refine

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

@@ -18,7 +18,10 @@ Base Dataset Class
 .. autoclass:: DGLDataset
     :members: download, save, load, process, has_cache, __getitem__, __len__
 
-.. autoclass:: DGLCSVDataset
+CSV Dataset Class
+-----------------
+
+.. autoclass:: CSVDataset
 
 Node Prediction Datasets
 ---------------------------------------

docs/source/guide/data-loadcsv.rst

@@ -171,8 +171,8 @@ for edges:
     3,0,False,True,False,"0.9784264442230887, 0.22131880861864428, 0.3161154827254189"
     4,1,True,True,False,"0.23142237259162102, 0.8715767748481147, 0.19117861103555467"
 
-After loaded, the dataset has one graph. Node/edge features are stored in ```ndata`` and ``edata``
-with the same column names. The example demonstrates how to specify a vector-shaped feature --
+After loaded, the dataset has one graph. Node/edge features are stored in ``ndata`` and ``edata``
+with the same column names. The example demonstrates how to specify a vector-shaped feature
 using comma-separated list enclosed by double quotes ``"..."``.
 
 .. code:: python
@@ -378,18 +378,14 @@ After loaded, the dataset has multiple homographs with features and labels:
           ndata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)}
           edata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)})
     >>> print(data0)
-    {'feat': tensor([0.7426, 0.5197, 0.8149]), 'label': tensor([0])}
+    {'feat': tensor([0.7426, 0.5197, 0.8149], dtype=torch.float64), 'label': tensor(0)}
     >>> graph1, data1 = dataset[1]
     >>> print(graph1)
     Graph(num_nodes=5, num_edges=10,
           ndata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)}
           edata_schemes={'feat': Scheme(shape=(3,), dtype=torch.float64)})
     >>> print(data1)
-    {'feat': tensor([0.5348, 0.2864, 0.1155]), 'label': tensor([0])}
-
-.. note::
-
-    When there are multiple graphs, ``CSVDataset`` currently requires them to be homogeneous.
+    {'feat': tensor([0.5348, 0.2864, 0.1155], dtype=torch.float64), 'label': tensor(0)}
 
 
 Custom Data Parser
@@ -469,10 +465,11 @@ To parse the string type labels, one can define a ``DataParser`` class as follow
                 parsed[header] = dt
             return parsed
 
- Create a ``CSVDataset`` using the defined ``DataParser``:
+Create a ``CSVDataset`` using the defined ``DataParser``:
 
 .. code:: python
 
+    >>> import dgl
     >>> dataset = dgl.data.CSVDataset('./customized_parser_dataset',
     ...                               ndata_parser=MyDataParser(),
     ...                               edata_parser=MyDataParser())
@@ -483,7 +480,7 @@ To parse the string type labels, one can define a ``DataParser`` class as follow
 
 .. note::
 
-    To specify different ``DataParser`` s for different node/edge types, pass a dictionary to
+    To specify different ``DataParser``\s for different node/edge types, pass a dictionary to
     ``ndata_parser`` and ``edata_parser``, where the key is type name (a single string for
     node type; a string triplet for edge type) and the value is the ``DataParser`` to use.
 

python/dgl/data/csv_dataset.py

@@ -6,8 +6,7 @@
 
 
 class CSVDataset(DGLDataset):
-    """ This class aims to parse data from CSV files, construct DGLGraph
-        and behaves as a DGLDataset.
+    """Dataset class that loads and parses graph data from CSV files.
 
     Parameters
     ----------
@@ -52,11 +51,7 @@ class CSVDataset(DGLDataset):
 
     Examples
     --------
-    ``meta.yaml`` and CSV files are under ``csv_dir``.
-    
-    >>> csv_dataset = dgl.data.DGLCSVDataset(csv_dir)
-
-    See more details in :ref:`guide-data-pipeline-loadcsv`.
+    Please refer to :ref:`guide-data-pipeline-loadcsv`.
 
     """
     META_YAML_NAME = 'meta.yaml'

tutorials/blitz/6_load_data.py

@@ -226,12 +226,10 @@ def __len__(self):
 # Creating Dataset from CSV via :class:`~dgl.data.DGLCSVDataset`
 # ------------------------------------------------------------
 #
-# In the previous examples, dataset is created directly from raw CSV
-# files via :class:`~dgl.data.DGLDataset`. DGL provides utility class
-# :class:`~dgl.data.DGLCSVDataset` to read data from CSV files and
-# construct :class:`~dgl.DGLGraph` more flexibly. Please refer to
-# :ref:`guide-data-pipeline-loadcsv` and see if this utility is more
-# suitable for your case.
+# The previous examples describe how to create a dataset from CSV files
+# step-by-step. DGL also provides a utility class :class:`~dgl.data.CSVDataset`
+# for reading and parsing data from CSV files. See :ref:`guide-data-pipeline-loadcsv`
+# for more details.
 #