Generation and plotting functions
mlr's visualization capabilities rely on generation functions which generate data for plots, and plotting functions which plot this output using either ggplot2 or ggvis (the latter being currently experimental).
This separation allows users to easily make custom visualizations by taking advantage of the generation functions. The only data transformation that is handled inside plotting functions is reshaping. The reshaped data is also accessible by calling the plotting functions and then extracting the data from the ggplot object.
The functions are named accordingly.
- Names of generation functions start with
generateand are followed by a title-case description of their
FunctionPurpose, followed by
generateFunctionPurposeData. These functions output objects of class
- Plotting functions are prefixed by
plotfollowed by their purpose, i.e.,
- ggvis plotting functions have an additional suffix
In the example below we create a plot of classifier performance as function of the decision
threshold for the binary classification problem sonar.task.
The generation function generateThreshVsPerfData creates an object of class
ThreshVsPerfData which contains the data for the plot in slot
lrn = makeLearner("classif.lda", predict.type = "prob") n = getTaskSize(sonar.task) mod = train(lrn, task = sonar.task, subset = seq(1, n, by = 2)) pred = predict(mod, task = sonar.task, subset = seq(2, n, by = 2)) d = generateThreshVsPerfData(pred, measures = list(fpr, fnr, mmce)) class(d) #>  "ThreshVsPerfData" head(d$data) #> fpr fnr mmce threshold #> 1 1.0000000 0.0000000 0.4615385 0.00000000 #> 2 0.3541667 0.1964286 0.2692308 0.01010101 #> 3 0.3333333 0.2321429 0.2788462 0.02020202 #> 4 0.3333333 0.2321429 0.2788462 0.03030303 #> 5 0.3333333 0.2321429 0.2788462 0.04040404 #> 6 0.3125000 0.2321429 0.2692308 0.05050505
Note that by default the Measure
names are used to annotate the panels.
fpr$name #>  "False positive rate" fpr$id #>  "fpr"
This does not only apply to plotThreshVsPerf, but to other plot functions that
show performance measures as well, for example plotLearningCurve.
You can use the
ids instead of the names by setting
pretty.names = FALSE.
As mentioned above it is easily possible to customize the built-in plots or making your own visualizations from scratch based on the generated data.
What will probably come up most often is changing labels and annotations.
Generally, this can be done by manipulating the ggplot object,
in this example the object returned by plotThreshVsPerf, using the usual ggplot2
functions like ylab or labeller.
Moreover, you can change the underlying data, either
d$data (resulting from
generateThreshVsPerfData) or the possibly reshaped data contained in the
ggplot object (resulting from plotThreshVsPerf), most often by
renaming columns or factor levels.
Below are two examples of how to alter the axis and panel labels of the above plot.
Imagine you want to change the order of the panels and also are not satisfied with the panel names, for example you find that "Mean misclassification error" is too long and you prefer "Error rate" instead. Moreover, you want the error rate to be displayed first.
plt = plotThreshVsPerf(d, pretty.names = FALSE) ## Reshaped version of the underlying data d head(plt$data) #> threshold measure performance #> 1 0.00000000 fpr 1.0000000 #> 2 0.01010101 fpr 0.3541667 #> 3 0.02020202 fpr 0.3333333 #> 4 0.03030303 fpr 0.3333333 #> 5 0.04040404 fpr 0.3333333 #> 6 0.05050505 fpr 0.3125000 levels(plt$data$measure) #>  "fpr" "fnr" "mmce" ## Rename and reorder factor levels plt$data$measure = factor(plt$data$measure, levels = c("mmce", "fpr", "fnr"), labels = c("Error rate", "False positive rate", "False negative rate")) plt = plt + xlab("Cutoff") + ylab("Performance") plt
Using the labeller function requires calling facet_wrap (or facet_grid), which can be useful if you want to change how the panels are positioned (number of rows and columns) or influence the axis limits.
plt = plotThreshVsPerf(d, pretty.names = FALSE) measure_names = c( fpr = "False positive rate", fnr = "False negative rate", mmce = "Error rate" ) ## Manipulate the measure names via the labeller function and ## arrange the panels in two columns and choose common axis limits for all panels plt = plt + facet_wrap( ~ measure, labeller = labeller(measure = measure_names), ncol = 2) plt = plt + xlab("Decision threshold") + ylab("Performance") plt
ggplot(d$data, aes(threshold, fpr)) + geom_line()
The decoupling of generation and plotting functions is especially practical if you prefer traditional graphics or lattice. Here is a lattice plot which gives a result similar to that of plotThreshVsPerf.
lattice::xyplot(fpr + fnr + mmce ~ threshold, data = d$data, type = "l", ylab = "performance", outer = TRUE, scales = list(relation = "free"), strip = strip.custom(factor.levels = sapply(d$measures, function(x) x$name), par.strip.text = list(cex = 0.8)))
Let's conclude with a brief look on a second example.
Here we use plotPartialDependence but extract the data from the ggplot
pltand use it to create a traditional graphics::plot, additional to the ggplot2
sonar = getTaskData(sonar.task) pd = generatePartialDependenceData(mod, sonar, "V11") plt = plotPartialDependence(pd) head(plt$data) #> Class Probability Feature Value #> 1 M 0.2737158 V11 0.0289000 #> 2 M 0.3689970 V11 0.1072667 #> 3 M 0.4765742 V11 0.1856333 #> 4 M 0.5741233 V11 0.2640000 #> 5 M 0.6557857 V11 0.3423667 #> 6 M 0.7387962 V11 0.4207333 plt
plot(Probability ~ Value, data = plt$data, type = "b", xlab = plt$data$Feature)
Available generation and plotting functions
Below the currently available generation and plotting functions are listed and tutorial pages that provide in depth descriptions of the listed functions are referenced.
Note that some plots, e.g., plotTuneMultiCritResult are not mentioned here since they lack a generation function. Both plotThreshVsPerf and plotROCCurves operate on the result of generateThreshVsPerfData. Functions plotPartialDependence and plotPartialDependenceGGVIS can be applied to the results of both generatePartialDependenceData and generateFunctionalANOVAData.