class_docstr: This algorithm combines our ATPE optimizer with Hyperband
class_docstr_tok: ["This","algorithm","combines","our","ATPE","optimizer","with","Hyperband"]
code: def createCanonicalStringFromResult(
      self, result, hyperparameterSpace
  ):
      params = Hyperparameter(
          hyperparameterSpace
      ).convertToStructuredValues(result)
  
      keys = list(params.keys())
      for key in keys:
          if (
              key in OptimizationAlgorithmBase.resultInformationKeys
              or key.startswith("$")
          ):
              del params[key]
  
      return json.dumps(params, sort_keys=True)
  
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 4 for calling the AdaptiveBayesianHyperband obj's underlying member method code with example usage: obj.createCanonicalStringFromResult(result, hyperparameterSpace) and returns: json