Plotting critical differences in R with imported data

Question

A critical difference (CD) plot for comparing classifiers over multiple data sets (Demšar2006) can be generated with the mlr package like this:

# THIS WORKS
library(mlr)
lrns = list(makeLearner("classif.knn"), makeLearner("classif.svm"))
tasks = list(iris.task, sonar.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc)
bmr = benchmark(lrns, tasks, rdesc, measures = meas)
cd = generateCritDifferencesData(bmr)
plotCritDifferences(cd)

This requires the evaluation results to reside in a rather complex BenchmarkResult object, although the data is basically a matrix (where M[i, j] holds the score of classifier i for data set j). I have previously generated such data in a Python workflow and imported in R into a data.frame (as there seems to be no Python package for such plots).

How can I generate a CD plot from this data?

I thought about creating a BenchmarkResult from the data.frame, but didn't know where to start:

# THIS DOES NOT WORK
library(mlr)
# Here I would import results from my experiments instead of using random data
# e.g. scores for 5 classifiers and 30 data sets, each
results = data.frame(replicate(5, runif(30, 0, 1)))
# This is the functionality I'm looking for
bmr = benchmarkResultFromDataFrame(results)
cd = generateCritDifferencesData(bmr)
plotCritDifferences(cd)

Answer 1

I finally managed to create the plot. It is necessary to set only a handful of the BenchmarkResult's attributes:

• leaners with id and short.name for each classifier
• measures
• results with aggr for each dataset/classifier combination

The code may then look like this (smaller example of 5 datasets):

library(mlr)
# Here I would import results from my experiments instead of using random data
# e.g. scores for 5 classifiers and 30 data sets, each
results <- data.frame(replicate(5, runif(30, 0, 1)))
clf <- c('clf1', 'clf2', 'clf3', 'clf4', 'clf5')
clf.short.name <- c('c1', 'c2', 'c3', 'c4', 'c5')
dataset <- c('dataset1', 'dataset2', 'dataset3', 'dataset4', 'dataset5')
score <- list(acc)

# Setting up the learners: id, short.name
bmr <- list()
for (i in 1:5){
    bmr$learners[[clf[i]]]$id <- clf[i]
    bmr$learners[[clf[i]]]$short.name <- clf.short.name[i]
}

# Setting up the measures
bmr$measures <- list(acc)

# Setting up the results
for (i in 1:5){
  bmr$results$`dataset1`[[clf[i]]]$aggr <- list('acc.test.mean' = results[1, i])
}
for (i in 1:5){
  bmr$results$`dataset2`[[clf[i]]]$aggr <- list('acc.test.mean' = results[2, i])
}
for (i in 1:5){
  bmr$results$`dataset3`[[clf[i]]]$aggr <- list('acc.test.mean' = results[3, i])
}
for (i in 1:5){
  bmr$results$`dataset4`[[clf[i]]]$aggr <- list('acc.test.mean' = results[4, i])
}
for (i in 1:5){
  bmr$results$`dataset5`[[clf[i]]]$aggr <- list('acc.test.mean' = results[5, i])
}

# Set BenchmarkResult class
class(bmr) <- "BenchmarkResult"

# Statistics and plot
cd = generateCritDifferencesData(bmr)
plotCritDifferences(cd)

Anyone who could teach me better R to avoid these for loops and code duplication would still be very welcome!