Evaluate a collection of models.
library(knitr); library(pander); library(tibble); library(caret) library(AnalysisToolkit); library(MyUtils) library(ggrepel) devtools::load_all() knitr::opts_chunk$set(comment="#>", fig.show='hold', fig.align="center", fig.height=8, fig.width=8, message=FALSE, warning=FALSE, cache=FALSE, rownames.print=FALSE) ggplot2::theme_set(vizR::theme_()) set.seed(123)
kDIR_MULTITARGET <- file.path(hips::Fp_ml_dir(), "multitarget_vanilla") FpMultitarget <- function(s_chr) { MyUtils::fp_mostRecent(file.path(kDIR_MULTITARGET, s_chr), verbose = TRUE) } models <- readRDS(file = FpMultitarget("trained_models.rds")) load(FpMultitarget("test_cohort.Rdata")) target.info <- hipsInfo(targets) table(target.info$target_mode)
# Model inference ---- pY_lst <- map(models, predict_pY, newdata = test_df %>% unnest(pre)) Y_lst <- map(names(models), ~test_df[[.x]]) cCs <- pmap(list(pY_lst, Y_lst, names(models)), ~ClassifierCurve(pY = ..1, Y = ..2, id = ..3))
roc_tbl <- map(cCs, gg_data_roc) pretty_perf <- cCs %>% glance_pretty() %>% mapnames("Classifier", "target") %>% mutate(target = mapvalues(target, from = names(AES_TARGET_LABELS), to = unname(AES_TARGET_LABELS))) %>% arrange(desc(auc)) pretty_perf %>% kable(caption = "Performance of pretrained image models on various targets.") Tbl(pretty_perf, bn = "SuppTable2_MultitargetPerf")
# Target learnability perf_tbl <- glance(cCs) %>% mapnames("Classifier", "target") # ROC by target DATA <- merge(x = target.info, y = perf_tbl, by = "target") # Craft plot data intermediate kFN_OUT_INT <- "roc_plt_data.rds" fp_out_int <- file.path(kDIR_MULTITARGET, kFN_OUT_INT) if (!file.exists(fp_out_int)) { DATA %<>% arrange(desc(auc)) DATA$target %<>% fct_reorder(., DATA$auc) multitarget_roc_ci_df <- DATA[, c("target", "target_mode", "auc", "auc_lower", "auc_upper", "is_sig")] saveRDS(multitarget_roc_ci_df, fp_out_int) } gg_roc <- ggplot(DATA, aes(x = fct_reorder(target, auc), y = auc, ymin = auc_lower, ymax = auc_upper)) + geom_errorbar(width = 0.25, position = Pd()) + geom_point(position = Pd()) + geom_text(aes(label = is_sig), nudge_x = 0.2) gg_roc <- gg_roc + geom_hline(yintercept = 0.5, alpha = 0.5, linetype = 2) + geom_hline(yintercept = 1, alpha = 0.5) + theme(axis.text.x = element_text(angle = 90)) + scale_x_target() + labs(y = "AUROC +/- 95% bootstrap CI") + theme(strip.text.y = element_text(angle = 0), axis.text.x = element_text(vjust = 0.5)) + scale_y_continuous(breaks = c(0.5, 0.75, 1), labels = c(0.5, 0.75, 1)) + theme(legend.position = "bottom", legend.direction = "vertical") GG_ROC <- gg_roc %+% coord_flip() + labs(title = "A pre-trained CNN has non-random performance\non every classification target attempted") + theme(plot.title = element_text(hjust = 1)) GG_ROC + aes(col = target_mode) + scale_color_targetMode()
# ROC PRROC XY_byClassifier <- purrr::map(cCs, gg_data_roc) XY_byGeom <- purrr::transpose(XY_byClassifier) xy_geoms <- purrr::map(XY_byGeom, purrr::lift_dl(dplyr::bind_rows, .id = "Classifier")) xy_geoms$lines$Classifier %<>% as_factor() %>% fct_relevel(DATA$target) gg_roc <- ggplot2::ggplot(xy_geoms$lines, ggplot2::aes(x=x, y=y, col = Classifier)) + ggplot2::geom_line() + ggplot2::geom_point(data = xy_geoms$points, shape=10, size = 3) + ggplot2::geom_rug(data = xy_geoms$points) + ggplot2::geom_text(data = xy_geoms$points, aes(label = is_sig), size = 5, nudge_y = 0.03, show.legend=FALSE) + gg_style_roc XY_byGeom <- map(cCs, gg_data_prc) %>% transpose() xy_geoms <- map(XY_byGeom, lift_dl(bind_rows, .id="Classifier")) xy_geoms$lines$Classifier %<>% as_factor() %>% fct_relevel(DATA$target) gg_prc <- ggplot2::ggplot(xy_geoms$lines, ggplot2::aes(x=x, y=y, col=Classifier)) + ggplot2::geom_line() + ggplot2::geom_point(data = xy_geoms$points, shape=10, size = 3) + ggplot2::geom_rug(data = xy_geoms$points) + gg_style_prc gg_prc %<>% `+`(list(theme(legend.direction = "vertical", legend.position = "bottom"))) gg_prc %<>% `+`(guides(color = guide_legend(title = "Classification Target", ncol = 2, title.hjust = 0.5))) leg <- cowplot::get_legend(gg_prc) gg_prc %<>% `+`(NL) gg_roc %<>% `+`(NL) GG <- cowplot::plot_grid( cowplot::plot_grid(gg_roc, gg_prc, ncol = 2, align='hv'), cowplot::plot_grid(NULL, leg, NULL, ncol = 3, rel_widths = c(1, .1, 1)), ncol = 1, rel_heights = c(.6, 0.4)) GG # cowplot::save_plot("analysis/figures/ROC_PRROC.png", GG, base_width = 4, base_height = 6)
# ROC by Ntrain / Nlimiting library(cowplot) # extract sample sizes n_training_df <- map(models, train_n_eg) %>% lift_dl(bind_rows, .id = "target")() DATA <- merge(x = target.info, y = perf_tbl, by = "target") DATA <- merge(DATA, n_training_df, by= "target") DATA <- merge(DATA, AES_TARGET_LABELS %>% enframe(name = "target", "PrettyTarget"), by = "target") gg_nTrain <- DATA %>% { ggplot(., aes(x = n_train, y = auc, col = target_mode)) + geom_point() + scale_color_targetMode() + scale_x_continuous(labels = function(x) format(x, big.mark=",", scientific=FALSE)) + labs(x = "Total No. Training Samples") } GG_TRAIN <- gg_nTrain + geom_smooth(method = "lm", aes(group=1), se=FALSE, col = "#666666") + geom_text_repel(aes(label = PrettyTarget), force = 15) gg_nLimiting <- DATA %>% { ggplot(., aes(x = n_limiting, y = auc, col = target_mode)) + geom_point() + scale_color_targetMode() + scale_x_continuous(labels = function(x) format(x, big.mark=",", scientific=FALSE)) + labs(x = "Minor Class No. Training Samples", y = "AUROC") } GG_LIMIT <- gg_nLimiting + geom_smooth(method = "lm", aes(group=1), se=FALSE, col = "#666666") + geom_text_repel(aes(label = PrettyTarget), force = 15) LEG <- cowplot::get_legend(GG_TRAIN) GG_TRAIN %<>% `+`(NL) GG_TRAIN %<>% `+`(theme(axis.title.y = element_blank(), axis.text.y = element_blank())) GG_LIMIT %<>% `+`(NL) GG <- plot_grid(GG_LIMIT, GG_TRAIN, LEG, nrow=1, rel_widths = c(1, 0.85, 0.3)) GG cowplot::save_plot(filename = file.path(FS_hipsDir(), "analysis/figures/ROC_byN.tiff"), GG, base_width = 7, base_height = 5)
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