class_docstr: This algorithm combines our ATPE optimizer with Hyperband
class_docstr_tok: ["This","algorithm","combines","our","ATPE","optimizer","with","Hyperband"]
code: def createBudgetSchedule(self):
      runs = []
  
      for s in reversed(range(self.s_max + 1)):
          # initial number of configurations
          n = self.B / self.max_budget / (s + 1) * self.eta**s
  
          # initial amount of budget per config
          r = self.max_budget * self.eta ** (-s)
  
          runs_in_sequence = 0
  
          if round(r) >= self.min_budget:
              for i in range((s + 1)):
                  # Run each of the n configs for <iterations>
                  # and keep best (n_configs / eta) configurations
  
                  n_configs = n * self.eta ** (-i)
                  n_budget = r * self.eta ** (i)
  
                  runs.append(
                      {
                          "group": s,
                          "round": runs_in_sequence,
                          "configs_start": int(round(n_configs)),
                          "configs_finish": int(
                              round(n_configs / self.eta)
                          ),
                          "input_configs": int(
                              round(n_configs * self.eta)
                          ),
                          "input_round": runs_in_sequence - 1,
                          "input_budget": -1
                          if i == 0
                          else int(ceil(r * self.eta ** (i - 1))),
                          "budget": int(round(n_budget)),
                      }
                  )
  
                  runs_in_sequence += 1
  
      return runs
  
file_path: hypermax/hypermax/algorithms/adaptive_bayesian_hyperband_optimizer.py
imports: import numpy
  import random
  from math import log, ceil
  from time import time, ctime
  from .optimization_algorithm_base import OptimizationAlgorithmBase
  from .random_search_optimizer import RandomSearchOptimizer
  from pprint import pprint
  from ..hyperparameter import Hyperparameter
  import hyperopt
  import json
  import functools
  import copy
label_desc: Use image node_id 3 for calling the AdaptiveBayesianHyperband obj's underlying member method code with example usage: obj.createBudgetSchedule() and returns: runs