class_docstr: This algorithm combines our ATPE optimizer with Hyperband
class_docstr_tok: ["This","algorithm","combines","our","ATPE","optimizer","with","Hyperband"]
code: def __init__(self, baseOptimizer, min_budget, max_budget, eta=3):
      self.baseOptimizer = baseOptimizer
      self.randomOptimizer = RandomSearchOptimizer()
  
      self.min_budget = min_budget
      self.max_budget = (
          max_budget  # maximum iterations per configuration
      )
  
      self.eta = (
          eta  # defines configuration downsampling rate (default = 3)
      )
  
      self.logeta = lambda x: log(x) / log(self.eta)
      self.s_max = int(self.logeta(self.max_budget))
      self.B = (self.s_max + 1) * self.max_budget
  
      self.results = []
  
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 1 to create a new AdaptiveBayesianHyperband object from inherited base classes: OptimizationAlgorithmBase with example: obj = AdaptiveBayesianHyperband(baseOptimizer, min_budget, max_budget, eta)