figure_comparison.as_data: Figure Comparison

In ASML: Algorithm Portfolio Selection with Machine Learning

Figure Comparison

Description

Represents a bar plot with the percentage of times each algorithm is selected by ML compared with the optimal selection (according to the response variable or KPI).

Usage

## S3 method for class 'as_data'
figure_comparison(
  data_object,
  ties = "different_data_points",
  main = "Option Comparison",
  labels = NULL,
  mllabel = NULL,
  test = TRUE,
  predictions,
  by_families = FALSE,
  stacked = TRUE,
  color_list = NULL,
  legend = TRUE,
  ordered_option_names = NULL,
  xlab = "Criteria",
  ylab = "Instances (%)",
  ...
)

Arguments

data_object	object of class as_data.
ties	How to deal with ties. Must be one of: "different_data_points": Tied algorithms in the optimal selection are all counted as different data points (increasing the total number of x values and therefore giving all of the tied algorithms the same weight). "ml_if_optimal": For tied algorithms, the one selected by ML is chosen if it corresponds to the optimal one. Otherwise, the same as in option different_data_points is done. "ml_selection": For tied algorithms, the one prefered by the ML is chosen.
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.
mllabel	character vector with the labels for the Optimal and ML bars. If NULL, default 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).
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.
stacked	boolean to choose between bar plot and stacked bar plot.
color_list	list with the colors for the plots. If NULL, or insufficient number of colors, the colors will be generated automatically.
legend	boolean to activate or deactivate the legend in the plot.
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 bar plot with the percentage of times each algorithm is selected by ML compared with the optimal selection (according to the response variable or 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)
figure_comparison(data, predictions = predict_test)

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