try calculate auc

lib/scripts/calc_t_per_meth.R

@@ -366,15 +366,45 @@ df_auc = do.call(rbind, lapply(split(df_out,paste0(df_out$methode,df_out$couple)
                                                 P = x$precision98_02
                                                 R = R[! is.na(P)]
                                                 P = P[! is.na(P)]
+                                                P = P[order(R)]    
+                                                R = R[order(R)]
+                                                
                                                 auc = 0
+                                                auc2 = 0
+                                                best_R_90_P = NA
+                                               
                                                 if (length(P) > 1) {
+                                                    # add point for step curves
+                                                    if (length(P) < 20) {
+                                                    P_tmp = P    
+                                                    R_tmp = R
+                                                    for (i in seq(1,length(P)-1)) {
+                                                        R_i = R[i] + (R[i+1] - R[i]) * 0.001
+                                                        P_i = P[i+1]
+                                                        P_tmp = c(P_tmp, P_i)
+                                                        R_tmp = c(R_tmp, R_i)
+                                                    }
+                                                    P = P_tmp[order(R_tmp)]
+                                                    R = R_tmp[order(R_tmp)]
+                                                    }
                                                 auc = auc(x = R, y = P, dens = 1)
+                                                best_R_90_P = max(0,R[P>=0.9])
+                                                P = c(P[1],P)  
+                                                R = c(0,R)
+                                                auc2 = auc(x = R, y = P, dens = 1)
                                                 }
-                                                df= data.frame(methode =x$methode[1] ,couple =x$couple[1] ,auc=auc)
+                                                
+                                                
+                                                df= data.frame(methode =x$methode[1] ,couple =x$couple[1] ,auc=auc,best_R_90_P=best_R_90_P,auc2=auc2)
                                                 return(df)
                                                 }))
 
 df_auc = df_auc[order(df_auc$couple),]
+condensed_meths = c("PCOC","Diffsel_mean","Identical_LG08","Mutinomial_1MinusLRT","Tdg09_1MinusLRT","Msd_1MinusP","Msd_0.05_1MinusP","Topological_LG08")
+df_auc = subset(df_auc, methode %in% condensed_meths)
+df_auc$auc_rank = unlist(tapply(df_auc$auc,df_auc$couple, function(x){rank(-x)}))
+df_auc$auc2_rank = unlist(tapply(df_auc$auc2,df_auc$couple, function(x){rank(-x)}))
+df_auc$best_R_90_P_rank = unlist(tapply(df_auc$best_R_90_P,df_auc$couple,function(x){rank(-x)}))
 print(df_auc)
 
 ########################################################################
@@ -558,7 +588,7 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
         plot = plot + guides(fill=FALSE) + theme(legend.position="top")
         plot = plot + labs(x=x_labs, y=y_labs)
         plot = plot + ylim(c(0,1)) + xlim(c(0,1))
-        plot = plot + geom_step(size=1, alpha=alpha)
+        plot = plot + geom_step(size=1,direction="vh", alpha=alpha)
         plot = plot + scale_color_manual(values=colors)
         plot = plot + geom_vline( data = df_recall_sup09_per_meth2 , aes(xintercept = threshold, col=couple), size = 0.5, show.legend = NA,linetype="dashed")
         plot = plot + facet_grid(. ~ methode, scales="free")
@@ -674,7 +704,7 @@ plot_out = function(df_out, df_d , df_recall_sup09_per_meth, meths = NULL, suffi
     plot = plot + guides(fill=FALSE) + theme(legend.position="top")
     plot = plot + labs(x=x_labs, y=y_labs) #+ ylim(y_lim)
     #plot = plot + geom_point(size=0.5, alpha=alpha)
-    plot = plot + geom_step(size=1, alpha=alpha)
+    plot = plot + geom_step(size=1, direction="vh",alpha=alpha)
     plot = plot + scale_color_manual(values=colors)
     #plot = plot + geom_hline( data = df_max_mcc_per_method_2, aes(yintercept = MCC), alpha=alpha, show.legend = NA)
     plot = plot + geom_vline( data = df_recall_sup09_per_meth, aes(xintercept = threshold, col=couple), size = 0.5, show.legend = NA,linetype="dashed")
@@ -834,5 +864,6 @@ write.table(df_d, file=paste0(opt$out,".complete_d.tsv"), row.names=FALSE, quote
 write.table(df_out, file=paste0(opt$out,".complete.tsv"), row.names=FALSE, quote=F, sep = "\t")
 write.table(df_auc, file=paste0(opt$out,".auc.tsv"), row.names=FALSE, quote=F, sep = "\t")
 write.table(subset(df_auc, methode %in% condensed_meths), file=paste0(opt$out,".auc_condensed.tsv"), row.names=FALSE, quote=F, sep = "\t")
+write.table(df_auc, file=paste0(opt$out,".auc_condensed.tsv"), row.names=FALSE, quote=F, sep = "\t")
 write.table(df_max_mcc_per_method, file=paste0(opt$out,".max_MCC_per_meth.tsv"), row.names=FALSE, quote=F, sep = "\t")
 write.table(df_recall_sup09_per_meth, file=paste0(opt$out,".recall09_per_meth.tsv"), row.names=FALSE, quote=F, sep = "\t")