[Data] AsGraphPredDataset (dmlc#4073)

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docs/source/api/python/dgl.data.rst

@@ -99,6 +99,7 @@ Dataset adapters
 
     AsNodePredDataset
     AsLinkPredDataset
+    AsGraphPredDataset
 
 Utilities
 -----------------

docs/source/guide/data-loadcsv.rst

@@ -389,6 +389,8 @@ After loaded, the dataset has multiple homographs with features and labels:
     >>> print(data1)
     {'feat': tensor([0.5348, 0.2864, 0.1155], dtype=torch.float64), 'label': tensor(0)}
 
+If there is a single feature column in ``graphs.csv``, ``data0`` will directly be a tensor for the feature.
+
 
 Custom Data Parser
 ~~~~~~~~~~~~~~~~~~

docs/source/guide/data-process.rst

@@ -29,41 +29,41 @@ the data.
 
 Take :class:`~dgl.data.QM7bDataset` as example:
 
-.. code:: 
+.. code::
 
     from dgl.data import DGLDataset
 
     class QM7bDataset(DGLDataset):
         _url = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/' \
                'datasets/qm7b.mat'
         _sha1_str = '4102c744bb9d6fd7b40ac67a300e49cd87e28392'
-    
+
         def __init__(self, raw_dir=None, force_reload=False, verbose=False):
             super(QM7bDataset, self).__init__(name='qm7b',
                                               url=self._url,
                                               raw_dir=raw_dir,
                                               force_reload=force_reload,
                                               verbose=verbose)
-    
+
         def process(self):
             mat_path = self.raw_path + '.mat'
             # process data to a list of graphs and a list of labels
             self.graphs, self.label = self._load_graph(mat_path)
-        
+
         def __getitem__(self, idx):
             """ Get graph and label by index
-    
+
             Parameters
             ----------
             idx : int
                 Item index
-    
+
             Returns
             -------
             (dgl.DGLGraph, Tensor)
             """
             return self.graphs[idx], self.label[idx]
-    
+
         def __len__(self):
             """Number of graphs in the dataset"""
             return len(self.graphs)
@@ -78,33 +78,33 @@ for details of ``self._load_graph()`` and ``__getitem__``.
 
 One can also add properties to the class to indicate some useful
 information of the dataset. In :class:`~dgl.data.QM7bDataset`, one can add a property
-``num_labels`` to indicate the total number of prediction tasks in this
+``num_tasks`` to indicate the total number of prediction tasks in this
 multi-task dataset:
 
-.. code:: 
+.. code::
 
     @property
-    def num_labels(self):
+    def num_tasks(self):
         """Number of labels for each graph, i.e. number of prediction tasks."""
         return 14
 
 After all these coding, one can finally use :class:`~dgl.data.QM7bDataset` as
 follows:
 
-.. code:: 
+.. code::
 
     import dgl
     import torch
 
     from dgl.dataloading import GraphDataLoader
-    
+
     # load data
     dataset = QM7bDataset()
-    num_labels = dataset.num_labels
-    
+    num_tasks = dataset.num_tasks
+
     # create dataloaders
     dataloader = GraphDataLoader(dataset, batch_size=1, shuffle=True)
-    
+
     # training
     for epoch in range(100):
         for g, labels in dataloader:
@@ -115,7 +115,7 @@ A complete guide for training graph classification models can be found
 in :ref:`guide-training-graph-classification`.
 
 For more examples of graph classification datasets, please refer to DGL's builtin graph classification
-datasets: 
+datasets:
 
 * :ref:`gindataset`
 
@@ -140,18 +140,18 @@ computation and analysis conducted on the graph. DGL provides an API called
 :func:`dgl.reorder_graph` for this purpose. Please refer to ``process()``
 part in below example for more details.
 
-.. code:: 
+.. code::
 
     from dgl.data import DGLBuiltinDataset
     from dgl.data.utils import _get_dgl_url
-    
+
     class CitationGraphDataset(DGLBuiltinDataset):
         _urls = {
             'cora_v2' : 'dataset/cora_v2.zip',
             'citeseer' : 'dataset/citeseer.zip',
             'pubmed' : 'dataset/pubmed.zip',
         }
-    
+
         def __init__(self, name, raw_dir=None, force_reload=False, verbose=True):
             assert name.lower() in ['cora', 'citeseer', 'pubmed']
             if name.lower() == 'cora':
@@ -162,11 +162,11 @@ part in below example for more details.
                                                        raw_dir=raw_dir,
                                                        force_reload=force_reload,
                                                        verbose=verbose)
-    
+
         def process(self):
             # Skip some processing code
             # === data processing skipped ===
-    
+
             # build graph
             g = dgl.graph(graph)
             # splitting masks
@@ -178,15 +178,15 @@ part in below example for more details.
             # node features
             g.ndata['feat'] = torch.tensor(_preprocess_features(features),
                                            dtype=F.data_type_dict['float32'])
-            self._num_labels = onehot_labels.shape[1]
+            self._num_tasks = onehot_labels.shape[1]
             self._labels = labels
             # reorder graph to obtain better locality.
             self._g = dgl.reorder_graph(g)
-    
+
         def __getitem__(self, idx):
             assert idx == 0, "This dataset has only one graph"
             return self._g
-    
+
         def __len__(self):
             return 1
 
@@ -204,20 +204,20 @@ and TensorFlow, and ``float tensors`` in MXNet.
 The section uses a subclass of ``CitationGraphDataset``, :class:`dgl.data.CiteseerGraphDataset`,
 to show the usage of it:
 
-.. code:: 
+.. code::
 
     # load data
     dataset = CiteseerGraphDataset(raw_dir='')
     graph = dataset[0]
-    
+
     # get split masks
     train_mask = graph.ndata['train_mask']
     val_mask = graph.ndata['val_mask']
     test_mask = graph.ndata['test_mask']
-    
+
     # get node features
     feats = graph.ndata['feat']
-    
+
     # get labels
     labels = graph.ndata['label']
 
@@ -258,7 +258,7 @@ The section uses builtin dataset
 as an example, and still skips the detailed data processing code to
 highlight the key part for processing link prediction datasets:
 
-.. code:: 
+.. code::
 
     # Example for creating Link Prediction datasets
     class KnowledgeGraphDataset(DGLBuiltinDataset):
@@ -271,11 +271,11 @@ highlight the key part for processing link prediction datasets:
                                                         raw_dir=raw_dir,
                                                         force_reload=force_reload,
                                                         verbose=verbose)
-    
+
         def process(self):
             # Skip some processing code
             # === data processing skipped ===
-    
+
             # splitting mask
             g.edata['train_mask'] = train_mask
             g.edata['val_mask'] = val_mask
@@ -285,11 +285,11 @@ highlight the key part for processing link prediction datasets:
             # node type
             g.ndata['ntype'] = ntype
             self._g = g
-    
+
         def __getitem__(self, idx):
             assert idx == 0, "This dataset has only one graph"
             return self._g
-    
+
         def __len__(self):
             return 1
 
@@ -299,14 +299,14 @@ code <https://docs.dgl.ai/en/0.5.x/_modules/dgl/data/knowledge_graph.html#Knowle
 to see the complete code. The following code uses a subclass of ``KnowledgeGraphDataset``,
 :class:`dgl.data.FB15k237Dataset`, to show the usage of it:
 
-.. code:: 
+.. code::
 
     from dgl.data import FB15k237Dataset
 
     # load data
     dataset = FB15k237Dataset()
     graph = dataset[0]
-    
+
     # get training mask
     train_mask = graph.edata['train_mask']
     train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze()
@@ -319,7 +319,7 @@ A complete guide for training link prediction models can be found in
 :ref:`guide-training-link-prediction`.
 
 For more examples of link prediction datasets, please refer to DGL's
-builtin datasets: 
+builtin datasets:
 
 * :ref:`kgdata`