class_docstr: The pareto recommender recommends models by predicting their performance
  using a surrogate model and computing the Pareto front on the predictions.
class_docstr_tok: ["The","pareto","recommender","recommends","models","by","predicting","their","performance","using","a","surrogate","model","and","computing","the","Pareto","front","on","the","predictions","."]
docstr: Args:
      surrogate: The surrogate model which predicts metrics from models and their
          configurations. The surrogate will be trained when the `fit` method is called.
      objectives: The list of performance metrics to minimize.
      focus: The metric to prefer. Must be either in the list of objectives. If not
          provided, the first metric to optimize is chosen.
      generator: The generator that generates configurations for recommendations. By default,
          this is the replay candidate generator.
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code: def __init__(
      self,
      surrogate: Surrogate[T],
      objectives: List[str],
      focus: Optional[str] = None,
      generator: Optional[CandidateGenerator[T]] = None,
  ):
      """
      Args:
          surrogate: The surrogate model which predicts metrics from models and their
              configurations. The surrogate will be trained when the `fit` method is called.
          objectives: The list of performance metrics to minimize.
          focus: The metric to prefer. Must be either in the list of objectives. If not
              provided, the first metric to optimize is chosen.
          generator: The generator that generates configurations for recommendations. By default,
              this is the replay candidate generator.
      """
      super().__init__(objectives, focus, generator)
      self.surrogate = surrogate
  
file_path: gluonts/src/gluonts/nursery/tsbench/src/tsbench/recommender/pareto.py
imports: from typing import List, Optional
  from tsbench.config import Config
  from tsbench.evaluations.metrics import Performance
  from tsbench.surrogate import Surrogate
  from ._base import Recommender, T
  from ._factory import register_recommender
  from .generator import CandidateGenerator
label_desc: Use image node_id 1 to create a new ParetoRecommender object from inherited base classes: Recommender with example: obj = ParetoRecommender(surrogate, objectives, focus, generator)