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library(ggplot2) |
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task.dic <- list("fluorescence"=c("score"="fluorescence")) |
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genes <- c("fluorescence") |
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source('./AUROC.R') |
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alphabet <- c('<cls>', '<pad>', '<eos>', '<unk>', |
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'L', 'A', 'G', 'V', 'S', 'E', 'R', 'T', 'I', 'D', |
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'P', 'K', 'Q', 'N', 'F', 'Y', 'M', 'H', 'W', 'C', |
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'X', 'B', 'U', 'Z', 'O', '.', '-', |
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'<null_1>', '<mask>') |
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result <- data.frame() |
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dash.base.line.models <- data.frame() |
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for (i in 1:length(genes)) { |
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task.length <- length(task.dic[[genes[i]]]) |
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for (subset in 1:6) { |
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for (fold in 1:4) { |
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|
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if (!subset %in% c(1,2,4,6,8)) { |
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test.result <- read.csv(paste0('../', |
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yaml::read_yaml(paste0('../scripts/PreMode.mean.var/', |
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genes[i], '/', genes[i], '.seed.', |
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fold, '.yaml'))$log_dir, '/testing.round.', subset-1, '.csv')) |
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baseline.auc.1 <- list(R2=rep(NA, task.length)) |
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} else { |
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test.result <- read.csv(paste0('../', |
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yaml::read_yaml(paste0('../scripts/PreMode.mean.var/', |
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genes[i], '/', genes[i], '.seed.', |
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fold, '.yaml'))$log_dir, '/testing.round.', subset-1, '.csv')) |
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baseline.result.1 <- read.csv(paste0('PreMode/', genes[i], '/', |
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'/testing.subset.', subset, '.fold.', fold, '.csv')) |
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baseline.auc.1 <- plot.R2(baseline.result.1[,names(task.dic[[genes[i]]])], |
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baseline.result.1[,paste0("logits")], |
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bin = grepl("bin", genes[i])) |
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} |
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PreMode.auc <- plot.R2(test.result[,names(task.dic[[genes[i]]])], |
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test.result[,paste0("logits")], |
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bin = grepl("bin", genes[i])) |
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to.append <- data.frame(min.val.R = c(PreMode.auc$R2, |
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baseline.auc.1$R2), |
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task.name = paste0(genes[i], ":", rep(task.dic[[genes[i]]], 2))) |
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to.append$model <- rep(c("PreMode (Adaptive Learning)", |
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"PreMode" |
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), each = task.length) |
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to.append$subset <- subset |
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to.append$seed <- fold |
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result <- rbind(result, to.append) |
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} |
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} |
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} |
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num.models <- unique(result$model) |
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plots <- list() |
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library(patchwork) |
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for (i in 1:length(task.dic)) { |
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task <- names(task.dic)[i] |
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task.res <- result[startsWith(result$task.name, paste0(task, ":")),] |
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task.res <- task.res[,!is.na(task.res[1,])] |
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assays <- length(task.dic[[i]]) |
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data.points <- c() |
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for (subset in 1:6) { |
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train.file <- read.csv(paste0('../', yaml::read_yaml(paste0('../scripts/PreMode.mean.var/', |
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genes[i], '/', genes[i], '.seed.', |
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fold, '.yaml'))$log_dir, '/data_file_train.round.', subset-1, '.csv')) |
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data.points <- c(data.points, sum(train.file$split=='train')) |
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} |
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task.plots <- list() |
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for (k in 1:length(num.models)) { |
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model <- num.models[k] |
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to.plot <- task.res[task.res$model==model,] |
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to.plot <- to.plot[!is.na(to.plot$min.val.R),] |
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|
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to.plot.uniq <- to.plot[to.plot$seed==1,] |
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for (j in 1:dim(to.plot.uniq)[1]) { |
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rhos <- to.plot$min.val.R[to.plot$subset==to.plot.uniq$subset[j]] |
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rhos <- rhos[rhos>0] |
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to.plot.uniq$rho[j] <- mean(rhos, na.rm = T) |
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to.plot.uniq$rho.sd[j] <- sd(rhos, na.rm = T) |
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} |
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to.plot.uniq$task.name <- 'fluorescence' |
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p <- ggplot(to.plot.uniq, aes(x=subset, y=rho, col=task.name)) + |
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geom_point() + |
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geom_line() + |
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geom_errorbar(aes(ymin=rho-rho.sd, ymax=rho+rho.sd), width=.2) + |
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scale_y_continuous(breaks=seq(0.4, 0.8, 0.2), limits = c(0.4, 0.8)) + |
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scale_x_continuous(breaks=1:6, |
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labels=paste0(data.points, |
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paste0(" (", 1:6, "0%)"))) + |
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labs(col = "Fluorescence") + |
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theme_bw() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + |
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ggtitle(paste0(task, ":", model)) + ggeasy::easy_center_title() + xlab("training data size (%)") |
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p <- p + geom_abline(slope=0, intercept=0.69, linetype='dashed') + |
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geom_text(x=2, y=0.72, label='rho=0.69', col='black') |
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task.plots[[k]] <- p |
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} |
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plots[[i]] <- task.plots[[1]] + task.plots[[2]] |
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} |
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library(patchwork) |
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p <- plots[[1]] |
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ggsave(p, filename = paste0("figs/fig.sup.14.pdf"), width = 10, height = 4.5) |
