rf_ga)
formulas[["GeneticAlgorithmRFSMOTE_sig"]] = OmicSelector_create_formula(rf_ga$ga$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 41
# 12. SimulatedAnealing
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx, y = train$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRF"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 42
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx_smoted, y = train_smoted$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRFSMOTE"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 43
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx_sig, y = train_sig$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRF_sig"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 44
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting SimulatedAnealing")
sa_ctrl <- safsControl(functions = rfSA,
method = "repeatedcv",
number=5, repeats=10, allowParallel=T,
improve = 50)
rf_sa <- safs(x = trainx_sig_smoted, y = train_sig_smoted$Class,
iters = max_iterations,
safsControl = sa_ctrl)
print(rf_sa)
plot(rf_sa) + theme_bw()
formulas[["SimulatedAnnealingRFSMOTE_sig"]] = OmicSelector_create_formula(rf_sa$sa$final)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 45
# 14. Boruta (https://www.jstatsoft.org/article/view/v036i11)
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
suppressMessages(library(Boruta))
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting Boruta")
bor = Boruta(trainx, train$Class)
formulas[["Boruta"]] = OmicSelector_create_formula(names(bor$finalDecision)[as.character(bor$finalDecision) == "Confirmed"])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 46
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
bor = Boruta(trainx_smoted, train_smoted$Class)
formulas[["BorutaSMOTE"]] = OmicSelector_create_formula(names(bor$finalDecision)[as.character(bor$finalDecision) == "Confirmed"])
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 47
# 15. spFSR - feature selection and ranking by simultaneous perturbation stochastic approximation
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting spFSR")
suppressMessages(library(spFSR))
knnWrapper <- makeLearner("classif.knn", k = 5)
classifTask <- makeClassifTask(data = train, target = "Class")
perf.measure <- acc
spsaMod <- spFeatureSelection(
task = classifTask,
wrapper = knnWrapper,
measure = perf.measure ,
num.features.selected = prefer_no_features,
iters.max = max_iterations,
num.cores = detectCores())
formulas[["spFSR"]] = OmicSelector_create_formula(spsaMod$features)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 48
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting spFSR")
suppressMessages(library(spFSR))
classifTask <- makeClassifTask(data = train_smoted, target = "Class")
perf.measure <- acc
spsaMod <- spFeatureSelection(
task = classifTask,
wrapper = knnWrapper,
measure = perf.measure ,
num.features.selected = prefer_no_features,
iters.max = max_iterations,
num.cores = detectCores())
formulas[["spFSRSMOTE"]] = OmicSelector_create_formula(spsaMod$features)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 49
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
# varSelRF
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting varSelRF")
suppressMessages(library(varSelRF))
var.sel <- varSelRF(trainx, train$Class, ntree = 500, ntreeIterat = max_iterations, vars.drop.frac = 0.05, whole.range = T, keep.forest = T)
formulas[["varSelRF"]] = OmicSelector_create_formula(var.sel$selected.vars)
var.sel <- varSelRF(trainx_smoted, train_smoted$Class, ntree = 500, ntreeIterat = max_iterations, vars.drop.frac = 0.05, whole.range = T, keep.forest = T)
formulas[["varSelRFSMOTE"]] = OmicSelector_create_formula(var.sel$selected.vars)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 50
n = n + 1; if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting..")); start_time <- Sys.time();
# 13. WxNet (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6642261/)
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting WxNet")
suppressMessages(library(plyr))
suppressMessages(library(dplyr))
suppressMessages(library(reticulate))
suppressMessages(library(tidyverse))
suppressMessages(library(data.table))
suppressMessages(library(DMwR))
# Set the path to the Python executale file
#use_python("/anaconda3/bin/python", required = T)
# conda_list()
# try({ conda_create("wxnet", c("tensorflow-gpu","keras","numpy","pandas", "xgboost", "scikit-learn", "tqdm", "pickle5")) })
use_condaenv("base", required = T)
py_config()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
#train_org = train
#train = SMOTE(Class ~ ., data = train_org, perc.over = 10000,perc.under=100)
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
# system(paste0(conda_path," activate base"))
py_run_file("wx_konsta.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["Wx"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
# Wx with SMOTE
dane = OmicSelector_load_datamix(replace_smote = T); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
# setwd(paste0(code_path,"wx/DearWXpub/src/"))
# py_run_file("wx_konsta.py", local = T)
#
# np <- import("numpy")
# w = np$load("wyniki.npy",allow_pickle=T)
# print(w)
# setwd(wd)
# formulas[["WxSMOTE"]] = OmicSelector_create_formula(w)
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
py_run_file("wx_konsta.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["WxSMOTE"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
# Salvage results if SMOTE fails
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
# setwd(paste0(code_path,"wx/DearWXpub/src/"))
# py_run_file("wx_konsta_z.py", local = T)
#
# np <- import("numpy")
# w = np$load("wyniki.npy",allow_pickle=T)
# print(w)
# setwd(wd)
# formulas[["Wx_Zscore"]] = OmicSelector_create_formula(w)
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
py_run_file("wx_konsta_z.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["Wx_Zscore"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
dane = OmicSelector_load_datamix(replace_smote = T); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# Przygotowanie
trainx = dplyr::select(train, starts_with("hsa"))
testx = dplyr::select(test, starts_with("hsa"))
trainx = t(trainx)
testx = t(testx)
ids = paste0("train",1:ncol(trainx))
ids2 = paste0("test",1:ncol(testx))
colnames(trainx) = gsub("-", "", ids)
colnames(testx) = gsub("-", "", ids2)
traindata = cbind(data.frame(fnames = rownames(trainx), trainx))
fwrite(traindata, paste0(code_path, "wx/DearWXpub/src/train-data.csv"), row.names = F, quote = F)
testdata = cbind(data.frame(fnames = rownames(testx), testx))
fwrite(testdata, paste0(code_path, "wx/DearWXpub/src/test-data.csv"), row.names = F, quote = F)
trainanno = data.frame(id = colnames(trainx), label = train$Class)
fwrite(trainanno, paste0(code_path, "wx/DearWXpub/src/train-anno.csv"), row.names = F, quote = F)
testanno = data.frame(id = colnames(testx), label = test$Class)
fwrite(testanno, paste0(code_path, "wx/DearWXpub/src/test-anno.csv"), row.names = F, quote = F)
# Wywolanie WX
# setwd(paste0(code_path,"wx/DearWXpub/src/"))
# py_run_file("wx_konsta_z.py", local = T)
#
# np <- import("numpy")
# w = np$load("wyniki.npy",allow_pickle=T)
# print(w)
# setwd(wd)
# formulas[["Wx_ZscoreSMOTE"]] = OmicSelector_create_formula(w)
out <- tryCatch(
{
setwd(paste0(code_path,"wx/DearWXpub/src/"))
py_run_file("wx_konsta_z.py", local = T)
np <- import("numpy")
w = np$load("wyniki.npy",allow_pickle=T)
formulas[["Wx_ZscoreSMOTE"]] = OmicSelector_create_formula(w)
},
error=function(cond) {
message("ERROR:")
message(cond)
# Choose a return value in case of error
},
warning=function(cond) {
message("WARNING:")
message(cond)
},
finally={
setwd(wd)
}
)
end_time <- Sys.time(); saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS")); saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 51
# My.stepwise
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting My.stepwise")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
suppressMessages(library(My.stepwise))
temp = capture.output(My.stepwise.glm(Y = "Class", colnames(trainx), data = train, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx)))
{
temp6 = colnames(trainx)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_glm_binomial"]] = OmicSelector_create_formula(colnames(trainx)[wybrane])
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
temp = capture.output(My.stepwise.glm(Y = "Class", as.character(istotne$miR), data = train, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx)))
{
temp6 = colnames(trainx)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_sig_glm_binomial"]] = OmicSelector_create_formula(colnames(trainx)[wybrane])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 52
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting My.stepwise SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
suppressMessages(library(My.stepwise))
temp = capture.output(My.stepwise.glm(Y = "Class", colnames(trainx_smoted), data = train_smoted, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx_smoted)))
{
temp6 = colnames(trainx_smoted)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_glm_binomialSMOTE"]] = OmicSelector_create_formula(colnames(trainx_smoted)[wybrane])
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
temp = capture.output(My.stepwise.glm(Y = "Class", as.character(istotne$miR), data = train_smoted, sle = 0.05, sls = 0.05, myfamily = "binomial"))
# temp2 = temp[length(temp)-1]
# temp3 = temp[length(temp)-3]
# temp4 = temp[length(temp)-5]
temp5 = paste(temp[(length(temp)-12):length(temp)], collapse = " ")
wybrane = FALSE
for(i in 1:length(colnames(trainx_smoted)))
{
temp6 = colnames(trainx_smoted)[i]
wybrane[i] = grepl(temp6, temp5)
}
formulas[["Mystepwise_sig_glm_binomialSMOTE"]] = OmicSelector_create_formula(colnames(trainx_smoted)[wybrane])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 53
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting stepAIC")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
temp = glm(Class ~ ., data = train, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAIC"]] = temp2$formula
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
train.sig = dplyr::select(train, as.character(istotne$miR), Class)
temp = glm(Class ~ ., data = train.sig, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAICsig"]] = temp2$formula
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 54
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting stepAIC SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
temp = glm(Class ~ ., data = train_smoted, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAIC_SMOTE"]] = temp2$formula
wyniki = OmicSelector_differential_expression_ttest(trainx, train$Class, mode = type)
istotne = filter(wyniki, `p-value BH` <= 0.05) %>% arrange(`p-value BH`)
train.sig = dplyr::select(train_smoted, as.character(istotne$miR), Class)
temp = glm(Class ~ ., data = train.sig, family = "binomial")
temp2 = stepAIC(temp)
formulas[["stepAICsig_SMOTE"]] = temp2$formula
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 55
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting MK method (iterated RFE)")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
selectedMirsCV <- OmicSelector_iteratedRFE(trainSet = train, useCV = T, classLab = 'Class', checkNFeatures = prefer_no_features)$topFeaturesPerN[[prefer_no_features]]
selectedMirsTest <- OmicSelector_iteratedRFE(trainSet = train, testSet = test, classLab = 'Class', checkNFeatures = prefer_no_features)$topFeaturesPerN[[prefer_no_features]]
formulas[["iteratedRFECV"]] = OmicSelector_create_formula(selectedMirsCV$topFeaturesPerN[[prefer_no_features]])
formulas[["iteratedRFETest"]] = OmicSelector_create_formula(selectedMirsTest$topFeaturesPerN[[prefer_no_features]])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 56
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting MK method (iterated RFE) with SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
formulas[["iteratedRFECV_SMOTE"]] = OmicSelector_create_formula(selectedMirsCV$topFeaturesPerN[[prefer_no_features]])
formulas[["iteratedRFETest_SMOTE"]] = OmicSelector_create_formula(selectedMirsTest$topFeaturesPerN[[prefer_no_features]])
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 57
# LASSO model with alpha = 1 (Least Absolute Shrinkage and Selection Operator - a type of regularization method that penalizes with L1-norm.). The function cv.glmnet() is used to search for a regularization parameter, namely Lambda, that controls the penalty strength
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting LASSO with and without SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library("glmnet")
lasso_fit <- cv.glmnet(as.matrix(trainx), train$Class, family = "binomial", alpha = 1)
plot(lasso_fit)
coef <- predict(lasso_fit, s = "lambda.min", type = "nonzero")
result <- data.frame(GENE = names(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["LASSO"]] = OmicSelector_create_formula(as.character(result$GENE))
lasso_fit <- cv.glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", alpha = 1)
plot(lasso_fit)
coef <- predict(lasso_fit, s = "lambda.min", type = "nonzero")
result <- data.frame(GENE = names(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(lasso_fit, s = "lambda.min"))
[as.matrix(coef(lasso_fit,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["LASSO_SMOTE"]] = OmicSelector_create_formula(as.character(result$GENE))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 58
# Elastic net with tuning the value of Alpha through a line search with the parallelism
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting ElasticNet with and without SMOTE")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(foreach)
library(glmnet)
a <- seq(0.1, 0.9, 0.05)
search <- foreach(i = a, .combine = rbind) %dopar% {
library(OmicSelector)
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(glmnet)
cv <- cv.glmnet(as.matrix(trainx), train$Class, family = "binomial", nfold = 10, parallel = TRUE, alpha = i)
data.frame(cvm = cv$cvm[cv$lambda == cv$lambda.1se], lambda.1se = cv$lambda.1se, alpha = i)
}
cv3 <- search[search$cvm == min(search$cvm), ]
md3 <- glmnet(as.matrix(trainx), train$Class, family = "binomial", lambda = cv3$lambda.1se, alpha = cv3$alpha)
result <- data.frame(GENE = names(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["ElasticNet"]] = OmicSelector_create_formula(as.character(result$GENE))
# SMOTE:
a <- seq(0.1, 0.9, 0.05)
search <- foreach(i = a, .combine = rbind) %dopar% {
library(OmicSelector)
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(glmnet)
cv <- cv.glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", nfold = 10, parallel = TRUE, alpha = i)
data.frame(cvm = cv$cvm[cv$lambda == cv$lambda.1se], lambda.1se = cv$lambda.1se, alpha = i)
}
cv3 <- search[search$cvm == min(search$cvm), ]
md3 <- glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", lambda = cv3$lambda.1se, alpha = cv3$alpha)
result <- data.frame(GENE = names(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3, s = "lambda.min"))[,1]!=0, 1])[-1],
SCORE = as.numeric(as.matrix(coef(md3, s = "lambda.min"))
[as.matrix(coef(md3,
s = "lambda.min"))[,1]!=0, 1])[-1])
result <- result[order(-abs(result$SCORE)),]
formulas[["ElasticNet_SMOTE"]] = OmicSelector_create_formula(as.character(result$GENE))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# # n = 59
# # Ridge regression sorted
# n = n + 1
# if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
# OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting Ridge")
# start_time <- Sys.time()
# dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
# library(glmnet)
# fit <- cv.glmnet(as.matrix(trainx), train$Class, family = "binomial", alpha = 0)
# formulas[["Ridge"]] = OmicSelector_create_formula(as.character(names(sort(abs(coef(fit)[-1,]), decreasing = T)[1:prefer_no_features])))
# fit <- cv.glmnet(as.matrix(trainx_smoted), train_smoted$Class, family = "binomial", alpha = 0)
# formulas[["Ridge_SMOTE"]] = OmicSelector_create_formula(as.character(names(sort(abs(coef(fit)[-1,]), decreasing = T)[1:prefer_no_features])))
# end_time <- Sys.time()
# saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
# saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
# if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
# }
# n = 59
# Stepwise LDA
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting stepLDA")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
tempdb = cbind(`Class` = train$Class, trainx)
library(caret)
slda <- train(Class ~ ., data = tempdb,
method = "stepLDA",
trControl = trainControl(method = "loocv"))
formulas[["stepLDA"]] = OmicSelector_create_formula(predictors(slda))
tempdb = cbind(`Class` = train_smoted$Class, trainx_smoted)
library(caret)
slda <- train(Class ~ ., data = tempdb,
method = "stepLDA",
trControl = trainControl(method = "loocv"))
formulas[["stepLDA_SMOTE"]] = OmicSelector_create_formula(predictors(slda))
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 60
# feseR
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting feseR")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
library(feseR)
features <- as.matrix(scale(trainx, center=TRUE, scale=TRUE))
rownames(features) = 1:nrow(features)
cls <- as.numeric(train$Class)-1
try({ output <- filter.corr(features = features, class = cls, mincorr = 0.2); formulas[["feseR_filter.corr"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.gain.inf(features = features, class = cls, zero.gain.out = TRUE); formulas[["feseR_gain.inf"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.matrix.corr(features = features, maxcorr = 0.75); formulas[["feseR_matrix.corr"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- combineFS(features = features, class = cls,
univariate = 'corr', mincorr = 0.2,
multivariate = 'mcorr', maxcorr = 0.75,
wrapper = 'rfe.rf', number.cv = 10,
group.sizes = 1:prefer_no_features,
extfolds = 20); formulas[["feseR_combineFS_RF"]] = OmicSelector_create_formula(output$opt.variables); }, silent = T)
features <- as.matrix(scale(trainx_smoted, center=TRUE, scale=TRUE))
rownames(features) = 1:nrow(features)
cls <- as.numeric(train_smoted$Class)-1
try({ output <- filter.corr(features = features, class = cls, mincorr = 0.2); formulas[["feseR_filter.corr_SMOTE"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.gain.inf(features = features, class = cls, zero.gain.out = TRUE); formulas[["feseR_gain.inf_SMOTE"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- filter.matrix.corr(features = features, maxcorr = 0.75); formulas[["feseR_matrix.corr_SMOTE"]] = OmicSelector_create_formula(colnames(output)); }, silent = T)
try({ output <- combineFS(features = features, class = cls,
univariate = 'corr', mincorr = 0.2,
multivariate = 'mcorr', maxcorr = 0.75,
wrapper = 'rfe.rf', number.cv = 10,
group.sizes = 1:prefer_no_features,
extfolds = 20); formulas[["feseR_combineFS_RF_SMOTE"]] = OmicSelector_create_formula(output$opt.variables); }, silent = T)
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# n = 61
# Asakura2020
n = n + 1
if (n %in% m) { OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Matched method ", n, " with those requested.. Starting.."));
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = "Starting feseR")
start_time <- Sys.time()
dane = OmicSelector_load_datamix(replace_smote = F); train = dane[[1]]; test = dane[[2]]; valid = dane[[3]]; train_smoted = dane[[4]]; trainx = dane[[5]]; trainx_smoted = dane[[6]]
source(system.file("extdata", "Asakura2020/init.R", package = "readr"))
# TO DO
fisher_pivot_num <- 20
fisher_turn_num <- 3
cancer_type <- "SC"
output_dir <- "output"
include_miRNA <- c()
include_miR_logical <- "and"
exclude_miRNA <- c()
model <- train
test <- test
if (!identical(colnames(model), colnames(test))) {
stop("MODEL/TEST column name mismatched")
}
for (i in seq_len(ncol(model))) {
miR2 <- unlist(strsplit(colnames(model)[i], ","))
if (length(miR2) > 1) {
colnames(model)[i] <- miR2[1]
colnames(test)[i] <- miR2[1]
}
}
for (miR in exclude_miRNA) {
col <- charmatch(miR, colnames(model), 0)
if (col > 0) {
model <- model[-col]
test <- test[-col]
}
}
fisher_miR <- matrix(0, nrow = fisher_pivot_num, ncol = fisher_turn_num + 1)
print_log(paste0("Fisher process turn : ", 1))
cols <- c()
cols_hash <- hash()
miRs <- next_miR(model, fisher_pivot_num, cols, cols_hash)
sortlist <- order(miRs$V2, decreasing = T)
miRs <- miRs[sortlist, ]
for (i in seq_len(nrow(fisher_miR))) {
fisher_miR[i, 1] <- miRs$V1[i]
fisher_miR[i, ncol(fisher_miR)] <- miRs$V2[i]
}
output_turn_fisher(model, test, 1, fisher_miR, process_name)
for (i in seq(2, fisher_turn_num)) {
print_log(paste0("Fisher process turn : ", i))
k <- 0
wk_fisher_miR <- matrix(0,
nrow = fisher_pivot_num * ncol(model),
ncol = ncol(fisher_miR)
)
for (j in seq_len(fisher_pivot_num)) {
if (is.na(fisher_miR[j, 1])) break
cols <- fisher_miR[j, 1:(i - 1)]
miRs <- next_miR(model, fisher_pivot_num, cols, cols_hash)
d <- k * ncol(model)
for (l in seq_len(nrow(miRs))) {
wk_fisher_miR[d + l, 1:(i - 1)] <- cols
wk_fisher_miR[d + l, i] <- miRs$V1[l]
wk_fisher_miR[d + l, ncol(fisher_miR)] <- miRs$V2[l]
}
k <- k + 1
}
sortlist <- order(wk_fisher_miR[, ncol(fisher_miR)], decreasing = T)
wk_fisher_miR <- wk_fisher_miR[sortlist, ]
wk_fisher_miR <- wk_fisher_miR[wk_fisher_miR[, ncol(fisher_miR)] > 0, ]
for (j in seq_len(min(nrow(fisher_miR), nrow(wk_fisher_miR)))) {
fisher_miR[j, ] <- wk_fisher_miR[j, ]
}
output_turn_fisher(model, test, i, fisher_miR, process_name)
clear(cols_hash)
}
end_time <- Sys.time()
saveRDS(end_time - start_time, paste0("temp/time",n,"-",run_id,".RDS"))
saveRDS(formulas, paste0("temp/formulas",run_id,"-",n,".RDS"))
if(debug) { save(list = ls(), file = paste0("temp/all",n,"-",run_id,".rdata")); print(formulas) }
}
# End
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("Ending task. Selected: \n", formulas))
stopCluster(cl)
saveRDS(formulas, paste0("temp/featureselection_formulas",stamp,".RDS"))
#saveRDS(formulas, paste0("featureselection_formulas.RDS"))
#nazwa = paste0("temp/featureselection_data",stamp,".rda")
#save(list = ls(), file = nazwa)
#dev.off()
print(formulas)
setwd(oldwd)
formulas}, timeout = timeout_sec, onTimeout = "silent")
if (is.null(wynik_finalny)) {
OmicSelector_log(logfile = "temp/featureselection.log", message_to_log = paste0("STOPED BECAUSE OF ERROR OR TIMEOUT REACHED!! Debug result: ", print(wynik_finalny)))
warnings()
save(list = ls(), file = paste0("temp/timeoutdebug_",stamp,"-",m,".rdata"))
}
sink()
#dev.off()
return(wynik_finalny)
}
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