class_docstr: Fraud node prediction dataset.
  
  The dataset includes two multi-relational graphs extracted from Yelp and Amazon
  where nodes represent fraudulent reviews or fraudulent reviewers.
  
  It was first proposed in a CIKM'20 paper <https://arxiv.org/pdf/2008.08692.pdf> and
  has been used by a recent WWW'21 paper <https://ponderly.github.io/pub/PCGNN_WWW2021.pdf>
  as a benchmark. Another paper <https://arxiv.org/pdf/2104.01404.pdf> also takes
  the dataset as an example to study the non-homophilous graphs. This dataset is built
  upon industrial data and has rich relational information and unique properties like
  class-imbalance and feature inconsistency, which makes the dataset be a good instance
  to investigate how GNNs perform on real-world noisy graphs. These graphs are bidirected
  and not self connected.
  
  Reference: <https://github.com/YingtongDou/CARE-GNN>
  
  Parameters
  ----------
  name : str
      Name of the dataset
  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.
  
  Attributes
  ----------
  num_classes : int
      Number of label classes
  graph : dgl.DGLGraph
      Graph structure, etc.
  seed : int
      Random seed in splitting the dataset.
  train_size : float
      Training set size of the dataset.
  val_size : float
      Validation set size of the dataset
  
  Examples
  --------
  >>> dataset = FraudDataset('yelp')
  >>> graph = dataset[0]
  >>> num_classes = dataset.num_classes
  >>> feat = graph.ndata['feature']
  >>> label = graph.ndata['label']
class_docstr_tok: ["Fraud","node","prediction","dataset",".","The","dataset","includes","two","multi-relational","graphs","extracted","from","Yelp","and","Amazon","where","nodes","represent","fraudulent","reviews","or","fraudulent","reviewers",".","It","was","first","proposed","in","a","CIKM'20","paper","<","https",":","\/\/arxiv.org\/pdf\/2008.08692.pdf",">","and","has","been","used","by","a","recent","WWW'21","paper","<","https",":","\/\/ponderly.github.io\/pub\/PCGNN_WWW2021.pdf",">","as","a","benchmark",".","Another","paper","<","https",":","\/\/arxiv.org\/pdf\/2104.01404.pdf",">","also","takes","the","dataset","as","an","example","to","study","the","non-homophilous","graphs",".","This","dataset","is","built","upon","industrial","data","and","has","rich","relational","information","and","unique","properties","like","class-imbalance","and","feature","inconsistency",",","which","makes","the","dataset","be","a","good","instance","to","investigate","how","GNNs","perform","on","real-world","noisy","graphs",".","These","graphs","are","bidirected","and","not","self","connected",".","Reference",":","<","https",":","\/\/github.com\/YingtongDou\/CARE-GNN",">","Parameters","--","--","--","--","--","name",":","str","Name","of","the","dataset","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",".","Attributes","--","--","--","--","--","num_classes",":","int","Number","of","label","classes","graph",":","dgl.DGLGraph","Graph","structure",",","etc",".","seed",":","int","Random","seed","in","splitting","the","dataset",".","train_size",":","float","Training","set","size","of","the","dataset",".","val_size",":","float","Validation","set","size","of","the","dataset","Examples","--","--","--","--",">",">",">","dataset","=","FraudDataset","(","'yelp","'",")",">",">",">","graph","=","dataset","[","0","]",">",">",">","num_classes","=","dataset.num_classes",">",">",">","feat","=","graph.ndata","[","'feature","'","]",">",">",">","label","=","graph.ndata","[","'label","'","]"]
docstr: number of data examples
docstr_tok: ["number","of","data","examples"]
code: def __len__(self):
      """number of data examples"""
      return len(self.graph)
  
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 4 for calling the FraudDataset obj's underlying member method code with example usage: obj.__len__() and returns: len