ranking.as_data: Ranking Plot

In ASML: Algorithm Portfolio Selection with Machine Learning

Ranking Plot

Description

After ranking the algorithms for each instance, represents for each of the algorithms, a bar with the percentage of times it was in each of the ranking positions. The number inside is the mean value of the normalized response variable (KPI) for the problems for which the algorithm was in that ranking position. The option predictions allows to control if the "ML" algorithm is added to the plot.

Usage

## S3 method for class 'as_data'
ranking(
  data_object,
  main = "Ranking",
  labels = NULL,
  test = TRUE,
  predictions = NULL,
  by_families = FALSE,
  ordered_option_names = NULL,
  xlab = "",
  ylab = "",
  ...
)

Arguments

data_object	object of class as_data.
main	an overall title for the plot.
labels	character vector with the labels for each of the algorithms. If NULL, the y names of the data_object names will be used.
test	flag that indicates whether the function should use test data or training data.
predictions	a data frame with the predicted KPI for each algorithm (columns) and for each instance (rows). If NULL, the plot won't include a ML column.
by_families	boolean indicating whether the function should represent data by families or not. The family information must be included in the data_object parameter.
ordered_option_names	vector with the name of the columns of data_object y variable in the correct order.
xlab	a label for the x axis.
ylab	a label for the y axis.
...	other parameters.

Value

A ggplot object representing the ranking of algorithms based on the instance-normalized KPI.

Examples

data(branchingsmall)
data <- partition_and_normalize(branchingsmall$x, branchingsmall$y)
training <- AStrain(data, method = "glm")
predict_test <- ASpredict(training, newdata = data$x.test)
ranking(data, predictions = predict_test)

ASML documentation built on April 3, 2025, 8:47 p.m.