class_docstr: A dataset split provides all the representations for a particular split (train/val/test) of a
  dataset.
class_docstr_tok: ["A","dataset","split","provides","all","the","representations","for","a","particular","split","(","train\/val\/test",")","of","a","dataset","."]
docstr: Returns the NumPy arrays that are used to perform evaluation.
docstr_tok: ["Returns","the","NumPy","arrays","that","are","used","to","perform","evaluation","."]
code: def evaluation(self) -> EvaluationDataset:
      """
      Returns the NumPy arrays that are used to perform evaluation.
      """
      if self._split == "train":
          raise ValueError(
              "training data does not provide an evaluation dataset"
          )
  
      base = self._directory / "numpy" / self._split
      return EvaluationDataset(
          np.load(base / "future_data.npy")[
              ..., : self._prediction_length
          ],
          ma.MaskedArray(
              np.load(base / "past_data.npy"),
              mask=np.load(base / "past_mask.npy"),
          ),
      )
  
imports: from __future__ import annotations
  import json
  from dataclasses import dataclass
  from typing import Tuple, Optional, List
  from pathlib import Path
  from gluonts.dataset.common import FileDataset, MetaData
  from gluonts.dataset.field_names import FieldName
  import torch
  import numpy
  import numpy.ma
  import shutil
  from meta.data.dataset import TimeSeries, TimeSeriesDataset
  from meta.common import tensor_to_np
label_desc: Use image node_id 3 for calling the DatasetSplit obj's underlying member method code with example usage: obj.evaluation() and returns: EvaluationDataset