Plot for TuningInstanceSingleCrit

Generates plots for mlr3tuning::TuningInstanceSingleCrit.

Usage

# S3 method for TuningInstanceSingleCrit
autoplot(
  object,
  type = "marginal",
  cols_x = NULL,
  trafo = FALSE,
  learner = mlr3::lrn("regr.ranger"),
  grid_resolution = 100,
  ...
)

Arguments

object

(mlr3tuning::TuningInstanceSingleCrit.

type

(character(1)): Type of the plot. Available choices:

• "marginal": scatter plots of hyperparameter versus performance. The color of the points shows the batch number.

• "performance": scatter plots of batch number versus performance.

• "parameter": scatter plots of batch number versus hyperparameter. The color of the points shows the performance.

• "parallel" parallel coordinates plot. Parameter values are rescaled by (x - mean(x)) / sd(x).

• "points" - scatter plot of two hyperparameters versus performance. The color of the points shows the performance.

• "surface": surface plot of 2 hyperparameters versus performance. The performance values are interpolated with the supplied mlr3::Learner.

• "pairs": plots all hyperparameters and performance values against each other.

cols_x

(character())
Column names of hyperparameters. By default, all untransformed hyperparameters are plotted. Transformed hyperparameters are prefixed with x_domain_.

trafo

(logical(1))
Determines if untransformed (FALSE) or transformed (TRUE) hyperparametery are plotted.

learner

(mlr3::Learner)
Regression learner used to interpolate the data of the surface plot.

grid_resolution

(numeric())
Resolution of the surface plot.

...

(any): Additional arguments, possibly passed down to the underlying plot functions.

Value

ggplot2::ggplot() object.

Theme

The theme_mlr3() and viridis color maps are applied by default to all autoplot() methods. To change this behavior set options(mlr3.theme = FALSE).

Examples

if (requireNamespace("mlr3tuning") && requireNamespace("patchwork")) {
  library(mlr3tuning)

  learner = lrn("classif.rpart")
  learner$param_set$values$cp = to_tune(0.001, 0.1)
  learner$param_set$values$minsplit = to_tune(1, 10)

  instance = TuningInstanceSingleCrit$new(
    task = tsk("iris"),
    learner = learner,
    resampling = rsmp("holdout"),
    measure = msr("classif.ce"),
    terminator = trm("evals", n_evals = 10))

  tuner = tnr("random_search")

  tuner$optimize(instance)

  # plot performance versus batch number
  autoplot(instance, type = "performance")

  # plot cp values versus performance
  autoplot(instance, type = "marginal", cols_x = "cp")

  # plot transformed parameter values versus batch number
  autoplot(instance, type = "parameter", trafo = TRUE)

  # plot parallel coordinates plot
  autoplot(instance, type = "parallel")

  # plot pairs
  autoplot(instance, type = "pairs")
}
#> Loading required namespace: mlr3tuning
#> Warning: the standard deviation is zero
#> Warning: the standard deviation is zero
#> Warning: the standard deviation is zero