class_docstr: Insects dataset.
  
  This dataset has different variants, which are:
  
  - abrupt_balanced
  - abrupt_imbalanced
  - gradual_balanced
  - gradual_imbalanced
  - incremental-abrupt_balanced
  - incremental-abrupt_imbalanced
  - incremental-reoccurring_balanced
  - incremental-reoccurring_imbalanced
  - incremental_balanced
  - incremental_imbalanced
  - out-of-control
  
  The number of samples and the difficulty change from one variant to another. The number of
  classes is always the same (6), except for the last variant (24).
  
  Parameters
  ----------
  variant
      Indicates which variant of the dataset to load.
  
  References
  ----------
  [^1]: [USP DS repository](https://sites.google.com/view/uspdsrepository)
  [^2]: [Souza, V., Reis, D.M.D., Maletzke, A.G. and Batista, G.E., 2020. Challenges in Benchmarking Stream Learning Algorithms with Real-world Data. arXiv preprint arXiv:2005.00113.](https://arxiv.org/abs/2005.00113)
code: def __init__(self, variant="abrupt_balanced"):
      try:
          n_samples, size = self.variant_sizes[variant]
      except KeyError:
          variants = "\n".join(f"- {v}" for v in self.variant_sizes)
          raise ValueError(
              f"Unknown variant, possible choices are:\n{variants}"
          )
      n_classes = 24 if variant == "out-of-control" else 6
  
      super().__init__(
          n_classes=n_classes,
          n_samples=n_samples,
          n_features=33,
          task=base.MULTI_CLF,
          url=f"http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-{variant}_norm.arff",
          size=size,
          unpack=False,
      )
      self.variant = variant
  
file_path: river/river/datasets/insects.py
imports: from __future__ import annotations
  from river import stream
  from .None import base
label_desc: Use image node_id 1 to create a new Insects object from inherited base classes: base with example: obj = Insects(variant)