bracket: R6 class to create single bracket objects
In ja-thomas/hyperbandr:

Description Usage Format Value Fields Methods Examples

An R6Class that consits of multiple algorithm objects

bracket

R6Class object

Bracket object

max.perf: [logical()]
TRUE if to maximize the performance (e.g. accuracy of a neural net), FALSE if to minimize the performance (e.g. find the minimum of the branin function).
max.resources: [integer()]
The maximum amount of resource that can be allocated to a single configuration
prop.discard: [integer()]
An input that controls the proportion of configurations discarded in each round of successive halving
s: [integer()]
The s'th bracket object to create. Note that s is in
(0,...,floor(log(max.resources, base = prop.discard)))
B: [integer()]
The total budget for the bracket. Note that B is given by
(max(s) + 1)*max.resources
id: [string]
An id for each Algorithm object in the bracket object
par.set: The parameter set to sample from
sample.fun: The function to sample from par.set
train.fun: The function to carry out training
performance.fun: The function to measure the performance

$run() computes the whole bracket
$step() computes one iteration of successive halving
$getTopKModels(k) displays the best k models
$filterTopKModels(k) filters the best k models and deletes the remaining models from the bracket object
$getPerformances() computes the performance of all remaining models

# we need some packages
library("ggplot2")
library("smoof")
library("data.table")

# we choose the 2 dimensional branin function
braninProb = makeBraninFunction()

# the branin function has 3 global minima
opt = data.table(x1 = getGlobalOptimum(braninProb)$param$x1,
  x2 = getGlobalOptimum(braninProb)$param$x2)
param.set = getParamSet(braninProb)


#######################################
## define functions to use hyperband ##
#######################################

# config space
configSpace = makeParamSet(
    makeNumericParam(id = "x1", lower = -5, upper = 10.1))

# sample fun
sample.fun = function(par.set, n.configs, ...) {
  sampleValues(par = par.set, n = n.configs)
}

# init fun
init.fun = function(r, config, problem) {
  x1 = unname(unlist(config))
  x2 = runif(1, 0, 15)
  mod = c(x1, x2)
  return(mod)
}

# train fun
train.fun = function(mod, budget, problem) {
  for(i in seq_len(budget)) {
    mod.new = c(mod[[1]], mod[[2]] + rnorm(1, sd = 3))
    if(performance.fun(mod.new) < performance.fun(mod))
      mod = mod.new
  }
  return(mod)
}

# performance fun
performance.fun = function(model, problem) {
  braninProb(c(model[[1]], model[[2]]))
}
###### make branin bracket object #####
brack = bracket$new(
  problem = braninProb,
  max.perf = FALSE,
  max.resources = 81,
  prop.discard = 3,
  s = 4,
  B = (4 + 1)*81,
  id = "branin",
  par.set = configSpace,
  sample.fun = sample.fun,
  init.fun = init.fun,
  train.fun = train.fun,
  performance.fun = performance.fun)

# the data matrix shows us the hyperparameters, the current budget and the performance
brack$bracket.storage$data.matrix
# run the bracket
brack$run()
# inspect the data matrix again
brack$bracket.storage$data.matrix
# visualize the the bracket
# access the performance of the best model
brack$getPerformances()