[bugfix] Fix force_reload parameter of FraudDataset (#3210)

* enable force_reload of FraudDataset

* rewrite hash_key in FraudDataset

python/dgl/data/fraud.py

@@ -46,6 +46,10 @@ class FraudDataset(DGLBuiltinDataset):
         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.
 
     Attributes
     ----------
@@ -84,8 +88,9 @@ class FraudDataset(DGLBuiltinDataset):
         'yelp': 'review',
         'amazon': 'user'
     }
-
-    def __init__(self, name, raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1):
+    
+    def __init__(self, name, raw_dir=None, random_seed=717, train_size=0.7,
+                 val_size=0.1, force_reload=False, verbose=True):
         assert name in ['yelp', 'amazon'], "only supports 'yelp', or 'amazon'"
         url = _get_dgl_url(self.file_urls[name])
         self.seed = random_seed
@@ -93,29 +98,32 @@ class FraudDataset(DGLBuiltinDataset):
         self.val_size = val_size
         super(FraudDataset, self).__init__(name=name,
                                            url=url,
-                                           raw_dir=raw_dir)
-
+                                           raw_dir=raw_dir,
+                                           hash_key=(random_seed, train_size, val_size),
+                                           force_reload=force_reload,
+                                           verbose=verbose)
+    
     def process(self):
         """process raw data to graph, labels, splitting masks"""
         file_path = os.path.join(self.raw_path, self.file_names[self.name])
-
+        
         data = io.loadmat(file_path)
         node_features = data['features'].todense()
         node_labels = data['label']
-
+        
         graph_data = {}
         for relation in self.relations[self.name]:
             adj = data[relation].tocoo()
             row, col = adj.row, adj.col
             graph_data[(self.node_name[self.name], relation, self.node_name[self.name])] = (row, col)
         g = heterograph(graph_data)
-
+        
         g.ndata['feature'] = F.tensor(node_features)
         g.ndata['label'] = F.tensor(node_labels.T)
         self.graph = g
-
+        
         self._random_split(g.ndata['feature'], self.seed, self.train_size, self.val_size)
-
+    
     def __getitem__(self, idx):
         r""" Get graph object
 
@@ -137,11 +145,11 @@ class FraudDataset(DGLBuiltinDataset):
         """
         assert idx == 0, "This dataset has only one graph"
         return self.graph
-
+    
     def __len__(self):
         """number of data examples"""
         return len(self.graph)
-
+    
     @property
     def num_classes(self):
         """Number of classes.
@@ -151,37 +159,37 @@ class FraudDataset(DGLBuiltinDataset):
         int
         """
         return 2
-
+    
     def save(self):
         """save processed data to directory `self.save_path`"""
-        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph.bin')
+        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph_{}.bin'.format(self.hash))
         save_graphs(str(graph_path), self.graph)
-
+    
     def load(self):
         """load processed data from directory `self.save_path`"""
-        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph.bin')
+        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph_{}.bin'.format(self.hash))
         graph_list, _ = load_graphs(str(graph_path))
         g = graph_list[0]
         self.graph = g
-
+    
     def has_cache(self):
         """check whether there are processed data in `self.save_path`"""
-        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph.bin')
+        graph_path = os.path.join(self.save_path, self.name + '_dgl_graph_{}.bin'.format(self.hash))
         return os.path.exists(graph_path)
-
+    
     def _random_split(self, x, seed=717, train_size=0.7, val_size=0.1):
         """split the dataset into training set, validation set and testing set"""
-
+        
         assert 0 <= train_size + val_size <= 1, \
             "The sum of valid training set size and validation set size " \
             "must between 0 and 1 (inclusive)."
-
+        
         N = x.shape[0]
         index = np.arange(N)
         if self.name == 'amazon':
             # 0-3304 are unlabeled nodes
             index = np.arange(3305, N)
-
+        
         index = np.random.RandomState(seed).permutation(index)
         train_idx = index[:int(train_size * len(index))]
         val_idx = index[len(index) - int(val_size * len(index)):]
@@ -243,6 +251,10 @@ class FraudYelpDataset(FraudDataset):
         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.
 
     Examples
     --------
@@ -252,13 +264,16 @@ class FraudYelpDataset(FraudDataset):
     >>> feat = graph.ndata['feature']
     >>> label = graph.ndata['label']
     """
-
-    def __init__(self, raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1):
+    
+    def __init__(self, raw_dir=None, random_seed=717, train_size=0.7,
+                 val_size=0.1, force_reload=False, verbose=True):
         super(FraudYelpDataset, self).__init__(name='yelp',
                                                raw_dir=raw_dir,
                                                random_seed=random_seed,
                                                train_size=train_size,
-                                               val_size=val_size)
+                                               val_size=val_size,
+                                               force_reload=force_reload,
+                                               verbose=verbose)
 
 
 class FraudAmazonDataset(FraudDataset):
@@ -312,6 +327,10 @@ class FraudAmazonDataset(FraudDataset):
         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.
 
     Examples
     --------
@@ -321,10 +340,13 @@ class FraudAmazonDataset(FraudDataset):
     >>> feat = graph.ndata['feature']
     >>> label = graph.ndata['label']
     """
-
-    def __init__(self, raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1):
+    
+    def __init__(self, raw_dir=None, random_seed=717, train_size=0.7,
+                 val_size=0.1, force_reload=False, verbose=True):
         super(FraudAmazonDataset, self).__init__(name='amazon',
                                                  raw_dir=raw_dir,
                                                  random_seed=random_seed,
                                                  train_size=train_size,
-                                                 val_size=val_size)
+                                                 val_size=val_size,
+                                                 force_reload=force_reload,
+                                                 verbose=verbose)