algorithm: R6 class to create algorithm objects

In ja-thomas/hyperbandr:

Description

An R6Class to be optimized by hyperband

Usage

algorithm

Format

R6Class object

Value

Algorithm object

Fields

id

[string]
An id for the Algorithm object

configuration

The configuration to use

initial.budget

The budget to use for the initialization of the model

init.fun

The function to initialize the model

train.fun

The function to carry out training

performance.fun

The function to measure the performance

Methods

$continue(budget) continue training for budget iterations
$getPerformance() computes the performance of the model
$visPerformance() visualizes the performance of the model

Examples

# 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]]))
}


#######################################
############# applications ############
#######################################

#### make branin algorithm object ####
obj = algorithm$new(
  problem = braninProb,
  id = "branin",
  configuration = sample.fun(par.set = configSpace, n.configs = 1)[[1]],
  initial.budget = 1,
  init.fun = init.fun,
  train.fun = train.fun,
  performance.fun = performance.fun)

# we can inspect model of our algorithm object
obj$model
# the data matrix shows us the hyperparameters, the current budget and the performance
obj$algorithm.result$data.matrix
# if we are interested in the performance, we can also call the getPerformance method
obj$getPerformance()
# we can continue training our object for one iteration by calling
obj$continue(1)
# inspect of the data matrix has changed
obj$algorithm.result$data.matrix
# continue training for 18 iterations to obtain a total of 20 iterations
invisible(capture.output(replicate(18, obj$continue(1))))
# inspect model the model again
obj$model
# inspect the data matrix again
obj$algorithm.result$data.matrix
# we can immediately visualize the performance function
obj$visPerformance()

ja-thomas/hyperbandr documentation built on May 6, 2019, 8:33 p.m.