code: def __init__(self):
      scalingModelData = json.loads(
          pkg_resources.resource_string(
              __name__, "../atpe_models/scaling_model.json"
          )
      )
      self.featureScalingModels = {}
      for key in self.atpeModelFeatureKeys:
          self.featureScalingModels[
              key
          ] = sklearn.preprocessing.StandardScaler()
          self.featureScalingModels[key].scale_ = numpy.array(
              scalingModelData[key]["scales"]
          )
          self.featureScalingModels[key].mean_ = numpy.array(
              scalingModelData[key]["means"]
          )
          self.featureScalingModels[key].var_ = numpy.array(
              scalingModelData[key]["variances"]
          )
  
      self.parameterModels = {}
      self.parameterModelConfigurations = {}
      for param in self.atpeParameters:
          modelData = pkg_resources.resource_string(
              __name__, "../atpe_models/model-" + param + ".txt"
          )
          with hypermax.file_utils.ClosedNamedTempFile(
              modelData
          ) as model_file_name:
              self.parameterModels[param] = lightgbm.Booster(
                  model_file=model_file_name
              )
  
          configString = pkg_resources.resource_string(
              __name__,
              "../atpe_models/model-" + param + "-configuration.json",
          )
          data = json.loads(configString)
          self.parameterModelConfigurations[param] = data
  
      self.lastATPEParameters = None
      self.lastLockedParameters = []
      self.atpeParamDetails = None
  
imports: from .optimization_algorithm_base import OptimizationAlgorithmBase
  import hyperopt
  import functools
  import random
  import numpy
  import numpy.random
  import json
  import pkg_resources
  from hypermax.hyperparameter import Hyperparameter
  import sklearn
  import lightgbm
  import scipy.stats
  import math
  from pprint import pprint
  import copy
  import hypermax.file_utils
label_desc: Use image node_id 1 to create a new ATPEOptimizer object from inherited base classes: OptimizationAlgorithmBase with example: obj = ATPEOptimizer()