class_docstr: Fraud Yelp Dataset
  
  The Yelp dataset includes hotel and restaurant reviews filtered (spam) and recommended
  (legitimate) by Yelp. A spam review detection task can be conducted, which is a binary
  classification task. 32 handcrafted features from <http://dx.doi.org/10.1145/2783258.2783370>
  are taken as the raw node features. Reviews are nodes in the graph, and three relations are:
  
      1. R-U-R: it connects reviews posted by the same user
      2. R-S-R: it connects reviews under the same product with the same star rating (1-5 stars)
      3. R-T-R: it connects two reviews under the same product posted in the same month.
  
  Statistics:
  
  - Nodes: 45,954
  - Edges:
  
      - R-U-R: 98,630
      - R-T-R: 1,147,232
      - R-S-R: 6,805,486
  
  - Classes:
  
      - Positive (spam): 6,677
      - Negative (legitimate): 39,277
  
  - Positive-Negative ratio: 1 : 5.9
  - Node feature size: 32
  
  Parameters
  ----------
  raw_dir : str
      Specifying the directory that will store the
      downloaded data or the directory that
      already stores the input data.
      Default: ~/.dgl/
  random_seed : int
      Specifying the random seed in splitting the dataset.
      Default: 717
  train_size : float
      training set size of the dataset.
      Default: 0.7
  val_size : float
      validation set size of the dataset, and the
      size of testing set is (1 - train_size - val_size)
      Default: 0.1
  force_reload : bool
      Whether to reload the dataset. Default: False
  verbose : bool
      Whether to print out progress information. Default: True.
  transform : callable, optional
      A transform that takes in a :class:`~dgl.DGLGraph` object and returns
      a transformed version. The :class:`~dgl.DGLGraph` object will be
      transformed before every access.
  
  Examples
  --------
  >>> dataset = FraudYelpDataset()
  >>> graph = dataset[0]
  >>> num_classes = dataset.num_classes
  >>> feat = graph.ndata['feature']
  >>> label = graph.ndata['label']
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code: def __init__(
      self,
      raw_dir=None,
      random_seed=717,
      train_size=0.7,
      val_size=0.1,
      force_reload=False,
      verbose=True,
      transform=None,
  ):
      super(FraudYelpDataset, self).__init__(
          name="yelp",
          raw_dir=raw_dir,
          random_seed=random_seed,
          train_size=train_size,
          val_size=val_size,
          force_reload=force_reload,
          verbose=verbose,
          transform=transform,
      )
  
imports: import os
  import numpy
  from scipy import io
  from ..None import backend
  from ..convert import heterograph
  from .dgl_dataset import DGLBuiltinDataset
  from .utils import _get_dgl_url, load_graphs, save_graphs
label_desc: Use image node_id 1 to create a new FraudYelpDataset object from inherited base classes: FraudDataset with example: obj = FraudYelpDataset(raw_dir, random_seed, train_size, val_size, force_reload, verbose, transform)