Type:	Package
Title:	Algorithm Portfolio Selection with Machine Learning
Version:	1.0.0
Description:	A wrapper for machine learning (ML) methods to select among a portfolio of algorithms based on the value of a key performance indicator (KPI). A number of features is used to adjust a model to predict the value of the KPI for each algorithm, then, for a new value of the features the KPI is estimated and the algorithm with the best one is chosen. To learn it can use the regression methods in 'caret' package or a custom function defined by the user. Several graphics available to analyze the results obtained. This library has been used in Ghaddar et al. (2023) <doi:10.1287/ijoc.2022.0090>).
License:	GPL-3
Language:	en-US
Encoding:	UTF-8
LazyData:	true
Imports:	caret, ggplot2, dplyr, purrr, tibble, tidyr, reshape2, Polychrome, scales, rlang
Suggests:	snow
Depends:	R (≥ 3.5.0)
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-02-18 13:01:46 UTC; brais
Author:	Brais González-Rodríguez [aut, cre], Ignacio Gómez-Casares [aut], Beatriz Pateiro-López [aut], Julio González-Díaz [aut], María Caseiro-Arias [ctb], Antonio Fariña-Elorza [ctb], Manuel Timiraos-López [ctb]
Maintainer:	Brais González-Rodríguez <brais.gonzalez.rodriguez@uvigo.gal>
Repository:	CRAN
Date/Publication:	2025-02-19 14:40:05 UTC

Predicting the KPI value for the algorithms

Description

For each algorithm, the output (KPI) is predicted using the models trained with AStrain().

Usage

ASpredict(training_object, ...)

Arguments

Value

A data frame, result of the respective ASpredict method.

Predicting the KPI value for the algorithms

Description

For each algorithm, the output (KPI) is predicted using the models traing with AStrain().

Usage

## S3 method for class 'as_train'
ASpredict(training_object, newdata = NULL, f = NULL, ...)

Arguments

Details

The ASpredict() uses the prediction function from caret to compute (for each of the models trained) the predictions for the new data provided by the user. If the user used a custom function in AStrain() (given by parameter f), caret's default prediction function might not work, and the user might have to provide a custom function for ASpredict() as well. Additionally, this custom prediction function allows to pass additional arguments, something that caret's default prediction function does not. The object return by the train function used in AStrain() (caret's or a custom one) is the one passed to the custom f function defined by the user. This f function must return a vector with the predictions.

Value

A data frame with the predictions for each instance (rows), corresponding to each algorithm (columns). In case f is specified, some actions might be needed to get the predictions from the returned value.

Examples

data(branchingsmall)
data_object <- partition_and_normalize(branchingsmall$x, branchingsmall$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE)
training <- AStrain(data_object, method = "glm")
predictions <- ASpredict(training, newdata = data_object$x.test)
qrf_q_predict <- function(modelFit, newdata, what = 0.5, submodels = NULL) {
  out <- predict(modelFit, newdata, what = what)
  if (is.matrix(out))
    out <- out[, 1]
  out
}
custom_predictions <- ASpredict(training, newdata = data_object$x.test, f = "qrf_q_predict",
what = 0.25)

Training models for posterior selection of algorithms

Description

For each algorithm (column) in the data, a model is trained to later predict the output (KPI) for that algorithm (using function ASpredict()).

Usage

AStrain(data_object, ...)

Arguments

Value

A list, result of the respective AStrain method.

Training models for posterior selection of algorithms

Description

For each algorithm (column) in the data, a model is trained to later predict the output (KPI) for that algorithm (using function ASpredict()).

Usage

## S3 method for class 'as_data'
AStrain(data_object, method = NULL, parallel = FALSE, f = NULL, ...)

Arguments

Value

A list is returned of class as_train containing the trained models, one for each of the algorithms.

Examples

data(branchingsmall)
data_object <- partition_and_normalize(branchingsmall$x, branchingsmall$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE)
training <- AStrain(data_object, method = "glm")
custom_function <- function(x, y) {
  glm.fit(x, y)
}
custom_training <- AStrain(data_object, f = "custom_function")

KPI summary table

Description

Generates a summary table with the values of the KPI.

Function that generates a summary table of the KPI values. Optimal is the value of the KPI when choosing the best option for each instance. It's the best that we could do with respect to that KPI. Best is the value of the KPI for the best option overall according to the KPI. ML is the value of the KPI choosing for each instance the option selected by the learning.

Usage

KPI_summary_table(data_object, ...)

## S3 method for class 'as_data'
KPI_summary_table(
  data_object,
  predictions = NULL,
  test = TRUE,
  normalized = FALSE,
  ...
)

Arguments

Value

A table, result of the respective KPI_summary_table method.

A table with the statistics of the pace.

Examples

data(branchingsmall)
data_object <- partition_and_normalize(branchingsmall$x, branchingsmall$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE)
training <- AStrain(data_object, method = "glm")
predictions <- ASpredict(training, newdata = data_object$x.test)
KPI_summary_table(data_object, predictions = predictions)

KPI table

Description

Generates a table with the values of the KPI.

Function that generates a table with the values of the KPI.

Usage

KPI_table(data_object, ...)

## S3 method for class 'as_data'
KPI_table(data_object, predictions = NULL, test = TRUE, ...)

Arguments

Value

A table, result of the respective KPI_table method.

A table with the statistics of the pace.

Examples

data(branchingsmall)
data_object <- partition_and_normalize(branchingsmall$x, branchingsmall$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE)
training <- AStrain(data_object, method = "glm")
predictions <- ASpredict(training, newdata = data_object$x.test)
KPI_table(data_object, predictions = predictions)

Boxplots

Description

Generates boxplots for an object.

Usage

boxplots(data_object, ...)

Arguments

Value

A ggplot object, result of the respective boxplots method.

Boxplots

Description

Represents a boxplot for each of the algorithms to compare their performance according to the response variable (KPI). When available, it also includes a box plot for the "ML" algorithm generated from the predictions.

Usage

## S3 method for class 'as_data'
boxplots(
  data_object,
  main = "Boxplot Comparison",
  labels = NULL,
  test = TRUE,
  predictions = NULL,
  by_families = FALSE,
  color_list = NULL,
  ml_color = NULL,
  ordered_option_names = NULL,
  xlab = "Strategy",
  ylab = "KPI",
  ...
)

Arguments

Value

A ggplot object representing the boxplots of instance-normalized KPI for each algorithm across instances.

Examples

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

Branching point selection in Polynomial Optimization

Description

Data from Ghaddar et al. (2023) used to select among several branching criteria for an RLT-based algorithm. Includes features for the instances and KPI values for the different branching criteria for executions lasting 1 hour.

Usage

branching

Format

A list with x (features) and y (KPIs) data.frames.

Source

Ghaddar, B., Gómez-Casares, I., González-Díaz, J., González-Rodríguez, B., Pateiro-López, B., & Rodríguez-Ballesteros, S. (2023). Learning for Spatial Branching: An Algorithm Selection Approach. INFORMS Journal on Computing.

Branching point selection in Polynomial Optimization

Description

Data from Ghaddar et al. (2023) used to select among several branching criteria for an RLT-based algorithm. Includes features for the instances and KPI values for the different branching criteria for executions lasting 10 minutes.

Usage

branchingsmall

Format

A list with x (features) and y (KPIs) data.frames.

Source

Ghaddar, B., Gómez-Casares, I., González-Díaz, J., González-Rodríguez, B., Pateiro-López, B., & Rodríguez-Ballesteros, S. (2023). Learning for Spatial Branching: An Algorithm Selection Approach. INFORMS Journal on Computing.

Figure comparison

Description

Generates figure comparison plot for an object.

Usage

figure_comparison(data_object, ...)

Arguments

Value

A ggplot object, result of the respective figure_comparison method.

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

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)

Machine learning process

Description

Function that proceses input data, trains the machine learning models, makes a prediction and plots the results.

Usage

ml(
  x,
  y,
  x.test = NULL,
  y.test = NULL,
  family_column = NULL,
  split_by_family = FALSE,
  predict = TRUE,
  test_size = 0.25,
  better_smaller = TRUE,
  method = "ranger",
  test = TRUE,
  color_list = NULL
)

Arguments

Value

A list with the data and plots generated, including:

• data_obj An as_data object with the processed data from partition_and_normalize() function.

• training An as_train object with the trainings from the AStrain() function.

• predictions A data frame with the predictions from the ASpredict() function, if the predict param is TRUE.

• table A table with the summary of the output data.

• boxplot, ranking_plot, figure_comparison, optml_figure_comparison and optmlall_figure_comparison with the corresponding plots.

Examples

data(branchingsmall)
machine_learning <- ml(branchingsmall$x, branchingsmall$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE, method = "glm")

Partition and Normalize

Description

Function that processes the input data splitting it into training and test sets and normalizes the outputs depending on the best instance performance. The user can bypass the partition into training and test set by passing the parameters x.test and y.test.

Usage

partition_and_normalize(
  x,
  y,
  x.test = NULL,
  y.test = NULL,
  family_column = NULL,
  split_by_family = FALSE,
  test_size = 0.3,
  better_smaller = TRUE
)

Arguments

Value

A list is returned of class as_data containing:

• x.train A data frame with the training features.

• y.train A data frame with the training output.

• x.test A data frame with the test features.

• y.test A data frame with the test output.

• y.train.original A vector with the original training output (without normalizing).

• y.test.original A vector with the original test output (without normalizing).

• families.train A data frame with the families of the training data.

• families.test A data frame with the families of the test data.

Examples

data(branching)
data_obj <- partition_and_normalize(branching$x, branching$y, test_size = 0.3,
family_column = 1, split_by_family = TRUE)

Plot

Description

For an object of class as_data, function that makes several plots, including the following: a boxplot, a ranking plot and comparisons between the different options.

Usage

## S3 method for class 'as_data'
plot(
  x,
  labels = NULL,
  test = TRUE,
  predictions = NULL,
  by_families = FALSE,
  stacked = TRUE,
  legend = TRUE,
  color_list = NULL,
  ml_color = NULL,
  path = NULL,
  ...
)

Arguments

Value

A list with boxplot, ranking, fig_comp, optml_fig_comp and optmlall_fig_comp plots.

Examples

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

Ranking

Description

Generates ranking plot for an object.

Usage

ranking(data_object, ...)

Arguments

Value

A ggplot object, result of the respective ranking method.

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

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)