benchmark/tpe_func.R

In smilesun/reinbo:

# TPE algorithm:
opt.tpe = function(task, budget, measure, train_set = NULL) {
  subTask <<- task
  if (!is.null(train_set)) subTask <<- subsetTask(task, train_set)
  inner_loop <<- makeResampleInstance("CV", iters = getGconf()$NCVInnerIter, stratify = TRUE, subTask)
  source_python("tpe_space.py")
  hp = import("hyperopt")
  model_index <<- 0
  model_list <<- list()
  perf_list <<- NULL
  measure <<- measure
  best = hp$fmin(objective, space = space, algo = hp$tpe$suggest, max_evals = budget)
  best_model_index = which(perf_list == min(perf_list))[1]
  mmodel = model_list[[best_model_index]]
  return(mmodel)
}


# Predict function: evaluate best model on test dataset
lock_eval.tpe = function(task, measure, train_set, test_set, best_model){
  lrn = genLearner.tpe(best_model)
  mod = train(lrn, task, subset = train_set)
  pred = predict(mod, task, subset = test_set)
  mpred = performance(pred, measures = measure)
  return(mpred)
}


# Objective to optimize:
objective = function(args) {
  model_index <<- model_index + 1
  model_list[[model_index]] <<- args
  lrn = genLearner.tpe(args)
  perf = resample(lrn, subTask, resampling = inner_loop, measures = measure, show.info = FALSE)$aggr
  perf_list <<- c(perf_list, as.numeric(perf))
  return(perf)
}
# one sample of args: args = hp$pyll$stochastic$sample(py$space)


# Generate mlr learner for configuration:
genLearner.tpe = function(args){
  model = args$Classifier$model
  args$Classifier$model = NULL
  ps.learner = args$Classifier    
  filter = args$FeatureFilter$filter
  lrn = sprintf("%s %%>>%% %s %%>>%% makeLearner('classif.%s', par.vals = ps.learner)", 
                args$Preprocess, filter, model)
  lrn = gsub(pattern = "NA %>>%", x = lrn, replacement = "", fixed = TRUE)
  lrn = gsub(pattern = "perc", x = lrn, replacement = "perc = args$FeatureFilter$perc", fixed = TRUE)
  p = getTaskNFeats(subTask)
  lrn = gsub(pattern = "rank", x = lrn, replacement = "rank = as.integer(max(1, round(p*args$FeatureFilter$rank)))", fixed = TRUE)
  if (model == "ranger") {
    p1 = p
    if (!is.null(args$FeatureFilter$perc)) {p1 = max(1, round(p*args$FeatureFilter$perc))}
    if (!is.null(args$FeatureFilter$rank)) {p1 = max(1, round(p*args$FeatureFilter$rank))}
    ps.learner$mtry = max(1, as.integer(p1*args$FeatureFilter$mtry))
  }
  lrn = eval(parse(text = lrn))
  return(lrn)
}