docstr: Suggest the next promosing hyperparameters which were not yet evaluated
docstr_tok: ["Suggest","the","next","promosing","hyperparameters","which","were","not","yet","evaluated"]
code: def metalearning_suggest(self, history):
      """Suggest the next promosing hyperparameters which were not yet evaluated"""
      # TODO test the object in the history!
      neighbors = self._learn()
      # Iterate over all datasets which are sorted ascending by distance
  
      history_with_indices = []
      for run in history:
          history_with_indices.append(
              self.meta_base.get_algorithm_index_from_configuration(run)
          )
  
      for idx, neighbor in enumerate(neighbors):
          already_evaluated = False
          # Check if that dataset was already evaluated
          for run in history_with_indices:
              # If so, return to the outer loop
  
              if neighbor[2] == run:
                  already_evaluated = True
                  break
  
          if not already_evaluated:
              self.logger.info(
                  "Nearest dataset with hyperparameters of best value "
                  "not evaluated yet is %s with a distance of %f"
                  % (neighbor[0], neighbor[1])
              )
              return (
                  self.meta_base.get_configuration_from_algorithm_index(
                      neighbor[2]
                  )
              )
      raise StopIteration("No more values available.")
  
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 3 for calling the MetaLearningOptimizer obj's underlying member method code with example usage: obj.metalearning_suggest(history) and returns: self