diff --git a/README.md b/README.md index aada51c..54918b0 100644 --- a/README.md +++ b/README.md @@ -1,653 +1,653 @@ Methods and open-source toolkit for analyzing and visualizing challenge results ================ - [Introduction](#introduction) - [Installation](#installation) - [Terms of use](#terms-of-use) - [Usage](#usage) - [Troubleshooting](#troubleshooting) - [Changes](#changes) - [Team](#team) - [Reference](#reference) Note that this is ongoing work (version 0.3.3), there may be updates with possibly major changes. *Please make sure that you use the latest version\!* The change log can be found in section “Changes”. # Introduction The current framework is a tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond. Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms. While the number of challenges is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the current framework aims to enable fast and wide adoption of comprehensively analyzing and visualizing the results of single-task and multi-task challenges. This approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. # Installation Requires R version \>= 3.5.2 (). Further, a recent version of Pandoc (\>= 1.12.3) is required. RStudio () automatically includes this so you do not need to download Pandoc if you plan to use rmarkdown from the RStudio IDE, otherwise you’ll need to install Pandoc for your platform (). Finally, if you want to generate a PDF report you will need to have LaTeX installed (e.g. MiKTeX, MacTeX or TinyTeX). To get the latest released version (master branch) of the R package from GitHub: ``` r if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("Rgraphviz", dependencies = TRUE) devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` If you are asked whether you want to update installed packages and you type “a” for all, you might need administrator permissions to update R core packages. You can also try to type “n” for updating no packages. If you are asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)”, you can safely type “no”. If you get *warning* messages (in contrast to *error* messages), these might not be problematic and you can try to proceed. If you encounter errors during the setup, looking into the “Troubleshooting” section might be worth it. # Terms of use Licenced under GPL-3. If you use this software for a publication, cite Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. (2019). Methods and open-source toolkit for analyzing and visualizing challenge results. *arXiv preprint arXiv:1910.05121* # Usage -Each of the following steps have to be run to generate the report: (1) +Each of the following steps has to be run to generate the report: (1) Load package, (2) load data, (3) perform ranking, (4) perform bootstrapping and (5) generation of the report Here, we provide a step-by-step guide that leads you to your final report. ## 1\. Load package Load package ``` r library(challengeR) ``` ## 2\. Load data ### Data requirements Data requires the following *columns*: - *task identifier* in case of multi-task challenges (string or numeric) - *test case identifier* (string or numeric) - *algorithm identifier* (string or numeric) - *metric value* (numeric) In case of missing metric values, a missing observation has to be provided (either as blank field or “NA”). For example, in a challenge with 2 tasks, 2 test cases and 2 algorithms, where in task “T2”, test case “case2”, algorithm “A2” didn’t give a prediction (and thus NA or a blank field for missing value is inserted), the data set might look like this: | Task | TestCase | Algorithm | MetricValue | | :--- | :------- | :-------- | ----------: | | T1 | case1 | A1 | 0.266 | | T1 | case1 | A2 | 0.202 | | T1 | case2 | A1 | 0.573 | | T1 | case2 | A2 | 0.945 | | T2 | case1 | A1 | 0.372 | | T2 | case1 | A2 | 0.898 | | T2 | case2 | A1 | 0.908 | | T2 | case2 | A2 | NA | ### 2.1 Load data from file If you have assessment data at hand stored in a csv file (if you want to use simulated data skip the following code line) use ``` r data_matrix=read.csv(file.choose()) # type ?read.csv for help ``` This allows to choose a file interactively, otherwise replace *file.choose()* by the file path (in style “/path/to/dataset.csv”) in quotation marks. ### 2.2 Simulate data In the following, simulated data is generated *instead* for illustration purposes (skip the following code chunk if you have already loaded data). The data is also stored as “inst/extdata/data\_matrix.csv” in the repository. ``` r if (!requireNamespace("permute", quietly = TRUE)) install.packages("permute") n <- 50 set.seed(4) strip <- runif(n,.9,1) c_ideal <- cbind(task="c_ideal", rbind( data.frame(alg_name="A1",value=runif(n,.9,1),case=1:n), data.frame(alg_name="A2",value=runif(n,.8,.89),case=1:n), data.frame(alg_name="A3",value=runif(n,.7,.79),case=1:n), data.frame(alg_name="A4",value=runif(n,.6,.69),case=1:n), data.frame(alg_name="A5",value=runif(n,.5,.59),case=1:n) )) set.seed(1) c_random <- data.frame(task="c_random", alg_name=factor(paste0("A",rep(1:5,each=n))), value=plogis(rnorm(5*n,1.5,1)),case=rep(1:n,times=5) ) strip2 <- seq(.8,1,length.out=5) a <- permute::allPerms(1:5) c_worstcase <- data.frame(task="c_worstcase", alg_name=c(t(a)), value=rep(strip2,nrow(a)), case=rep(1:nrow(a),each=5) ) c_worstcase <- rbind(c_worstcase, data.frame(task="c_worstcase",alg_name=1:5,value=strip2,case=max(c_worstcase$case)+1) ) c_worstcase$alg_name <- factor(c_worstcase$alg_name,labels=paste0("A",1:5)) data_matrix <- rbind(c_ideal, c_random, c_worstcase) ``` ## 3\. Perform ranking ### 3.1 Define challenge object Code differs slightly for single- and multi-task challenges. In case of a single-task challenge use ``` r # Use only task "c_random" in object data_matrix dataSubset <- subset(data_matrix, task=="c_random") challenge <- as.challenge(dataSubset, # Specify which column contains the algorithms, # which column contains a test case identifier # and which contains the metric value: algorithm = "alg_name", case = "case", value = "value", # Specify if small metric values are better smallBetter = FALSE) ``` *Instead*, for a multi-task challenge use ``` r # Same as above but with 'by="task"' where variable "task" contains the task identifier challenge=as.challenge(data_matrix, by = "task", algorithm = "alg_name", case = "case", value = "value", smallBetter = FALSE) ``` ### 3.2 Configure ranking Different ranking methods are available, choose one of them: - for “aggregate-then-rank” use (here: take mean for aggregation) ``` r ranking <- challenge%>%aggregateThenRank(FUN = mean, # aggregation function, # e.g. mean, median, min, max, # or e.g. function(x) quantile(x, probs=0.05) na.treat = 0, # either "na.rm" to remove missing data, # set missings to numeric value (e.g. 0) # or specify a function, # e.g. function(x) min(x) ties.method = "min" # a character string specifying # how ties are treated, see ?base::rank ) ``` - *alternatively*, for “rank-then-aggregate” with arguments as above (here: take mean for aggregation) ``` r ranking <- challenge%>%rankThenAggregate(FUN = mean, ties.method = "min" ) ``` - *alternatively*, for test-then-rank based on Wilcoxon signed rank test ``` r ranking <- challenge%>%testThenRank(alpha = 0.05, # significance level p.adjust.method = "none", # method for adjustment for # multiple testing, see ?p.adjust na.treat = 0, # either "na.rm" to remove missing data, # set missings to numeric value (e.g. 0) # or specify a function, e.g. function(x) min(x) ties.method = "min" # a character string specifying # how ties are treated, see ?base::rank ) ``` ## 4\. Perform bootstrapping Perform bootstrapping with 1000 bootstrap samples using one CPU ``` r set.seed(1) ranking_bootstrapped <- ranking%>%bootstrap(nboot = 1000) ``` If you want to use multiple CPUs (here: 8 CPUs), use ``` r library(doParallel) registerDoParallel(cores = 8) set.seed(1) ranking_bootstrapped <- ranking%>%bootstrap(nboot = 1000, parallel = TRUE, progress = "none") stopImplicitCluster() ``` ## 5\. Generate the report Generate report in PDF, HTML or DOCX format. Code differs slightly for single- and multi-task challenges. ### 5.1 For single-task challenges ``` r ranking_bootstrapped %>% report(title = "singleTaskChallengeExample", # used for the title of the report file = "filename", format = "PDF", # format can be "PDF", "HTML" or "Word" latex_engine = "pdflatex", #LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex" clean = TRUE #optional. Using TRUE will clean intermediate files that are created during rendering. ) ``` Argument *file* allows for specifying the output file path as well, otherwise the working directory is used. If file is specified but does not have a file extension, an extension will be automatically added according to the output format given in *format*. Using argument *clean=FALSE* allows to retain intermediate files, such as separate files for each figure. If argument “file” is omitted, the report is created in a temporary folder with file name “report”. ### 5.2 For multi-task challenges Same as for single-task challenges, but additionally consensus ranking (rank aggregation across tasks) has to be given. Compute ranking consensus across tasks (here: consensus ranking according to mean ranks across tasks) ``` r # See ?relation_consensus for different methods to derive consensus ranking meanRanks <- ranking%>%consensus(method = "euclidean") meanRanks # note that there may be ties (i.e. some algorithms have identical mean rank) ``` Generate report as above, but with additional specification of consensus ranking ``` r ranking_bootstrapped %>% report(consensus = meanRanks, title = "multiTaskChallengeExample", file = "filename", format = "PDF", # format can be "PDF", "HTML" or "Word" latex_engine = "pdflatex"#LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex" ) ``` # Troubleshooting In this section we provide an overview of issues that the users reported and how they were solved. ### RStudio specific #### \- Warnings while installing the GitHub repository ##### Error: While trying to install the current version of the repository: ``` r devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` The following warning showed up in the output: ``` r WARNING: Rtools is required to build R packages, but is not currently installed. ``` Therefore, Rtools was installed via a separate executable: and the warning disappeared. ##### Solution: Actually there is no need of installing Rtools, it is not really used in the toolkit. Insted, choose not to install it when it is asked. See comment in the installation section: “If you are asked whether you want to update installed packages and you type “a” for all, you might need administrator rights to update R core packages. You can also try to type “n” for updating no packages. If you are asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)”, you can safely type “no”.” #### \- Unable to install the current version of the tool from GitHub ##### Error: While trying the current version of the tool from GitHub, it was unable to install. The error message was: ``` r byte-compile and prepare package for lazy loading Error: (converted from warning) package 'ggplot2' was built under R version 3.6.3 Execution halted ERROR: lazy loading failed for package 'challengeR' * removing 'C:/Users/.../Documents/R/win-library/3.6/challengeR' * restoring previous 'C:/Users/.../Documents/R/win-library/3.6/challengeR' Error: Failed to install 'challengeR' from GitHub: (converted from warning) installation of package 'C:/Users/.../AppData/Local/Temp/Rtmp615qmV/file4fd419555eb4/challengeR_0.3.1.tar.gz' had non-zero exit status ``` The problem was that some of the packages that were built under R3.6.1 had been updated, but the current installed version was still R3.6.1. ##### Solution: The solution was to update R3.6.1 to R3.6.3. Another way would have been to reset the single packages to the versions built under R3.6.1 #### \- Unable to install the toolkit from GitHub ##### Error: While trying the current version of the tool from GitHub, it was unable to install. ``` r devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` The error message was: ``` r Error: .onLoad failed in loadNamespace() for 'pkgload', details: call: loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) error: there is no package called ‘backports’ ``` The problem was that the packages ‘backports’ had not been installed. ##### Solution: The solution was to install ‘backports’ manually. ``` r install.packages("backports") ``` #### \- Unable to install R ##### Error: While trying to install the package in the R, after running the following commands: ``` r if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("Rgraphviz", dependencies = TRUE) devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` The error message was: ``` r ERROR: 1: In file(con, "r") : URL 'https://bioconductor.org/config.yaml': status was 'SSL connect error' 2: packages ‘BiocVersion’, ‘Rgraphviz’ are not available (for R version 3.6.1) ``` ##### Solution: The solution was to restart RStudio. #### \- Incorrect column order ##### Error: When naming the columns “task” and “case”, R was confused because the arguments in the challenge object are also called like this and it produced the following error: ``` r Error in table(object[[task]][[algorithm]], object[[task]][[case]]) : all arguments must have the same length ``` ##### Solution: The solution was to rename the columns. #### \- Wrong versions of packages ##### Error: While running this command : ``` r devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` I had the following errors : - Error : the package ‘purrr’ has been compiled with version of R 3.6.3 - Error : the package ‘ggplot2’ has been compiled with version of R 3.6.3 - Error in loadNamespace(j \<- i\[\[L\]\], c(lib.loc, .libPaths()), versionCheck = vI\[\[j\]\]) namespace ‘glue’ 1.3.1 is already loaded, but \>= 1.3.2 is required ##### Solution: To solve the issue I changed the versions of the packages. I had the following versions : - purrr 0.3.4 - ggplot2 3.3.2 - glue 1.3.1 I moved to the following ones : - purrr 0.3.3 - ggplot2 3.3.0 - glue 1.4.2 ### Related to MiKTeX #### \- Missing packages ##### Error: While generating the PDF with MiKTeX (2.9), the following error showed up: ``` r fatal pdflatex - gui framework cannot be initialized ``` There is an issue with installing missing packages in LaTeX. ##### Solution: Open your MiKTeX Console –\> Settings, select “Always install missing packages on-the-fly”. Then generate the report. Once the report is generated, you can reset the settings to your preferred ones. #### \- Unable to generate report ##### Error: While generating the PDF with MiKTeX (2.9): ``` r ranking_bootstrapped %>% report(title = "singleTaskChallengeExample", # used for the title of the report file = "filename", format = "PDF", # format can be "PDF", "HTML" or "Word" latex_engine = "pdflatex", #LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex" clean = TRUE #optional. Using TRUE will clean intermediate files that are created during rendering. ) ``` The following error showed up: ``` r output file: filename.knit.md "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS filename.utf8.md --to latex --from markdown+autolink_bare_uris+tex_math_single_backslash --output filename.tex --self-contained --number-sections --highlight-style tango --pdf-engine pdflatex --variable graphics --lua-filter "C:/Users/adm/Documents/R/win-library/3.6/rmarkdown/rmd/lua/pagebreak.lua" --lua-filter "C:/Users/adm/Documents/R/win-library/3.6/rmarkdown/rmd/lua/latex-div.lua" --variable "geometry:margin=1in" Error: LaTeX failed to compile filename.tex. See https://yihui.org/tinytex/r/#debugging for debugging tips. Warning message: In system2(..., stdout = if (use_file_stdout()) f1 else FALSE, stderr = f2) : '"pdflatex"' not found ``` ##### Solution: The solution was to restart RStudio. # Changes #### Version 0.3.3 - Force line break to avoid that authors exceed the page in generated PDF reports #### Version 0.3.2 - Correct names of authors #### Version 0.3.1 - Refactoring #### Version 0.3.0 - Major bug fix release #### Version 0.2.5 - Bug fixes #### Version 0.2.4 - Automatic insertion of missings #### Version 0.2.3 - Bug fixes - Reports for subsets (top list) of algorithms: Use e.g. `subset(ranking_bootstrapped, top=3) %>% report(...)` (or `subset(ranking, top=3) %>% report(...)` for report without bootstrap results) to only show the top 3 algorithms according to the chosen ranking methods, where `ranking_bootstrapped` and `ranking` objects as defined in the example. Line plot for ranking robustness can be used to check whether algorithms performing well in other ranking methods are excluded. Bootstrapping still takes entire uncertainty into account. Podium plot and ranking heatmap neglect excluded algorithms. Only available for single-task challenges (for multi-task challenges not sensible because each task would contain a different set of algorithms). - Reports for subsets of tasks: Use e.g. `subset(ranking_bootstrapped, tasks=c("task1", "task2","task3")) %>% report(...)` to restrict report to tasks “task1”, “task2”,"task3. You may want to recompute the consensus ranking before using `meanRanks=subset(ranking, tasks=c("task1", "task2", "task3"))%>%consensus(method = "euclidean")` #### Version 0.2.1 - Introduction in reports now mentions e.g. ranking method, number of test cases,… - Function `subset()` allows selection of tasks after bootstrapping, e.g. `subset(ranking_bootstrapped,1:3)` - `report()` functions gain argument `colors` (default: `default_colors`). Change e.g. to `colors=viridisLite::inferno` which “is designed in such a way that it will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. It is also designed to be perceived by readers with the most common form of color blindness.” See package `viridis` for further similar functions. #### Version 0.2.0 - Improved layout in case of many algorithms and tasks (while probably still not perfect) - Consistent coloring of algorithms across figures - `report()` function can be applied to ranked object before bootstrapping (and thus excluding figures based on bootstrapping), i.e. in the example `ranking %>% report(...)` - bug fixes # Team The developer team includes members from both division of Computer Assisted Medical Interventions (CAMI) and Biostatistics at the German Cancer Research Center (DKFZ): - Manuel Wiesenfarth - Annette Kopp-Schneider - Annika Reinke - Matthias Eisenmann - Laura Aguilera Saiz - Elise Récéjac - Lena Maier-Hein # Reference Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. (2019). Methods and open-source toolkit for analyzing and visualizing challenge results. *arXiv preprint arXiv:1910.05121* diff --git a/Readme.Rmd b/Readme.Rmd index f2dc049..d5dc669 100644 --- a/Readme.Rmd +++ b/Readme.Rmd @@ -1,549 +1,549 @@ --- title: "Methods and open-source toolkit for analyzing and visualizing challenge results" output: github_document: toc: yes toc_depth: '1' html_document: toc: yes toc_depth: '1' pdf_document: toc: yes toc_depth: '1' editor_options: chunk_output_type: console --- ```{r, echo = FALSE} knitr::opts_chunk$set( collapse = TRUE, comment = "#>", # fig.path = "README-", fig.width = 9, fig.height = 5, width=160 ) ``` Note that this is ongoing work (version `r packageVersion("challengeR")`), there may be updates with possibly major changes. *Please make sure that you use the latest version!* The change log can be found in section "Changes". # Introduction The current framework is a tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond. Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms. While the number of challenges is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the current framework aims to enable fast and wide adoption of comprehensively analyzing and visualizing the results of single-task and multi-task challenges. This approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. # Installation Requires R version >= 3.5.2 (https://www.r-project.org). Further, a recent version of Pandoc (>= 1.12.3) is required. RStudio (https://rstudio.com) automatically includes this so you do not need to download Pandoc if you plan to use rmarkdown from the RStudio IDE, otherwise you’ll need to install Pandoc for your platform (https://pandoc.org/installing.html). Finally, if you want to generate a PDF report you will need to have LaTeX installed (e.g. MiKTeX, MacTeX or TinyTeX). To get the latest released version (master branch) of the R package from GitHub: ```{r, eval=F,R.options,} if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("Rgraphviz", dependencies = TRUE) devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` If you are asked whether you want to update installed packages and you type "a" for all, you might need administrator permissions to update R core packages. You can also try to type "n" for updating no packages. If you are asked "Do you want to install from sources the packages which need compilation? (Yes/no/cancel)", you can safely type "no". If you get *warning* messages (in contrast to *error* messages), these might not be problematic and you can try to proceed. If you encounter errors during the setup, looking into the "Troubleshooting" section might be worth it. # Terms of use Licenced under GPL-3. If you use this software for a publication, cite Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. (2019). Methods and open-source toolkit for analyzing and visualizing challenge results. *arXiv preprint arXiv:1910.05121* # Usage -Each of the following steps have to be run to generate the report: (1) Load package, (2) load data, (3) perform ranking, (4) perform bootstrapping and (5) generation of the report +Each of the following steps has to be run to generate the report: (1) Load package, (2) load data, (3) perform ranking, (4) perform bootstrapping and (5) generation of the report Here, we provide a step-by-step guide that leads you to your final report. ## 1. Load package Load package ```{r, eval=F} library(challengeR) ``` ## 2. Load data ### Data requirements Data requires the following *columns*: * *task identifier* in case of multi-task challenges (string or numeric) * *test case identifier* (string or numeric) * *algorithm identifier* (string or numeric) * *metric value* (numeric) In case of missing metric values, a missing observation has to be provided (either as blank field or "NA"). For example, in a challenge with 2 tasks, 2 test cases and 2 algorithms, where in task "T2", test case "case2", algorithm "A2" didn't give a prediction (and thus NA or a blank field for missing value is inserted), the data set might look like this: ```{r, eval=T, echo=F,results='asis'} set.seed(1) a=cbind(expand.grid(Task=paste0("T",1:2),TestCase=paste0("case",1:2),Algorithm=paste0("A",1:2)),MetricValue=round(c(runif(7,0,1),NA),3)) print(knitr::kable(a[order(a$Task,a$TestCase,a$Algorithm),],row.names=F)) ``` ### 2.1 Load data from file If you have assessment data at hand stored in a csv file (if you want to use simulated data skip the following code line) use ```{r, eval=F, echo=T} data_matrix=read.csv(file.choose()) # type ?read.csv for help ``` This allows to choose a file interactively, otherwise replace *file.choose()* by the file path (in style "/path/to/dataset.csv") in quotation marks. ### 2.2 Simulate data In the following, simulated data is generated *instead* for illustration purposes (skip the following code chunk if you have already loaded data). The data is also stored as "inst/extdata/data_matrix.csv" in the repository. ```{r, eval=F, echo=T} if (!requireNamespace("permute", quietly = TRUE)) install.packages("permute") n <- 50 set.seed(4) strip <- runif(n,.9,1) c_ideal <- cbind(task="c_ideal", rbind( data.frame(alg_name="A1",value=runif(n,.9,1),case=1:n), data.frame(alg_name="A2",value=runif(n,.8,.89),case=1:n), data.frame(alg_name="A3",value=runif(n,.7,.79),case=1:n), data.frame(alg_name="A4",value=runif(n,.6,.69),case=1:n), data.frame(alg_name="A5",value=runif(n,.5,.59),case=1:n) )) set.seed(1) c_random <- data.frame(task="c_random", alg_name=factor(paste0("A",rep(1:5,each=n))), value=plogis(rnorm(5*n,1.5,1)),case=rep(1:n,times=5) ) strip2 <- seq(.8,1,length.out=5) a <- permute::allPerms(1:5) c_worstcase <- data.frame(task="c_worstcase", alg_name=c(t(a)), value=rep(strip2,nrow(a)), case=rep(1:nrow(a),each=5) ) c_worstcase <- rbind(c_worstcase, data.frame(task="c_worstcase",alg_name=1:5,value=strip2,case=max(c_worstcase$case)+1) ) c_worstcase$alg_name <- factor(c_worstcase$alg_name,labels=paste0("A",1:5)) data_matrix <- rbind(c_ideal, c_random, c_worstcase) ``` ## 3. Perform ranking ### 3.1 Define challenge object Code differs slightly for single- and multi-task challenges. In case of a single-task challenge use ```{r, eval=F, echo=T} # Use only task "c_random" in object data_matrix dataSubset <- subset(data_matrix, task=="c_random") challenge <- as.challenge(dataSubset, # Specify which column contains the algorithms, # which column contains a test case identifier # and which contains the metric value: algorithm = "alg_name", case = "case", value = "value", # Specify if small metric values are better smallBetter = FALSE) ``` *Instead*, for a multi-task challenge use ```{r, eval=F, echo=T} # Same as above but with 'by="task"' where variable "task" contains the task identifier challenge=as.challenge(data_matrix, by = "task", algorithm = "alg_name", case = "case", value = "value", smallBetter = FALSE) ``` ### 3.2 Configure ranking Different ranking methods are available, choose one of them: - for "aggregate-then-rank" use (here: take mean for aggregation) ```{r, eval=F, echo=T} ranking <- challenge%>%aggregateThenRank(FUN = mean, # aggregation function, # e.g. mean, median, min, max, # or e.g. function(x) quantile(x, probs=0.05) na.treat = 0, # either "na.rm" to remove missing data, # set missings to numeric value (e.g. 0) # or specify a function, # e.g. function(x) min(x) ties.method = "min" # a character string specifying # how ties are treated, see ?base::rank ) ``` - *alternatively*, for "rank-then-aggregate" with arguments as above (here: take mean for aggregation) ```{r, eval=F, echo=T} ranking <- challenge%>%rankThenAggregate(FUN = mean, ties.method = "min" ) ``` - *alternatively*, for test-then-rank based on Wilcoxon signed rank test ```{r, eval=F, echo=T} ranking <- challenge%>%testThenRank(alpha = 0.05, # significance level p.adjust.method = "none", # method for adjustment for # multiple testing, see ?p.adjust na.treat = 0, # either "na.rm" to remove missing data, # set missings to numeric value (e.g. 0) # or specify a function, e.g. function(x) min(x) ties.method = "min" # a character string specifying # how ties are treated, see ?base::rank ) ``` ## 4. Perform bootstrapping Perform bootstrapping with 1000 bootstrap samples using one CPU ```{r, eval=F, echo=T} set.seed(1) ranking_bootstrapped <- ranking%>%bootstrap(nboot = 1000) ``` If you want to use multiple CPUs (here: 8 CPUs), use ```{r, eval=F, echo=T} library(doParallel) registerDoParallel(cores = 8) set.seed(1) ranking_bootstrapped <- ranking%>%bootstrap(nboot = 1000, parallel = TRUE, progress = "none") stopImplicitCluster() ``` ## 5. Generate the report Generate report in PDF, HTML or DOCX format. Code differs slightly for single- and multi-task challenges. ### 5.1 For single-task challenges ```{r, eval=F, echo=T} ranking_bootstrapped %>% report(title = "singleTaskChallengeExample", # used for the title of the report file = "filename", format = "PDF", # format can be "PDF", "HTML" or "Word" latex_engine = "pdflatex", #LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex" clean = TRUE #optional. Using TRUE will clean intermediate files that are created during rendering. ) ``` Argument *file* allows for specifying the output file path as well, otherwise the working directory is used. If file is specified but does not have a file extension, an extension will be automatically added according to the output format given in *format*. Using argument *clean=FALSE* allows to retain intermediate files, such as separate files for each figure. If argument "file" is omitted, the report is created in a temporary folder with file name "report". ### 5.2 For multi-task challenges Same as for single-task challenges, but additionally consensus ranking (rank aggregation across tasks) has to be given. Compute ranking consensus across tasks (here: consensus ranking according to mean ranks across tasks) ```{r, eval=F, echo=T} # See ?relation_consensus for different methods to derive consensus ranking meanRanks <- ranking%>%consensus(method = "euclidean") meanRanks # note that there may be ties (i.e. some algorithms have identical mean rank) ``` Generate report as above, but with additional specification of consensus ranking ```{r, eval=F, echo=T} ranking_bootstrapped %>% report(consensus = meanRanks, title = "multiTaskChallengeExample", file = "filename", format = "PDF", # format can be "PDF", "HTML" or "Word" latex_engine = "pdflatex"#LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex" ) ``` # Troubleshooting In this section we provide an overview of issues that the users reported and how they were solved. ### RStudio specific #### - Warnings while installing the GitHub repository ##### Error: While trying to install the current version of the repository: ```{r, eval=F, echo=T} devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` The following warning showed up in the output: ```{r, eval=F, echo=T} WARNING: Rtools is required to build R packages, but is not currently installed. ``` Therefore, Rtools was installed via a separate executable: https://cran.r-project.org/bin/windows/Rtools/ and the warning disappeared. ##### Solution: Actually there is no need of installing Rtools, it is not really used in the toolkit. Insted, choose not to install it when it is asked. See comment in the installation section: “If you are asked whether you want to update installed packages and you type “a” for all, you might need administrator rights to update R core packages. You can also try to type “n” for updating no packages. If you are asked “Do you want to install from sources the packages which need compilation? (Yes/no/cancel)”, you can safely type “no”.” #### - Unable to install the current version of the tool from GitHub ##### Error: While trying the current version of the tool from GitHub, it was unable to install. The error message was: ```{r, eval=F, echo=T} byte-compile and prepare package for lazy loading Error: (converted from warning) package 'ggplot2' was built under R version 3.6.3 Execution halted ERROR: lazy loading failed for package 'challengeR' * removing 'C:/Users/.../Documents/R/win-library/3.6/challengeR' * restoring previous 'C:/Users/.../Documents/R/win-library/3.6/challengeR' Error: Failed to install 'challengeR' from GitHub: (converted from warning) installation of package 'C:/Users/.../AppData/Local/Temp/Rtmp615qmV/file4fd419555eb4/challengeR_0.3.1.tar.gz' had non-zero exit status ``` The problem was that some of the packages that were built under R3.6.1 had been updated, but the current installed version was still R3.6.1. ##### Solution: The solution was to update R3.6.1 to R3.6.3. Another way would have been to reset the single packages to the versions built under R3.6.1 #### - Unable to install the toolkit from GitHub ##### Error: While trying the current version of the tool from GitHub, it was unable to install. ```{r, eval=F, echo=T} devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` The error message was: ```{r, eval=F, echo=T} Error: .onLoad failed in loadNamespace() for 'pkgload', details: call: loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) error: there is no package called ‘backports’ ``` The problem was that the packages 'backports' had not been installed. ##### Solution: The solution was to install 'backports' manually. ```{r, eval=F, echo=T} install.packages("backports") ``` #### - Unable to install R ##### Error: While trying to install the package in the R, after running the following commands: ```{r, eval=F, echo=T} if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("Rgraphviz", dependencies = TRUE) devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` The error message was: ```{r, eval=F, echo=T} ERROR: 1: In file(con, "r") : URL 'https://bioconductor.org/config.yaml': status was 'SSL connect error' 2: packages ‘BiocVersion’, ‘Rgraphviz’ are not available (for R version 3.6.1) ``` ##### Solution: The solution was to restart RStudio. #### - Incorrect column order ##### Error: When naming the columns "task" and "case", R was confused because the arguments in the challenge object are also called like this and it produced the following error: ```{r, eval=F, echo=T} Error in table(object[[task]][[algorithm]], object[[task]][[case]]) : all arguments must have the same length ``` ##### Solution: The solution was to rename the columns. #### - Wrong versions of packages ##### Error: While running this command : ```{r, eval=F, echo=T} devtools::install_github("wiesenfa/challengeR", dependencies = TRUE) ``` I had the following errors : - Error : the package 'purrr' has been compiled with version of R 3.6.3 - Error : the package 'ggplot2' has been compiled with version of R 3.6.3 - Error in loadNamespace(j <- i[[L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) namespace 'glue' 1.3.1 is already loaded, but >= 1.3.2 is required ##### Solution: To solve the issue I changed the versions of the packages. I had the following versions : - purrr 0.3.4 - ggplot2 3.3.2 - glue 1.3.1 I moved to the following ones : - purrr 0.3.3 - ggplot2 3.3.0 - glue 1.4.2 ### Related to MiKTeX #### - Missing packages ##### Error: While generating the PDF with MiKTeX (2.9), the following error showed up: ```{r, eval=F, echo=T} fatal pdflatex - gui framework cannot be initialized ``` There is an issue with installing missing packages in LaTeX. ##### Solution: Open your MiKTeX Console --> Settings, select "Always install missing packages on-the-fly". Then generate the report. Once the report is generated, you can reset the settings to your preferred ones. #### - Unable to generate report ##### Error: While generating the PDF with MiKTeX (2.9): ```{r, eval=F, echo=T} ranking_bootstrapped %>% report(title = "singleTaskChallengeExample", # used for the title of the report file = "filename", format = "PDF", # format can be "PDF", "HTML" or "Word" latex_engine = "pdflatex", #LaTeX engine for producing PDF output. Options are "pdflatex", "lualatex", and "xelatex" clean = TRUE #optional. Using TRUE will clean intermediate files that are created during rendering. ) ``` The following error showed up: ```{r, eval=F, echo=T} output file: filename.knit.md "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS filename.utf8.md --to latex --from markdown+autolink_bare_uris+tex_math_single_backslash --output filename.tex --self-contained --number-sections --highlight-style tango --pdf-engine pdflatex --variable graphics --lua-filter "C:/Users/adm/Documents/R/win-library/3.6/rmarkdown/rmd/lua/pagebreak.lua" --lua-filter "C:/Users/adm/Documents/R/win-library/3.6/rmarkdown/rmd/lua/latex-div.lua" --variable "geometry:margin=1in" Error: LaTeX failed to compile filename.tex. See https://yihui.org/tinytex/r/#debugging for debugging tips. Warning message: In system2(..., stdout = if (use_file_stdout()) f1 else FALSE, stderr = f2) : '"pdflatex"' not found ``` ##### Solution: The solution was to restart RStudio. # Changes #### Version 0.3.3 - Force line break to avoid that authors exceed the page in generated PDF reports #### Version 0.3.2 - Correct names of authors #### Version 0.3.1 - Refactoring #### Version 0.3.0 - Major bug fix release #### Version 0.2.5 - Bug fixes #### Version 0.2.4 - Automatic insertion of missings #### Version 0.2.3 - Bug fixes - Reports for subsets (top list) of algorithms: Use e.g. `subset(ranking_bootstrapped, top=3) %>% report(...)` (or `subset(ranking, top=3) %>% report(...)` for report without bootstrap results) to only show the top 3 algorithms according to the chosen ranking methods, where `ranking_bootstrapped` and `ranking` objects as defined in the example. Line plot for ranking robustness can be used to check whether algorithms performing well in other ranking methods are excluded. Bootstrapping still takes entire uncertainty into account. Podium plot and ranking heatmap neglect excluded algorithms. Only available for single-task challenges (for multi-task challenges not sensible because each task would contain a different set of algorithms). - Reports for subsets of tasks: Use e.g. `subset(ranking_bootstrapped, tasks=c("task1", "task2","task3")) %>% report(...)` to restrict report to tasks "task1", "task2","task3. You may want to recompute the consensus ranking before using `meanRanks=subset(ranking, tasks=c("task1", "task2", "task3"))%>%consensus(method = "euclidean")` #### Version 0.2.1 - Introduction in reports now mentions e.g. ranking method, number of test cases,... - Function `subset()` allows selection of tasks after bootstrapping, e.g. `subset(ranking_bootstrapped,1:3)` - `report()` functions gain argument `colors` (default: `default_colors`). Change e.g. to `colors=viridisLite::inferno` which "is designed in such a way that it will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. It is also designed to be perceived by readers with the most common form of color blindness." See package `viridis` for further similar functions. #### Version 0.2.0 - Improved layout in case of many algorithms and tasks (while probably still not perfect) - Consistent coloring of algorithms across figures - `report()` function can be applied to ranked object before bootstrapping (and thus excluding figures based on bootstrapping), i.e. in the example `ranking %>% report(...)` - bug fixes # Team The developer team includes members from both division of Computer Assisted Medical Interventions (CAMI) and Biostatistics at the German Cancer Research Center (DKFZ): - Manuel Wiesenfarth - Annette Kopp-Schneider - Annika Reinke - Matthias Eisenmann - Laura Aguilera Saiz - Elise Récéjac - Lena Maier-Hein # Reference Wiesenfarth, M., Reinke, A., Landman, B.A., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. (2019). Methods and open-source toolkit for analyzing and visualizing challenge results. *arXiv preprint arXiv:1910.05121*