docstr: Return a list of the best hyperparameters of neighboring datasets
docstr_tok: ["Return","a","list","of","the","best","hyperparameters","of","neighboring","datasets"]
code: def metalearning_suggest_all(
      self, exclude_double_configurations=True
  ):
      """Return a list of the best hyperparameters of neighboring datasets"""
      # TODO check if _learn was called before!
      neighbors = self._learn(exclude_double_configurations)
      hp_list = []
      for neighbor in neighbors:
          try:
              configuration = (
                  self.meta_base.get_configuration_from_algorithm_index(
                      neighbor[2]
                  )
              )
              self.logger.info(
                  "%s %s %s" % (neighbor[0], neighbor[1], configuration)
              )
          except KeyError:
              self.logger.warning(
                  "Configuration %s not found" % neighbor[2]
              )
              continue
  
          hp_list.append(configuration)
      return hp_list
  
file_path: auto-sklearn/autosklearn/metalearning/optimizers/metalearn_optimizer/metalearner.py
imports: import ast
  import numpy
  import pandas
  import sklearn.utils
  from autosklearn.metalearning.metalearning.kNearestDatasets.kND import KNearestDatasets
label_desc: Use image node_id 2 for calling the MetaLearningOptimizer obj's underlying member method code with example usage: obj.metalearning_suggest_all(exclude_double_configurations) and returns: hp_list