diff --git a/descriptive-statistics/statistics-ph2-dsc.csv b/descriptive-statistics/statistics-ph2-dsc.csv index 472f301..f3ed91b 100644 --- a/descriptive-statistics/statistics-ph2-dsc.csv +++ b/descriptive-statistics/statistics-ph2-dsc.csv @@ -1,20 +1,20 @@ "","Algorithm","P25","Median","P75","IQR" -"1","BCVuniandes","0.423388600684908","0.7011167318889","0.861729833082426","0.438341232397518" -"2","K.A.V.athlon","0.581387808041505","0.770486010728855","0.873702422145329","0.292314614103824" -"3","CerebriuDIKU","0.51226332346288","0.75800623669752","0.875379939209726","0.363116615746846" -"4","EdwardMa12593","0.00383790249218167","0.308366931293902","0.688699732115967","0.684861829623785" -"5","MIMI","0.513062011912083","0.732402974432108","0.86415963161934","0.351097619707257" -"6","VST","0.391332631187659","0.689381823238996","0.835212828368424","0.443880197180765" -"7","AI-MED","0.297526186761756","0.629521269185076","0.792045644441261","0.494519457679505" -"8","nnU-Net","0.613936773541468","0.79133426709701","0.88486221215553","0.270925438614062" -"9","jiafucang","0.112569667562783","0.494374091758356","0.807954545454545","0.695384877891762" -"10","Lesswire1","0.326368685532928","0.650127681307457","0.787185066458417","0.460816380925489" -"11","lupin","0.517472794845058","0.745263366729491","0.862833521232619","0.345360726387561" -"12","BUT","0.398921832884097","0.723801535711789","0.844167962674961","0.445246129790864" -"13","LfB","0.426606717799524","0.675455815667887","0.815674891146589","0.389068173347065" -"14","NVDLMED","0.573335805674022","0.779713222187271","0.869630190197478","0.296294384523456" -"15","LS Wang's Group","0.505575029353537","0.748289888911184","0.875850340136054","0.370275310782517" -"16","UBIlearn","0.404002746983224","0.722521936951576","0.853594891228806","0.449592144245582" -"17","whale","0.275561097256858","0.648878617574452","0.832727272727273","0.557166175470415" -"18","RegionTec","0.185330284866836","0.571340142370783","0.730061349693252","0.544731064826416" -"19","A-REUMI01","0.415258537185381","0.702069446351795","0.853465846225518","0.438207309040137" +"1","RegionTec","0","0.292939257128055","0.504963153238872","0.504963153238872" +"2","A-REUMI01","0.139277560973839","0.510377871097316","0.649464049047015","0.510186488073176" +"3","BCVuniandes","0.0101722237273957","0.10348763684525","0.381128504107312","0.370956280379916" +"4","K.A.V.athlon","0.488606365033984","0.67175523291836","0.797879165035651","0.309272800001667" +"5","CerebriuDIKU","0.150974168520916","0.564311608040027","0.705144331071378","0.554170162550462" +"6","EdwardMa12593","0.00992688841975129","0.0828854819806497","0.1802541688333","0.170327280413549" +"7","MIMI","0.445080608811342","0.648996553644884","0.749025133928528","0.303944525117186" +"8","VST","0","0.408089696842264","0.641251714864854","0.641251714864854" +"9","AI-MED","0.00793220023761484","0.334029934489337","0.521482963221238","0.513550762983623" +"10","nnU-Net","0.575740244642305","0.711430656080371","0.820800003467194","0.245059758824888" +"11","jiafucang","0.040811614775516","0.483611286791732","0.667366555179116","0.6265549404036" +"12","Lesswire1","0.0787915980982831","0.400252120967285","0.518683671070803","0.43989207297252" +"13","lupin","0.189322432424537","0.571551881101535","0.693127501531446","0.503805069106909" +"14","BUT","0.00724702984006717","0.383785127118883","0.603222952846831","0.595975923006764" +"15","LfB","0.158342409280643","0.491783121999397","0.636179728203088","0.477837318922445" +"16","NVDLMED","0.547596728291421","0.692515331844589","0.792722114878901","0.24512538658748" +"17","LS Wang's Group","0.459466650888111","0.639872399962687","0.780891400192111","0.321424749304" +"18","UBIlearn","0.0452767187461065","0.547162956535861","0.693431629275872","0.648154910529765" +"19","whale","0.201282465338449","0.552742325121933","0.677205616052239","0.47592315071379" diff --git a/descriptive-statistics/statistics-ph2-nsd.csv b/descriptive-statistics/statistics-ph2-nsd.csv index e886b7b..ae00629 100644 --- a/descriptive-statistics/statistics-ph2-nsd.csv +++ b/descriptive-statistics/statistics-ph2-nsd.csv @@ -1,20 +1,20 @@ "","Algorithm","P25","Median","P75","IQR" -"1","BCVuniandes","0.74335396","0.92217857","0.97813","0.23477604" -"2","K.A.V.athlon","0.8455735","0.95486796","0.98759484","0.14202134" -"3","CerebriuDIKU","0.79732275","0.9434328","0.984833","0.18751025" -"4","EdwardMa12593","0.061272882","0.5494698","0.873861","0.812588118" -"5","MIMI","0.77753896","0.93238926","0.9810406","0.20350164" -"6","VST","0.6993637","0.89474523","0.96285486","0.26349116" -"7","AI-MED","0.61282206","0.8050565","0.9183326","0.30551054" -"8","nnU-Net","0.87228984","0.96595234","0.9911397","0.11884986" -"9","jiafucang","0.31107643","0.7928946","0.9526199","0.64154347" -"10","Lesswire1","0.67003256","0.87214977","0.939495","0.26946244" -"11","lupin","0.80983406","0.95018315","0.98521686","0.1753828" -"12","BUT","0.7385204","0.9185186","0.97278285","0.23426245" -"13","LfB","0.6483316","0.84594953","0.9349045","0.2865729" -"14","NVDLMED","0.8310241","0.95014876","0.9873143","0.1562902" -"15","LS Wang's Group","0.7934901","0.9428301","0.9874466","0.1939565" -"16","UBIlearn","0.7157965","0.9148736","0.97586864","0.26007214" -"17","whale","0.610581","0.8991979","0.9680677","0.3574867" -"18","RegionTec","0.52181375","0.78464997","0.9173176","0.39550385" -"19","A-REUMI01","0.69519997","0.9087221","0.9691808","0.27398083" +"1","RegionTec","0","0.53582918","0.7501758","0.7501758" +"2","A-REUMI01","0.24750099","0.70550305","0.8490844875","0.6015834975" +"3","BCVuniandes","0.10370205875","0.399053795","0.646592275","0.54289021625" +"4","K.A.V.athlon","0.65740651","0.8685043","0.92695871","0.2695522" +"5","CerebriuDIKU","0.31964796","0.76532195","0.8705862","0.55093824" +"6","EdwardMa12593","0.048273794","0.172797175","0.34692804","0.298654246" +"7","MIMI","0.543586425","0.814816355","0.904751425","0.361165" +"8","VST","0","0.53334103","0.79236325","0.79236325" +"9","AI-MED","0.05466854275","0.583872285","0.7274329375","0.67276439475" +"10","nnU-Net","0.76946972","0.8820259","0.94639511","0.17692539" +"11","jiafucang","0.1141623325","0.67034558","0.8431633975","0.729001065" +"12","Lesswire1","0.1578660475","0.5629189","0.761390625","0.6035245775" +"13","lupin","0.302462595","0.7794152","0.8795834875","0.5771208925" +"14","BUT","0.04466459475","0.599635955","0.778182475","0.73351788025" +"15","LfB","0.251027345","0.65551918","0.8342983725","0.5832710275" +"16","NVDLMED","0.7212675825","0.86280165","0.9353750625","0.21410748" +"17","LS Wang's Group","0.63282345","0.8238938","0.921952325","0.289128875" +"18","UBIlearn","0.1697991275","0.74922492","0.86382562","0.6940264925" +"19","whale","0.431902315","0.780006525","0.8814405125","0.4495381975" diff --git a/msd-descriptive-statistics.R b/msd-descriptive-statistics.R index 8c3fb94..d070753 100644 --- a/msd-descriptive-statistics.R +++ b/msd-descriptive-statistics.R @@ -1,46 +1,46 @@ library(rstudioapi) current_path = rstudioapi::getActiveDocumentContext()$path setwd(dirname(current_path)) library(dplyr) source("msd-prepare-data.R") ########################### # Define functions ########################### compute_statistics <- function(data) { algorithms <- unique(data$alg_name) statistics <- data.frame(Algorithm = character(0), P25 = numeric(0), Median = numeric(0), P75 = numeric(0), IQR = numeric(0)) for(algorithm in algorithms) { data_algorithm <- data %>% subset(alg_name==algorithm) quantile <- quantile(data_algorithm$value,probs=c(0.25,0.50,0.75)) statistics[nrow(statistics)+1,] <- c(algorithm, quantile, quantile[3]-quantile[1]) } return(statistics) } ########################### # Read and prepare challenge data ########################### msd_data_matrix <- prepare_data() # Prepare data subsets for each metric and phase dataDSCph1= msd_data_matrix %>% subset(score=="DSC" & phase==1) dataNSDph1= msd_data_matrix %>% subset(score=="NSD" & phase==1) dataDSCph2= msd_data_matrix %>% subset(score=="DSC" & phase==2) dataNSDph2= msd_data_matrix %>% subset(score=="NSD" & phase==2) statistics_ph1_dsc <- compute_statistics(dataDSCph1) statistics_ph1_nsd <- compute_statistics(dataNSDph1) statistics_ph2_dsc <- compute_statistics(dataDSCph2) statistics_ph2_nsd <- compute_statistics(dataNSDph2) write.csv(statistics_ph1_dsc, "descriptive-statistics/statistics-ph1-dsc.csv") write.csv(statistics_ph1_nsd, "descriptive-statistics/statistics-ph1-nsd.csv") -write.csv(statistics_ph1_dsc, "descriptive-statistics/statistics-ph2-dsc.csv") -write.csv(statistics_ph1_nsd, "descriptive-statistics/statistics-ph2-nsd.csv") +write.csv(statistics_ph2_dsc, "descriptive-statistics/statistics-ph2-dsc.csv") +write.csv(statistics_ph2_nsd, "descriptive-statistics/statistics-ph2-nsd.csv")