mlr_tuners_hyperband {mlr3hyperband}R Documentation

Tuner Using the Hyperband Algorithm

Description

Optimizer using the Hyperband (HB) algorithm. HB runs the Successive Halving Algorithm (SHA) with different numbers of stating configurations. The algorithm is initialized with the same parameters as Successive Halving but without n. Each run of Successive Halving is called a bracket and starts with a different budget r_0. A smaller starting budget means that more configurations can be tried out. The most explorative bracket allocated the minimum budget r_min. The next bracket increases the starting budget by a factor of eta. In each bracket, the starting budget increases further until the last bracket s = 0 essentially performs a random search with the full budget r_max. The number of brackets s_max + 1 is calculated with s_max = log(r_min / r_max)(eta). Under the condition that r_0 increases by eta with each bracket, r_min sometimes has to be adjusted slightly in order not to use more than r_max resources in the last bracket. The number of configurations in the base stages is calculated so that each bracket uses approximately the same amount of budget. The following table shows a full run of HB with eta = 2, r_min = 1 and r_max = 8.

s 3 2 1 0
i n_i r_i n_i r_i n_i r_i n_i r_i
0 8 1 6 2 4 4 8 4
1 4 2 3 4 2 8
2 2 4 1 8
3 1 8

s is the bracket number, i is the stage number, n_i is the number of configurations and r_i is the budget allocated to a single configuration.

The budget hyperparameter must be tagged with "budget" in the search space. The minimum budget (r_min) which is allocated in the base stage of the most explorative bracket, is set by the lower bound of the budget parameter. The upper bound defines the maximum budget (r_max) which is allocated to the candidates in the last stages.

Dictionary

This mlr3tuning::Tuner can be instantiated via the dictionary mlr3tuning::mlr_tuners or with the associated sugar function mlr3tuning::tnr(): 

TunerBatchHyperband$new()
mlr_tuners$get("hyperband")
tnr("hyperband")

Subsample Budget

If the learner lacks a natural budget parameter, mlr3pipelines::PipeOpSubsample can be applied to use the subsampling rate as budget parameter. The resulting mlr3pipelines::GraphLearner is fitted on small proportions of the mlr3::Task in the first stage, and on the complete task in last stage.

Custom Sampler

Hyperband supports custom paradox::Sampler object for initial configurations in each bracket. A custom sampler may look like this (the full example is given in the examples section): 

# - beta distribution with alpha = 2 and beta = 5
# - categorical distribution with custom probabilities
sampler = SamplerJointIndep$new(list(
  Sampler1DRfun$new(params[[2]], function(n) rbeta(n, 2, 5)),
  Sampler1DCateg$new(params[[3]], prob = c(0.2, 0.3, 0.5))
))

Progress Bars

⁠$optimize()⁠ supports progress bars via the package progressr combined with a bbotk::Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

Parallelization

This hyperband implementation evaluates hyperparameter configurations of equal budget across brackets in one batch. For example, all configurations in stage 1 of bracket 3 and stage 0 of bracket 2 in one batch. To select a parallel backend, use the plan() function of the future package.

Logging

Hyperband uses a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Resources

The gallery features a collection of case studies and demos about optimization.

Parameters

eta

numeric(1)
With every stage, the budget is increased by a factor of eta and only the best 1 / eta points are promoted to the next stage. Non-integer values are supported, but eta is not allowed to be less or equal to 1.

sampler

paradox::Sampler
Object defining how the samples of the parameter space should be drawn in the base stage of each bracket. The default is uniform sampling.

repetitions

integer(1)
If 1 (default), optimization is stopped once all brackets are evaluated. Otherwise, optimization is stopped after repetitions runs of HB. The bbotk::Terminator might stop the optimization before all repetitions are executed.

Archive

The bbotk::Archive holds the following additional columns that are specific to HB:

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerBatchHyperband

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
TunerBatchHyperband$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
TunerBatchHyperband$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Source

Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A (2018). “Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization.” Journal of Machine Learning Research, 18(185), 1-52. https://jmlr.org/papers/v18/16-558.html.

Examples

if(requireNamespace("xgboost")) {
  library(mlr3learners)

  # define hyperparameter and budget parameter
  search_space = ps(
    nrounds = p_int(lower = 1, upper = 16, tags = "budget"),
    eta = p_dbl(lower = 0, upper = 1),
    booster = p_fct(levels = c("gbtree", "gblinear", "dart"))
  )

  
  # hyperparameter tuning on the pima indians diabetes data set
  instance = tune(
    tnr("hyperband"),
    task = tsk("pima"),
    learner = lrn("classif.xgboost", eval_metric = "logloss"),
    resampling = rsmp("cv", folds = 3),
    measures = msr("classif.ce"),
    search_space = search_space,
    term_evals = 100
  )

  # best performing hyperparameter configuration
  instance$result
  
}
[Package mlr3hyperband version 0.6.0 Index]