Testing Workbench for Precision-Recall Curves.

prcbench

The aim of the prcbench package is to provide a testing workbench for evaluating precision-recall curves under various conditions. It contains integrated interfaces for the following five tools. It also contains predefined test data sets.

Disclaimer: prcbench was originally develop to help our precrec library in order to provide fast and accurate calculations of precision-recall curves with extra functionality.

Accuracy evaluation of precision-recall curves

prcbench uses pre-defined test sets to help evaluate the accuracy of precision-recall curves.

1. create_toolset: creates objects of different tools for testing (5 different tools)
2. create_testset: selects pre-defined data sets (c1, c2, and c3)
3. run_evalcurve: evaluates the selected tools on the simulation data
4. autoplot: shows the results with ggplot2 and patchwork

## Load library
library(prcbench)

## Plot base points and the result of 5 tools on pre-defined test sets (c1, c2, and c3)
toolset <- create_toolset(c("precrec", "ROCR", "AUCCalculator", "PerfMeas", "PRROC"))
testset <- create_testset("curve", c("c1", "c2", "c3"))
scores1 <- run_evalcurve(testset, toolset)
autoplot(scores1, ncol = 3, nrow = 2)

Running-time evaluation of precision-recall curves

prcbench helps create simulation data to measure computational times of creating precision-recall curves.

1. create_toolset: creates objects of different tools for testing
2. create_testset: creates simulation data
3. run_benchmark: evaluates the selected tools on the simulation data

## Load library
library(prcbench)

## Run benchmark for auc5 (5 tools) on b10 (balanced 5 positives and 5 negatives)
toolset <- create_toolset(set_names = "auc5")
testset <- create_testset("bench", "b10")
res <- run_benchmark(testset, toolset)

print(res)

Documentation

• Introduction to prcbench – a package vignette that contains the descriptions of the functions with several useful examples. View the vignette with vignette("introduction", package = "prcbench") in R.

• Help pages – all the functions including the S3 generics have their own help pages with plenty of examples. View the main help page with help(package = "prcbench") in R.

Installation

CRAN

install.packages("prcbench")

Dependencies

AUCCalculator requires a Java runtime environment (>= 6) if AUCCalculator needs to be evaluated.

GitHub

You can install a development version of prcbench from our GitHub repository.

devtools::install_github("evalclass/prcbench")

1. Make sure you have a working development environment.

   • Windows: Install Rtools (available on the CRAN website).

   • Mac: Install Xcode from the Mac App Store.

   • Linux: Install a compiler and various development libraries (details vary across different flavors of Linux).

2. Install devtools from CRAN with install.packages("devtools").

3. Install prcbench from the GitHub repository with devtools::install_github("evalclass/prcbench").

Troubleshooting

microbenchmark

microbenchmark does not work on some OSs. prcbench uses system.time when microbenchmark is not available.

rJava

• Some OSs require en extra configuration step after rJava installation.

sudo R CMD javareconf

• JDKs

1. Oracle JDK
2. OpenJDK

• JDKs for macOS

1. AdoptOpenJDK
2. AdoptOpenJDK with homebrew

• JRI support on macOS Big Sur – see this Stack Overflow thread.

install.packages("rJava", configure.args = "--disable-jri")

Citation

Precrec: fast and accurate precision-recall and ROC curve calculations in R

Takaya Saito; Marc Rehmsmeier

Bioinformatics 2017; 33 (1): 145-147.

doi: 10.1093/bioinformatics/btw570

External links

• Classifier evaluation with imbalanced datasets – our web site that contains several pages with useful tips for performance evaluation on binary classifiers.

• The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets – our paper that summarized potential pitfalls of ROC plots with imbalanced datasets and advantages of using precision-recall plots instead.