### [bt]: This is batchtools v0.9.16 ### [bt]: Starting calculation of 1 jobs ### [bt]: Setting working directory to '/projects/genomic-ml/projects/max-generalized-auc' ### [bt]: Memory measurement disabled ### [bt]: Starting job [batchtools job.id=296] ### [bt]: Setting seed to 23777 ... subtrain validation test aum::aum 0.1642993 0.1720076 0.1535296 penaltyLearning::ROChange Inf Inf 53.2971632 Loading required namespace: future.apply Loading required namespace: directlabels seed=1 init.name=IntervalRegressionCV algo=grid step=1 auc 0.905597->0.922727 seed=1 init.name=IntervalRegressionCV algo=grid step=2 auc 0.922727->0.938331 seed=1 init.name=IntervalRegressionCV algo=grid step=3 auc 0.938331->0.948275 seed=1 init.name=IntervalRegressionCV algo=grid step=4 auc 0.948275->0.954560 seed=1 init.name=IntervalRegressionCV algo=grid step=5 auc 0.954560->0.960566 seed=1 init.name=IntervalRegressionCV algo=grid step=6 auc 0.960566->0.963304 seed=1 init.name=IntervalRegressionCV algo=grid 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init.name=IntervalRegressionCV algo=grid step=30396 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30397 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30398 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30399 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30400 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30401 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30402 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30403 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30404 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30405 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30406 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30407 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30408 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30409 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30410 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30411 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30412 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30413 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30414 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30415 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30416 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30417 auc 0.974179->0.974179 seed=1 init.name=IntervalRegressionCV algo=grid step=30418 auc 0.974179->0.974179 *** caught bus error *** address 0x147e2866a5e0, cause 'non-existent physical address' Traceback: 1: aum_sort_interface(error.diff.df, pred.vec) 2: aum(error.diff.df, pred.vec) 3: aum::aum_line_search(diffs.list$subtrain, X.subtrain, weight.vec) 4: (function (testFold.path, aum.type) { library(data.table) cv.path <- dirname(dirname(testFold.path)) folds.csv <- file.path(cv.path, "folds.csv") cv.type <- basename(cv.path) test.fold <- basename(testFold.path) data.dir <- dirname(dirname(cv.path)) data.name <- basename(data.dir) data.list <- list() for (f in c("inputs", "outputs", "evaluation")) { f.csv.xz <- file.path(data.dir, paste0(f, ".csv.xz")) if (file.exists(f.csv.xz)) { system(paste("unxz", f.csv.xz)) } f.csv <- file.path(data.dir, paste0(f, ".csv")) f.dt <- data.table::fread(f.csv) data.list[[f]] <- f.dt } data.list$evaluation[, `:=`(min.fn, min(fn)), by = sequenceID] data.list$evaluation[, `:=`(min.lambda = exp(min.log.lambda), example = sequenceID), by = sequenceID] bad <- data.list$evaluation[min.log.lambda == -Inf & min.fn < fn] if (nrow(bad)) { print(bad) } data.list$evaluation[min.log.lambda == -Inf & 0 < fn] data.list$aum.input <- data.table(data.list$evaluation)[, `:=`(possible.fn = possible.fn - min.fn, fn = fn - min.fn), by = sequenceID] folds.dt <- data.table::fread(folds.csv) folds.dt[fold == test.fold, `:=`(set, "test")] folds.dt[fold != test.fold, `:=`(set, rep(c("subtrain", "validation"), l = .N))] folds.dt[, table(fold, set)] X.all <- scale(data.list$inputs[, -1]) rownames(X.all) <- data.list$inputs$sequenceID X.finite <- X.all[, apply(is.finite(X.all), 2, all)] set.vec <- folds.dt[rownames(X.finite), set, on = "sequenceID"] seqs.list <- list() diffs.list <- list() aum.vec.list <- list() for (s in unique(folds.dt$set)) { seqs.set <- folds.dt[s == set, sequenceID] seqs.list[[s]] <- seqs.set seqs.diff <- aum::aum_diffs_penalty(data.list$aum.input, seqs.set, denominator = aum.type) diffs.list[[s]] <- seqs.diff zero <- rep(0, length(seqs.set)) aum.vec.list[[s]] <- rbind(`aum::aum` = aum::aum(seqs.diff, zero)$aum, `penaltyLearning::ROChange` = penaltyLearning::ROChange(data.list$evaluation, data.table(sequenceID = seqs.set, pred.log.lambda = zero), problem.vars = "sequenceID")$aum) } print(do.call(data.frame, aum.vec.list)) X.subtrain <- X.finite[set.vec == "subtrain", ] neg.t.X.subtrain <- -t(X.subtrain) seqs.train <- with(seqs.list, c(subtrain, validation)) y.train <- data.list[["outputs"]][seqs.train, cbind(min.log.lambda, max.log.lambda), on = "sequenceID"] keep <- apply(is.finite(y.train), 1, any) X.train <- X.finite[seqs.train, ] init.fun.list <- list(IntervalRegressionCV = function() { fit <- penaltyLearning::IntervalRegressionCV(X.train[keep, ], y.train[keep, ]) fit[["param.mat"]] }, zero = function() { N.param <- ncol(X.finite) + 1 rep(0, N.param) + rnorm(N.param) }) iteration.dt.list <- list() considered.dt.list <- list() obj.sign.list <- list(auc = -1, aum = 1) for (seed in 1:4) for (init.name in names(init.fun.list)) { init.fun <- init.fun.list[[init.name]] set.seed(seed) int.weights <- init.fun() for (algo in c("grid", "exact", "hybrid")) for (objective in names(obj.sign.list)) { obj.sign <- obj.sign.list[[objective]] weight.vec <- int.weights[-1] intercept <- int.weights[1] computeROC <- function(w, i, set) { pred.pen.vec <- (X.finite %*% w) + i pred.dt <- data.table(sequenceID = rownames(pred.pen.vec), pred.log.lambda = -as.numeric(pred.pen.vec)) is.set <- set.vec == set set.dt <- pred.dt[is.set] penaltyLearning::ROChange(data.list$evaluation, set.dt, "sequenceID") } prev.obj <- Inf * obj.sign new.obj <- -Inf * obj.sign step.number <- 0 while (obj.sign * (new.obj - prev.obj) < 1e-06) { step.number <- step.number + 1 summary.dt.list <- list() for (set in names(seqs.list)) { set.PL <- computeROC(weight.vec, intercept, set) summary.dt.list[[set]] <- with(set.PL, data.table(set, thresholds[threshold == "predicted"], auc, aum)) } prev.obj <- summary.dt.list$subtrain[[objective]] summary.dt <- do.call(rbind, summary.dt.list) iteration.dt.list[[paste(seed, init.name, algo, step.number, objective)]] <- data.table(seed, init.name, algo, step.number, objective, summary.dt) LS = aum::aum_line_search(diffs.list$subtrain, X.subtrain, weight.vec) pred.vec <- X.subtrain %*% weight.vec aum.list <- aum::aum(diffs.list$subtrain, pred.vec) pred.grad.vec <- rowMeans(aum.list$derivative_mat) direction.vec <- neg.t.X.subtrain %*% pred.grad.vec step.grid <- 10^seq(-9, 0) take.step <- function(s) { weight.vec + s * direction.vec } totals <- colSums(diffs.list$subtrain[, .(fp_diff, fn_diff)]) grid.dt <- data.table(step.size = step.grid)[, { step.weight <- take.step(step.size) grid.aum <- aum::aum(diffs.list$subtrain, X.subtrain %*% step.weight) before.dt <- data.table(grid.aum$total_error, key = "thresh")[, `:=`(TPR_before = 1 - fn_before/-totals[["fn_diff"]], FPR_before = fp_before/totals[["fp_diff"]])] auc <- before.dt[, .(FPR = c(FPR_before, 1), TPR = c(TPR_before, 1))][, sum((FPR[-1] - FPR[-.N]) * (TPR[-1] + TPR[-.N])/2)] data.table(auc, aum = grid.aum$aum) }, by = step.size] steps.considered <- rbind(if (algo != "grid") LS$line_search_result[, .(search = "exact", step.size, auc, aum)], if (algo != "exact") grid.dt[, .(search = "grid", step.size, auc, aum)])[, `:=`(step.prop, seq(1, .N)/.N), by = search][] considered.dt.list[[paste(seed, init.name, algo, objective, step.number)]] <- data.table(seed, init.name, algo, objective, step.number, steps.considered) best.step <- steps.considered[which.min(obj.sign * get(objective))] new.weight.vec <- take.step(best.step$step.size) after.roc <- computeROC(new.weight.vec, 0, "subtrain") new.obj <- after.roc[[objective]] cat(sprintf("seed=%d init.name=%s algo=%s step=%d %s %f->%f\n", seed, init.name, algo, step.number, objective, prev.obj, new.obj)) intercept <- after.roc$thresholds[threshold == "min.error", (max.thresh + min.thresh)/2] weight.vec <- new.weight.vec } } } list(sets = data.table(do.call(rbind, iteration.dt.list), data.name, cv.type, test.fold), steps = data.table(rbindlist(considered.dt.list), data.name, cv.type, test.fold))})(testFold.path = "../neuroblastoma-data/data/detailed/cv/sequenceID/testFolds/4", aum.type = "rate") 5: do.call(job$fun, job$pars, envir = .GlobalEnv) 6: with_preserve_seed({ set_seed(list(seed = seed, rng_kind = rng_kind)) code}) 7: with_seed(job$seed, do.call(job$fun, job$pars, envir = .GlobalEnv)) 8: execJob.Job(job) 9: execJob(job) 10: doTryCatch(return(expr), name, parentenv, handler) 11: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 12: tryCatchList(expr, classes, parentenv, handlers) 13: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))}) 14: try(execJob(job)) 15: doJobCollection.JobCollection(obj, output = output) 16: doJobCollection.character("/projects/genomic-ml/projects/max-generalized-auc/figure-line-grid-search-interactive-registry/jobs/job5386420cc536ada1bf6fc3acca28b641.rds") 17: batchtools::doJobCollection("/projects/genomic-ml/projects/max-generalized-auc/figure-line-grid-search-interactive-registry/jobs/job5386420cc536ada1bf6fc3acca28b641.rds") An irrecoverable exception occurred. R is aborting now ... /var/spool/slurm/slurmd/job63517202/slurm_script: line 13: 610491 Bus error (core dumped) Rscript -e 'batchtools::doJobCollection("/projects/genomic-ml/projects/max-generalized-auc/figure-line-grid-search-interactive-registry/jobs/job5386420cc536ada1bf6fc3acca28b641.rds")'