Optimizer Using the Hyperband Algorithm

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.

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.

Resources

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

Tune the hyperparameters of XGBoost with Hyperband.
Use data subsampling and Hyperband to optimize a support vector machine.

Dictionary

This bbotk::Optimizer can be instantiated via the dictionary bbotk::mlr_optimizers or with the associated sugar function bbotk::opt():

mlr_optimizers$get("hyperband")
opt("hyperband")

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.

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").

Logging

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

Super classes

bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchHyperband

Methods

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

OptimizerBatchHyperband$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

OptimizerBatchHyperband$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(bbotk)
library(data.table)

# set search space
search_space = domain = ps(
  x1 = p_dbl(-5, 10),
  x2 = p_dbl(0, 15),
  fidelity = p_dbl(1e-2, 1, tags = "budget")
)

# Branin function with fidelity, see `bbotk::branin()`
fun = function(xs) branin_wu(xs[["x1"]], xs[["x2"]], xs[["fidelity"]])

# create objective
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = ps(y = p_dbl(tags = "minimize"))
)

# initialize instance and optimizer
instance = OptimInstanceSingleCrit$new(
  objective = objective,
  search_space = search_space,
  terminator = trm("evals", n_evals = 50)
)
#> OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead.

optimizer = opt("hyperband")

# optimize branin function
optimizer$optimize(instance)
#>          x1       x2 fidelity  x_domain        y
#>       <num>    <num>    <num>    <list>    <num>
#> 1: 2.953187 2.235532 0.015625 <list[3]> 1.013389

# best scoring evaluation
instance$result
#>          x1       x2 fidelity  x_domain        y
#>       <num>    <num>    <num>    <list>    <num>
#> 1: 2.953187 2.235532 0.015625 <list[3]> 1.013389

# all evaluations
as.data.table(instance$archive)
#>              x1         x2 fidelity stage bracket repetition          y
#>           <num>      <num>    <num> <num>   <int>      <num>      <num>
#>  1: -3.78874794  8.9439418 0.015625     0       6          1  14.781959
#>  2:  7.51499556  2.8727705 0.015625     0       6          1  63.557786
#>  3:  4.01141329 14.2164591 0.015625     0       6          1 202.787861
#>  4: -2.64187338  8.1372061 0.015625     0       6          1   6.779880
#>  5: -4.88900838  8.1690509 0.015625     0       6          1  51.971208
#>  6:  1.99590246  4.1789573 0.015625     0       6          1   7.560334
#>  7:  2.46666083  6.7005370 0.015625     0       6          1  22.210292
#>  8: -0.65349133  5.5726678 0.015625     0       6          1  19.815710
#>  9:  5.99322981  0.4209146 0.015625     0       6          1  27.352543
#> 10:  6.58782267  6.9898079 0.015625     0       6          1 121.984687
#> 11:  8.11900991  5.8504708 0.015625     0       6          1 122.950307
#> 12: -2.37589060  0.3009783 0.015625     0       6          1  96.276269
#> 13: -4.48638001  5.6545639 0.015625     0       6          1  73.532730
#> 14: -0.19421404  8.3986926 0.015625     0       6          1  23.783136
#> 15:  1.03492358 12.8562538 0.015625     0       6          1  86.651345
#> 16: -2.06495248  5.7721457 0.015625     0       6          1  18.735016
#> 17:  1.05307176  7.9187555 0.015625     0       6          1  27.430526
#> 18: -4.04507814  9.0095629 0.015625     0       6          1  19.514032
#> 19:  0.83051970  3.9205704 0.015625     0       6          1  17.083118
#> 20:  9.63321753  4.3507524 0.015625     0       6          1 117.877434
#> 21: -0.65161557  7.2011276 0.015625     0       6          1  17.657487
#> 22:  5.17570641 13.8000832 0.015625     0       6          1 245.751887
#> 23:  6.02979398  6.0108028 0.015625     0       6          1  91.368896
#> 24: -2.06064900  3.1975907 0.015625     0       6          1  44.078574
#> 25:  9.70809512 10.0765022 0.015625     0       6          1 277.324244
#> 26:  6.12282294  0.8792117 0.015625     0       6          1  31.528931
#> 27: -4.22830586 14.9560370 0.015625     0       6          1   8.342752
#> 28:  2.95318695  2.2355320 0.015625     0       6          1   1.013389
#> 29:  5.43735818  7.7783495 0.015625     0       6          1 107.057527
#> 30:  5.32834005 12.6918008 0.015625     0       6          1 220.013816
#> 31: -4.53154512 10.7740459 0.015625     0       6          1  17.694913
#> 32: -1.61656198  3.6197103 0.015625     0       6          1  34.896610
#> 33: -0.48753791  8.2056505 0.015625     0       6          1  20.506586
#> 34:  4.54698422 12.5220272 0.015625     0       6          1 180.634251
#> 35:  2.18536825  0.4193404 0.015625     0       6          1   9.523050
#> 36:  1.48256887  7.0407645 0.015625     0       6          1  21.953386
#> 37:  5.59650757 12.0852005 0.015625     0       6          1 214.246974
#> 38:  9.22864864 12.2107697 0.015625     0       6          1 334.738923
#> 39: -2.29491848  6.0586650 0.015625     0       6          1  17.744430
#> 40: -1.74650185  3.2764651 0.015625     0       6          1  39.647547
#> 41:  5.20244376  6.2754210 0.015625     0       6          1  74.167080
#> 42:  2.48268416 10.0330612 0.015625     0       6          1  63.167824
#> 43:  4.62519023  7.6147542 0.015625     0       6          1  78.356573
#> 44:  4.90426524  9.9053896 0.015625     0       6          1 132.199431
#> 45: -3.55963763  7.6768697 0.015625     0       6          1  20.392237
#> 46:  6.48400246 12.5332866 0.015625     0       6          1 261.529668
#> 47:  6.54512206 10.6317174 0.015625     0       6          1 207.827119
#> 48:  9.86068468 13.1130891 0.015625     0       6          1 394.018374
#> 49:  9.55781354  0.1721931 0.015625     0       6          1  43.715083
#> 50:  0.83774141 13.3237435 0.015625     0       6          1  90.996559
#> 51:  1.91779697 14.9452038 0.015625     0       6          1 147.973171
#> 52: -0.27137371  7.5028725 0.015625     0       6          1  20.392837
#> 53: -2.37986159  5.3845054 0.015625     0       6          1  24.003227
#> 54:  2.97360311 11.6236953 0.015625     0       6          1 102.229329
#> 55:  2.40455524  8.7671288 0.015625     0       6          1  44.059174
#> 56:  6.68962939  9.5096456 0.015625     0       6          1 182.162506
#> 57: -1.93732486 12.8799923 0.015625     0       6          1  20.110380
#> 58:  5.70095918  8.5034151 0.015625     0       6          1 129.902880
#> 59: -4.02175832  3.7949553 0.015625     0       6          1  86.751308
#> 60:  0.31310198 13.7820482 0.015625     0       6          1  87.649828
#> 61:  7.37799132 13.0102531 0.015625     0       6          1 306.091599
#> 62: -0.89272632  3.7280805 0.015625     0       6          1  29.841207
#> 63:  3.55067427  6.0432182 0.015625     0       6          1  29.350783
#> 64:  0.03578621 11.5444526 0.015625     0       6          1  50.971297
#>              x1         x2 fidelity stage bracket repetition          y
#>               timestamp batch_nr x_domain_x1 x_domain_x2 x_domain_fidelity
#>                  <POSc>    <int>       <num>       <num>             <num>
#>  1: 2024-06-30 09:53:08        1 -3.78874794   8.9439418          0.015625
#>  2: 2024-06-30 09:53:08        1  7.51499556   2.8727705          0.015625
#>  3: 2024-06-30 09:53:08        1  4.01141329  14.2164591          0.015625
#>  4: 2024-06-30 09:53:08        1 -2.64187338   8.1372061          0.015625
#>  5: 2024-06-30 09:53:08        1 -4.88900838   8.1690509          0.015625
#>  6: 2024-06-30 09:53:08        1  1.99590246   4.1789573          0.015625
#>  7: 2024-06-30 09:53:08        1  2.46666083   6.7005370          0.015625
#>  8: 2024-06-30 09:53:08        1 -0.65349133   5.5726678          0.015625
#>  9: 2024-06-30 09:53:08        1  5.99322981   0.4209146          0.015625
#> 10: 2024-06-30 09:53:08        1  6.58782267   6.9898079          0.015625
#> 11: 2024-06-30 09:53:08        1  8.11900991   5.8504708          0.015625
#> 12: 2024-06-30 09:53:08        1 -2.37589060   0.3009783          0.015625
#> 13: 2024-06-30 09:53:08        1 -4.48638001   5.6545639          0.015625
#> 14: 2024-06-30 09:53:08        1 -0.19421404   8.3986926          0.015625
#> 15: 2024-06-30 09:53:08        1  1.03492358  12.8562538          0.015625
#> 16: 2024-06-30 09:53:08        1 -2.06495248   5.7721457          0.015625
#> 17: 2024-06-30 09:53:08        1  1.05307176   7.9187555          0.015625
#> 18: 2024-06-30 09:53:08        1 -4.04507814   9.0095629          0.015625
#> 19: 2024-06-30 09:53:08        1  0.83051970   3.9205704          0.015625
#> 20: 2024-06-30 09:53:08        1  9.63321753   4.3507524          0.015625
#> 21: 2024-06-30 09:53:08        1 -0.65161557   7.2011276          0.015625
#> 22: 2024-06-30 09:53:08        1  5.17570641  13.8000832          0.015625
#> 23: 2024-06-30 09:53:08        1  6.02979398   6.0108028          0.015625
#> 24: 2024-06-30 09:53:08        1 -2.06064900   3.1975907          0.015625
#> 25: 2024-06-30 09:53:08        1  9.70809512  10.0765022          0.015625
#> 26: 2024-06-30 09:53:08        1  6.12282294   0.8792117          0.015625
#> 27: 2024-06-30 09:53:08        1 -4.22830586  14.9560370          0.015625
#> 28: 2024-06-30 09:53:08        1  2.95318695   2.2355320          0.015625
#> 29: 2024-06-30 09:53:08        1  5.43735818   7.7783495          0.015625
#> 30: 2024-06-30 09:53:08        1  5.32834005  12.6918008          0.015625
#> 31: 2024-06-30 09:53:08        1 -4.53154512  10.7740459          0.015625
#> 32: 2024-06-30 09:53:08        1 -1.61656198   3.6197103          0.015625
#> 33: 2024-06-30 09:53:08        1 -0.48753791   8.2056505          0.015625
#> 34: 2024-06-30 09:53:08        1  4.54698422  12.5220272          0.015625
#> 35: 2024-06-30 09:53:08        1  2.18536825   0.4193404          0.015625
#> 36: 2024-06-30 09:53:08        1  1.48256887   7.0407645          0.015625
#> 37: 2024-06-30 09:53:08        1  5.59650757  12.0852005          0.015625
#> 38: 2024-06-30 09:53:08        1  9.22864864  12.2107697          0.015625
#> 39: 2024-06-30 09:53:08        1 -2.29491848   6.0586650          0.015625
#> 40: 2024-06-30 09:53:08        1 -1.74650185   3.2764651          0.015625
#> 41: 2024-06-30 09:53:08        1  5.20244376   6.2754210          0.015625
#> 42: 2024-06-30 09:53:08        1  2.48268416  10.0330612          0.015625
#> 43: 2024-06-30 09:53:08        1  4.62519023   7.6147542          0.015625
#> 44: 2024-06-30 09:53:08        1  4.90426524   9.9053896          0.015625
#> 45: 2024-06-30 09:53:08        1 -3.55963763   7.6768697          0.015625
#> 46: 2024-06-30 09:53:08        1  6.48400246  12.5332866          0.015625
#> 47: 2024-06-30 09:53:08        1  6.54512206  10.6317174          0.015625
#> 48: 2024-06-30 09:53:08        1  9.86068468  13.1130891          0.015625
#> 49: 2024-06-30 09:53:08        1  9.55781354   0.1721931          0.015625
#> 50: 2024-06-30 09:53:08        1  0.83774141  13.3237435          0.015625
#> 51: 2024-06-30 09:53:08        1  1.91779697  14.9452038          0.015625
#> 52: 2024-06-30 09:53:08        1 -0.27137371   7.5028725          0.015625
#> 53: 2024-06-30 09:53:08        1 -2.37986159   5.3845054          0.015625
#> 54: 2024-06-30 09:53:08        1  2.97360311  11.6236953          0.015625
#> 55: 2024-06-30 09:53:08        1  2.40455524   8.7671288          0.015625
#> 56: 2024-06-30 09:53:08        1  6.68962939   9.5096456          0.015625
#> 57: 2024-06-30 09:53:08        1 -1.93732486  12.8799923          0.015625
#> 58: 2024-06-30 09:53:08        1  5.70095918   8.5034151          0.015625
#> 59: 2024-06-30 09:53:08        1 -4.02175832   3.7949553          0.015625
#> 60: 2024-06-30 09:53:08        1  0.31310198  13.7820482          0.015625
#> 61: 2024-06-30 09:53:08        1  7.37799132  13.0102531          0.015625
#> 62: 2024-06-30 09:53:08        1 -0.89272632   3.7280805          0.015625
#> 63: 2024-06-30 09:53:08        1  3.55067427   6.0432182          0.015625
#> 64: 2024-06-30 09:53:08        1  0.03578621  11.5444526          0.015625
#>               timestamp batch_nr x_domain_x1 x_domain_x2 x_domain_fidelity

Source

Resources

Dictionary

Parameters

Archive

Custom Sampler

Progress Bars

Logging

Super classes

Methods

Public methods

Method new()

Usage

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `clone()`