library(knitr) library(pander) knitr::opts_chunk$set(collapse=TRUE, comment="#>", results="markup", fig.show='hold', fig.align="center", fig.height=8, fig.width=8, message=FALSE, warning=FALSE, echo=TRUE, cache=TRUE) panderOptions("knitr.auto.asis", FALSE) theme_set( theme_bw(base_size=14) + theme( # legend.position="bottom", legend.key=element_rect(colour=NA), plot.margin=unit(c(0.5, 0.5, 0.5, 0.5), "cm") ) ) set.seed(123) PROJECT_DIR <- Sys.getenv('PROJECT_DIR')
data(test_df, package="ProjUtils") str(test_df)
kGOLD_STD <- "attg" # Make classifierPoints with logical columns cPs <- test_df %>% keep(.p=is.logical) %>% `[`(names(.) %ni% kGOLD_STD) %>% map(function(x_lgl) ClassifierPoint(Y_=x_lgl, Y=test_df[[kGOLD_STD]], id = test_df[["id"]])) # Make classifierCurves with numeric columns cCs <- test_df %>% keep(.p=is.numeric) %>% map(function(x_dbl) ClassifierCurve(pY=x_dbl, Y=test_df[[kGOLD_STD]], id = test_df[["id"]]))
glance()
Binary Classifier Performance metrics(lift_dl(rbind))(map(cPs, glance_ClassifierPoint)) %>% tibble::rownames_to_column(var="grader") %>% arrange(desc(acc)) %>% knitr::kable(digits=2)
glance()
Continuous Classifier Performance metrics(lift_dl(rbind))(map(cCs, glance_ClassifierCurve)) %>% tibble::rownames_to_column(var="grader") %>% arrange(desc(auc)) %>% knitr::kable(digits=2)
Pick a single cnn_4
map_dfr(cCs, roc, .id = "cnn") %>% ggplot(., aes(x=x, y=y, col=cnn)) + geom_line() + AnalysisToolkit:::gg_roc_layers cCs["cnn_4"] <- cCs["cnn_41"] cCs[c("cnn_41", "cnn_42", "cnn_43")] <- NULL
(lift_dl(rbind))(map(cCs, glance_ClassifierCurve)) %>% tibble::rownames_to_column() %>% arrange(desc(auc)) %>% knitr::kable(digits=2)
classifiers <- list( "classifier_points" = cPs, "classifier_curves" = cCs ) str(classifiers) #devtools::use_data(classifiers, overwrite = TRUE)
Sys.info() search()
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