[Dataset] Fix the docstring format for dgl.data section (#1941)

* PPIDataset

* Revert "PPIDataset"

This reverts commit 264bd0c960cfa698a7bb946dad132bf52c2d0c8a.

* update data rst

* update data doc and docstring

* API doc rst for dataset

* docstring

* update api doc

* add url format

* update docstring

* update citation graph

* update knowledge graph

* update gc datasets

* fix index

* Rst fix (#3)

* Fix syntax

* syntax

* update docstring

* update doc (#4)

* final update

* fix rdflib

* fix rdf
Co-authored-by: HuXiangkun <huxk_hit@qq.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>

.gitignore

@@ -160,3 +160,4 @@ cscope.*
 
 config.cmake
 .ycm_extra_conf.py
+**.png

docs/source/api/python/data.rst

@@ -5,89 +5,125 @@ dgl.data
 
 .. currentmodule:: dgl.data
 
-Utils
------
+Dataset Classes
+---------------
 
-.. autosummary::
-    :toctree: ../../generated/
+DGL dataset
+```````````
 
-    utils.get_download_dir
-    utils.download
-    utils.check_sha1
-    utils.extract_archive
-    utils.split_dataset
-    utils.save_graphs
-    utils.load_graphs
-    utils.load_labels
+.. autoclass:: DGLDataset
+    :members: download, save, load, process, has_cache, __getitem__, __len__
 
-.. autoclass:: dgl.data.utils.Subset
-    :members: __getitem__, __len__
+DGL builtin dataset
+```````````````````
 
-Dataset Classes
----------------
+.. autoclass:: DGLBuiltinDataset
+    :members: download
 
 Stanford sentiment treebank dataset
 ```````````````````````````````````
 
 For more information about the dataset, see `Sentiment Analysis <https://nlp.stanford.edu/sentiment/index.html>`__.
 
-.. autoclass:: SST
+.. autoclass:: SSTDataset
     :members: __getitem__, __len__
 
 
-Karate Club dataset
+Karate club dataset
 ```````````````````````````````````
 
-.. autoclass:: KarateClub
+.. autoclass:: KarateClubDataset
     :members: __getitem__, __len__
 
 
-Citation Network dataset
+Citation network dataset
 ```````````````````````````````````
 
-.. autoclass:: CitationGraphDataset
+.. autoclass:: CoraGraphDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: CiteseerGraphDataset
     :members: __getitem__, __len__
 
+.. autoclass:: PubmedGraphDataset
+    :members: __getitem__, __len__
+
+
+Knowlege graph dataset
+```````````````````````````````````
+
+.. autoclass:: FB15k237Dataset
+    :members: __getitem__, __len__
+
+.. autoclass:: FB15kDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: WN18Dataset
+    :members: __getitem__, __len__
+
+
+RDF datasets
+```````````````````````````````````
+
+.. autoclass:: AIFBDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: MUTAGDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: BGSDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: AMDataset
+    :members: __getitem__, __len__
+
+
 
 CoraFull dataset
 ```````````````````````````````````
 
-.. autoclass:: CoraFull
+.. autoclass:: CoraFullDataset
     :members: __getitem__, __len__
 
 
 Amazon Co-Purchase dataset
 ```````````````````````````````````
 
-.. autoclass:: AmazonCoBuy
+.. autoclass:: AmazonCoBuyComputerDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: AmazonCoBuyPhotoDataset
     :members: __getitem__, __len__
 
 
 Coauthor dataset
 ```````````````````````````````````
 
-.. autoclass:: Coauthor
+.. autoclass:: CoauthorCSDataset
+    :members: __getitem__, __len__
+
+.. autoclass:: CoauthorPhysicsDataset
     :members: __getitem__, __len__
 
 
 BitcoinOTC dataset
 ```````````````````````````````````
 
-.. autoclass:: BitcoinOTC
+.. autoclass:: BitcoinOTCDataset
     :members: __getitem__, __len__
 
 
 ICEWS18 dataset
 ```````````````````````````````````
 
-.. autoclass:: ICEWS18
+.. autoclass:: ICEWS18Dataset
     :members: __getitem__, __len__
 
 
 QM7b dataset
 ```````````````````````````````````
 
-.. autoclass:: QM7b
+.. autoclass:: QM7bDataset
     :members: __getitem__, __len__
 
 
@@ -95,7 +131,7 @@ QM7b dataset
 GDELT dataset
 ```````````````````````````````````
 
-.. autoclass:: GDELT
+.. autoclass:: GDELTDataset
     :members: __getitem__, __len__
 
 
@@ -103,17 +139,16 @@ Mini graph classification dataset
 `````````````````````````````````
 
 .. autoclass:: MiniGCDataset
-    :members: __getitem__, __len__, num_classes
-
-
-Graph kernel dataset
-````````````````````
+    :members: __getitem__, __len__
 
-For more information about the dataset, see `Benchmark Data Sets for Graph Kernels <https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets>`__.
+TU dataset
+``````````
 
 .. autoclass:: TUDataset
     :members: __getitem__, __len__
 
+.. autoclass:: LegacyTUDataset
+    :members: __getitem__, __len__
 
 Graph isomorphism network dataset
 ```````````````````````````````````
@@ -129,3 +164,36 @@ Protein-Protein Interaction dataset
 
 .. autoclass:: PPIDataset
     :members: __getitem__, __len__
+
+
+Reddit dataset
+``````````````
+
+.. autoclass:: RedditDataset
+    :members: __getitem__, __len__
+
+
+Symmetric Stochastic Block Model Mixture dataset
+````````````````````````````````````````````````
+
+.. autoclass:: SBMMixtureDataset
+    :members: __getitem__, __len__, collate_fn
+
+Utils
+-----
+
+.. autosummary::
+    :toctree: ../../generated/
+
+    utils.get_download_dir
+    utils.download
+    utils.check_sha1
+    utils.extract_archive
+    utils.split_dataset
+    utils.save_graphs
+    utils.load_graphs
+    utils.load_labels
+
+.. autoclass:: dgl.data.utils.Subset
+    :members: __getitem__, __len__
+

python/dgl/data/__init__.py

@@ -20,6 +20,8 @@ from .icews18 import ICEWS18, ICEWS18Dataset
 from .qm7b import QM7b, QM7bDataset
 from .dgl_dataset import DGLDataset, DGLBuiltinDataset
 from .citation_graph import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
+from .knowledge_graph import FB15k237Dataset, FB15kDataset, WN18Dataset
+from .rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
 
 
 def register_data_args(parser):

python/dgl/data/bitcoinotc.py

@@ -18,13 +18,15 @@ class BitcoinOTCDataset(DGLBuiltinDataset):
     a platform called Bitcoin OTC. Since Bitcoin users are anonymous,
     there is a need to maintain a record of users' reputation to prevent
     transactions with fraudulent and risky users.
-    Offical website: https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html
+
+    Offical website: `<https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html>`_
 
     Bitcoin OTC dataset statistics:
-    Nodes: 5,881
-    Edges: 35,592
-    Range of edge weight: -10 to +10
-    Percentage of positive edges: 89%
+
+    - Nodes: 5,881
+    - Edges: 35,592
+    - Range of edge weight: -10 to +10
+    - Percentage of positive edges: 89%
 
     Parameters
     ----------
@@ -117,7 +119,12 @@ class BitcoinOTCDataset(DGLBuiltinDataset):
         return self._graphs
 
     def __len__(self):
-        r""" Number of graphs in the dataset """
+        r""" Number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return len(self.graphs)
 
     def __getitem__(self, item):
@@ -130,9 +137,11 @@ class BitcoinOTCDataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
-            The graph contains the graph structure and edge weights
-            - edata['h'] : edge weights
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``edata['h']`` : edge weights
         """
         return self.graphs[item]
 

python/dgl/data/citation_graph.py

@@ -273,26 +273,38 @@ class CoraGraphDataset(CitationGraphDataset):
     r""" Cora citation network dataset.
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        - ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = CoraGraphDataset()
             >>> graph = dataset[0]
-        `train_mask` is deprecated, it is replaced by:
+
+        - ``train_mask`` is deprecated, it is replaced by:
+
             >>> dataset = CoraGraphDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.ndata['train_mask']
-        `val_mask` is deprecated, it is replaced by:
+
+        - ``val_mask`` is deprecated, it is replaced by:
+
             >>> dataset = CoraGraphDataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.ndata['val_mask']
-        `test_mask` is deprecated, it is replaced by:
+
+        - ``test_mask`` is deprecated, it is replaced by:
+
             >>> dataset = CoraGraphDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.ndata['test_mask']
-        `labels` is deprecated, it is replaced by:
+
+        - ``labels`` is deprecated, it is replaced by:
+
             >>> dataset = CoraGraphDataset()
             >>> graph = dataset[0]
             >>> labels = graph.ndata['label']
-        `feat` is deprecated, it is replaced by:
+
+        - ``feat`` is deprecated, it is replaced by:
+
             >>> dataset = CoraGraphDataset()
             >>> graph = dataset[0]
             >>> feat = graph.ndata['feat']
@@ -304,12 +316,16 @@ class CoraGraphDataset(CitationGraphDataset):
     The task is to predict the category of
     certain paper.
 
-    Statistics
-    ----------
-    Nodes: 2708
-    Edges: 10556
-    Number of Classes: 7
-    Label Split: Train: 140 ,Valid: 500, Test: 1000
+    Statistics:
+
+    - Nodes: 2708
+    - Edges: 10556
+    - Number of Classes: 7
+    - Label split:
+
+        - Train: 140 
+        - Valid: 500
+        - Test: 1000
 
     Parameters
     ----------
@@ -319,7 +335,7 @@ class CoraGraphDataset(CitationGraphDataset):
     force_reload : bool
         Whether to reload the dataset. Default: False
     verbose: bool
-      Whether to print out progress information. Default: True.
+        Whether to print out progress information. Default: True.
 
     Attributes
     ----------
@@ -327,13 +343,13 @@ class CoraGraphDataset(CitationGraphDataset):
         Number of label classes
     graph: networkx.DiGraph
         Graph structure
-    train_mask: Numpy array
+    train_mask: numpy.ndarray
         Mask of training nodes
-    val_mask: Numpy array
+    val_mask: numpy.ndarray
         Mask of validation nodes
-    test_mask: Numpy array
+    test_mask: numpy.ndarray
         Mask of test nodes
-    labels: Numpy array
+    labels: numpy.ndarray
         Ground truth labels of each node
     features: Tensor
         Node features
@@ -377,13 +393,15 @@ class CoraGraphDataset(CitationGraphDataset):
 
         Return
         ------
-        dgl.DGLGraph
+        :class:`dgl.DGLGraph`
+
             graph structure, node features and labels.
-            - ndata['train_mask']： mask for training node set
-            - ndata['val_mask']: mask for validation node set
-            - ndata['test_mask']: mask for test node set
-            - ndata['feat']: node feature
-            - ndata['label']: ground truth labels
+
+            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['val_mask']``: mask for validation node set
+            - ``ndata['test_mask']``: mask for test node set
+            - ``ndata['feat']``: node feature
+            - ``ndata['label']``: ground truth labels
         """
         return super(CoraGraphDataset, self).__getitem__(idx)
 
@@ -395,26 +413,38 @@ class CiteseerGraphDataset(CitationGraphDataset):
     r""" Citeseer citation network dataset.
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = CiteseerGraphDataset()
             >>> graph = dataset[0]
-        `train_mask` is deprecated, it is replaced by:
+
+        ``train_mask`` is deprecated, it is replaced by:
+
             >>> dataset = CiteseerGraphDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.ndata['train_mask']
-        `val_mask` is deprecated, it is replaced by:
+
+        ``val_mask`` is deprecated, it is replaced by:
+
             >>> dataset = CiteseerGraphDataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.ndata['val_mask']
-        `test_mask` is deprecated, it is replaced by:
+
+        ``test_mask`` is deprecated, it is replaced by:
+
             >>> dataset = CiteseerGraphDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.ndata['test_mask']
-        `labels` is deprecated, it is replaced by:
+
+        ``labels`` is deprecated, it is replaced by:
+
             >>> dataset = CiteseerGraphDataset()
             >>> graph = dataset[0]
             >>> labels = graph.ndata['label']
-        `feat` is deprecated, it is replaced by:
+
+        ``feat`` is deprecated, it is replaced by:
+
             >>> dataset = CiteseerGraphDataset()
             >>> graph = dataset[0]
             >>> feat = graph.ndata['feat']
@@ -426,12 +456,16 @@ class CiteseerGraphDataset(CitationGraphDataset):
     task. The task is to predict the category of
     certain publication.
 
-    Statistics
-    ----------
-    Nodes: 3327
-    Edges: 9228
-    Number of Classes: 6
-    Label Split: Train: 120 ,Valid: 500, Test: 1000
+    Statistics:
+
+    - Nodes: 3327
+    - Edges: 9228
+    - Number of Classes: 6
+    - Label Split:
+
+        - Train: 120
+        - Valid: 500
+        - Test: 1000
 
     Parameters
     -----------
@@ -441,7 +475,7 @@ class CiteseerGraphDataset(CitationGraphDataset):
     force_reload : bool
         Whether to reload the dataset. Default: False
     verbose: bool
-      Whether to print out progress information. Default: True.
+        Whether to print out progress information. Default: True.
 
     Attributes
     ----------
@@ -449,13 +483,13 @@ class CiteseerGraphDataset(CitationGraphDataset):
         Number of label classes
     graph: networkx.DiGraph
         Graph structure
-    train_mask: Numpy array
+    train_mask: numpy.ndarray
         Mask of training nodes
-    val_mask: Numpy array
+    val_mask: numpy.ndarray
         Mask of validation nodes
-    test_mask: Numpy array
+    test_mask: numpy.ndarray
         Mask of test nodes
-    labels: Numpy array
+    labels: numpy.ndarray
         Ground truth labels of each node
     features: Tensor
         Node features
@@ -502,13 +536,15 @@ class CiteseerGraphDataset(CitationGraphDataset):
 
         Return
         ------
-        dgl.DGLGraph
+        :class:`dgl.DGLGraph`
+
             graph structure, node features and labels.
-            - ndata['train_mask']： mask for training node set
-            - ndata['val_mask']: mask for validation node set
-            - ndata['test_mask']: mask for test node set
-            - ndata['feat']: node feature
-            - ndata['label']: ground truth labels
+
+            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['val_mask']``: mask for validation node set
+            - ``ndata['test_mask']``: mask for test node set
+            - ``ndata['feat']``: node feature
+            - ``ndata['label']``: ground truth labels
         """
         return super(CiteseerGraphDataset, self).__getitem__(idx)
 
@@ -520,26 +556,38 @@ class PubmedGraphDataset(CitationGraphDataset):
     r""" Pubmed citation network dataset.
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = PubmedGraphDataset()
             >>> graph = dataset[0]
-        `train_mask` is deprecated, it is replaced by:
+
+        ``train_mask`` is deprecated, it is replaced by:
+
             >>> dataset = PubmedGraphDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.ndata['train_mask']
-        `val_mask` is deprecated, it is replaced by:
+
+        ``val_mask`` is deprecated, it is replaced by:
+
             >>> dataset = PubmedGraphDataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.ndata['val_mask']
-        `test_mask` is deprecated, it is replaced by:
+
+        ``test_mask`` is deprecated, it is replaced by:
+
             >>> dataset = PubmedGraphDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.ndata['test_mask']
-        `labels` is deprecated, it is replaced by:
+
+        ``labels`` is deprecated, it is replaced by:
+
             >>> dataset = PubmedGraphDataset()
             >>> graph = dataset[0]
             >>> labels = graph.ndata['label']
-        `feat` is deprecated, it is replaced by:
+
+        ``feat`` is deprecated, it is replaced by:
+
             >>> dataset = PubmedGraphDataset()
             >>> graph = dataset[0]
             >>> feat = graph.ndata['feat']
@@ -551,12 +599,16 @@ class PubmedGraphDataset(CitationGraphDataset):
     task. The task is to predict the category of
     certain publication.
 
-    Statistics
-    ----------
-    Nodes: 19717
-    Edges: 88651
-    Number of Classes: 3
-    Label Split: Train: 60 ,Valid: 500, Test: 1000
+    Statistics:
+
+    - Nodes: 19717
+    - Edges: 88651
+    - Number of Classes: 3
+    - Label Split:
+
+        - Train: 60
+        - Valid: 500
+        - Test: 1000
 
     Parameters
     -----------
@@ -566,7 +618,7 @@ class PubmedGraphDataset(CitationGraphDataset):
     force_reload : bool
         Whether to reload the dataset. Default: False
     verbose: bool
-      Whether to print out progress information. Default: True.
+        Whether to print out progress information. Default: True.
 
     Attributes
     ----------
@@ -574,13 +626,13 @@ class PubmedGraphDataset(CitationGraphDataset):
         Number of label classes
     graph: networkx.DiGraph
         Graph structure
-    train_mask: Numpy array
+    train_mask: numpy.ndarray
         Mask of training nodes
-    val_mask: Numpy array
+    val_mask: numpy.ndarray
         Mask of validation nodes
-    test_mask: Numpy array
+    test_mask: numpy.ndarray
         Mask of test nodes
-    labels: Numpy array
+    labels: numpy.ndarray
         Ground truth labels of each node
     features: Tensor
         Node features
@@ -624,13 +676,15 @@ class PubmedGraphDataset(CitationGraphDataset):
 
         Return
         ------
-        dgl.DGLGraph
+        :class:`dgl.DGLGraph`
+
             graph structure, node features and labels.
-            - ndata['train_mask']： mask for training node set
-            - ndata['val_mask']: mask for validation node set
-            - ndata['test_mask']: mask for test node set
-            - ndata['feat']: node feature
-            - ndata['label']: ground truth labels
+
+            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['val_mask']``: mask for validation node set
+            - ``ndata['test_mask']``: mask for test node set
+            - ``ndata['feat']``: node feature
+            - ``ndata['label']``: ground truth labels
         """
         return super(PubmedGraphDataset, self).__getitem__(idx)
 

python/dgl/data/dgl_dataset.py

@@ -14,13 +14,13 @@ class DGLDataset(object):
     The following steps will are executed automatically:
 
       1. Check whether there is a dataset cache on disk
-      (already processed and stored on the disk) by
-      invoking ``has_cache()``. If true, goto 5.
+         (already processed and stored on the disk) by
+         invoking ``has_cache()``. If true, goto 5.
       2. Call ``download()`` to download the data.
       3. Call ``process()`` to process the data.
       4. Call ``save()`` to save the processed dataset on disk and goto 6.
       5. Call ``load()`` to load the processed dataset from disk.
-      6. Done
+      6. Done.
 
     Users can overwite these functions with their
     own data processing logic.
@@ -43,11 +43,31 @@ class DGLDataset(object):
         A tuple of values as the input for the hash function.
         Users can distinguish instances (and their caches on the disk)
         from the same dataset class by comparing the hash values.
-        Default: (), the corresponding hash value is 'f9065fa7'.
+        Default: (), the corresponding hash value is ``'f9065fa7'``.
     force_reload : bool
         Whether to reload the dataset. Default: False
     verbose : bool
         Whether to print out progress information
+
+    Attributes
+    ----------
+    url : str
+        The URL to download the dataset
+    name : str
+        The dataset name
+    raw_dir : str
+        Raw file directory contains the input data folder
+    raw_path : str
+        Directory contains the input data files.
+        Default : ``os.path.join(self.raw_dir, self.name)``
+    save_dir : str
+        Directory to save the processed dataset
+    save_path : str
+        Filr path to save the processed dataset
+    verbose : bool
+        Whether to print information
+    hash : str
+        Hash value for the dataset and the setting.
     """
     def __init__(self, name, url=None, raw_dir=None, save_dir=None,
                  hash_key=(), force_reload=False, verbose=False):
@@ -75,7 +95,7 @@ class DGLDataset(object):
 
         It is recommended to download the to the :obj:`self.raw_dir`
         folder. Can be ignored if the dataset is
-        already in self.raw_dir
+        already in :obj:`self.raw_dir`.
         """
         pass
 
@@ -83,9 +103,9 @@ class DGLDataset(object):
         r"""Overwite to realize your own logic of
         saving the processed dataset into files.
 
-        It is recommended to use dgl.utils.data.save_graphs
+        It is recommended to use ``dgl.utils.data.save_graphs``
         to save dgl graph into files and use
-        dgl.utils.data.save_info to save extra
+        ``dgl.utils.data.save_info`` to save extra
         information into files.
         """
         pass
@@ -94,9 +114,9 @@ class DGLDataset(object):
         r"""Overwite to realize your own logic of
         loading the saved dataset from files.
 
-        It is recommended to use dgl.utils.data.load_graphs
+        It is recommended to use ``dgl.utils.data.load_graphs``
         to load dgl graph from files and use
-        dgl.utils.data.load_info to load extra information
+        ``dgl.utils.data.load_info`` to load extra information
         into python dict object.
         """
         pass
@@ -116,9 +136,9 @@ class DGLDataset(object):
 
     @retry_method_with_fix(download)
     def _download(self):
-        r"""Download dataset by calling self.download() if the dataset does not exists under self.raw_path.
-            By default self.raw_path = os.path.join(self.raw_dir, self.name)
-            One can overwrite raw_path() function to change the path.
+        r"""Download dataset by calling ``self.download()`` if the dataset does not exists under ``self.raw_path``.
+            By default ``self.raw_path = os.path.join(self.raw_dir, self.name)``
+            One can overwrite ``raw_path()`` function to change the path.
         """
         if os.path.exists(self.raw_path):  # pragma: no cover
             return
@@ -185,13 +205,13 @@ class DGLDataset(object):
 
     @property
     def raw_dir(self):
-        r"""Raw file directory contains the input data directory.
+        r"""Raw file directory contains the input data folder.
         """
         return self._raw_dir
 
     @property
     def raw_path(self):
-        r"""File directory contains the input data.
+        r"""Directory contains the input data files.
             By default raw_path = os.path.join(self.raw_dir, self.name)
         """
         return os.path.join(self.raw_dir, self.name)
@@ -216,7 +236,7 @@ class DGLDataset(object):
 
     @property
     def hash(self):
-        r"""Hash value for the dataset.
+        r"""Hash value for the dataset and the setting.
         """
         return self._hash
 
@@ -235,6 +255,7 @@ class DGLBuiltinDataset(DGLDataset):
     r"""The Basic DGL Builtin Dataset.
 
     Parameters
+    ----------
     name : str
         Name of the dataset.
     url : str

python/dgl/data/gdelt.py

@@ -18,16 +18,15 @@ class GDELTDataset(DGLBuiltinDataset):
     (15 minutes time granularity).
 
     Reference:
-        - `Recurrent Event Network for Reasoning over Temporal
-            Knowledge Graphs` <https://arxiv.org/abs/1904.05530>
-        - `The Global Database of Events, Language, and Tone (GDELT) `
-       <https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/28075>
 
-    Statistics
-    ----------
-    Train examples: 2,304
-    Valid examples: 288
-    Test examples: 384
+        - `Recurrent Event Network for Reasoning over Temporal Knowledge Graphs <https://arxiv.org/abs/1904.05530>`_
+        - `The Global Database of Events, Language, and Tone (GDELT) <https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/28075>`_
+
+    Statistics:
+
+    - Train examples: 2,304
+    - Valid examples: 288
+    - Test examples: 384
 
     Parameters
     ----------
@@ -135,9 +134,11 @@ class GDELTDataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
-            graph structure and edge feature
-            - edata['rel_type']: edge type
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``edata['rel_type']``: edge type
         """
         if t >= len(self) or t < 0:
             raise IndexError("Index out of range")
@@ -150,7 +151,12 @@ class GDELTDataset(DGLBuiltinDataset):
         return g
 
     def __len__(self):
-        r"""Number of graphs in the dataset"""
+        r"""Number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return self._end_time - self._start_time + 1
 
     @property

python/dgl/data/gindt.py

@@ -17,21 +17,19 @@ from ..convert import graph as dgl_graph
 
 class GINDataset(DGLBuiltinDataset):
     """Datasets for Graph Isomorphism Network (GIN)
-    Adapted from https://github.com/weihua916/powerful-gnns/blob/master/dataset.zip.
+    Adapted from `<https://github.com/weihua916/powerful-gnns/blob/master/dataset.zip>`_.
 
-    The dataset contains the compact format of popular graph kernel datasets, which includes:
-    MUTAG, COLLAB, IMDBBINARY, IMDBMULTI, NCI1, PROTEINS, PTC, REDDITBINARY, REDDITMULTI5K
-    This datset class processes all data sets listed above. For more graph kernel datasets,
-    see :class:`TUDataset`.
+    The dataset contains the compact format of popular graph kernel datasets.
+    For more graph kernel datasets, see :class:`TUDataset`.
 
-    Paramters
+    Parameters
     ---------
     name: str
-        dataset name, one of below -
-        ('MUTAG', 'COLLAB', \
-        'IMDBBINARY', 'IMDBMULTI', \
-        'NCI1', 'PROTEINS', 'PTC', \
-        'REDDITBINARY', 'REDDITMULTI5K')
+        dataset name, one of
+        (``'MUTAG'``, ``'COLLAB'``, \
+        ``'IMDBBINARY'``, ``'IMDBMULTI'``, \
+        ``'NCI1'``, ``'PROTEINS'``, ``'PTC'``, \
+        ``'REDDITBINARY'``, ``'REDDITMULTI5K'``)
     self_loop: bool
         add self to self edge if true
     degree_as_nlabel: bool
@@ -41,7 +39,7 @@ class GINDataset(DGLBuiltinDataset):
     --------
     >>> data = GINDataset(name='MUTAG', self_loop=False)
 
-    **The dataset instance is an iterable**
+    The dataset instance is an iterable
 
     >>> len(data)
     188
@@ -53,7 +51,7 @@ class GINDataset(DGLBuiltinDataset):
     >>> label
     tensor(1)
 
-    **Batch the graphs and labels for mini-batch training**
+    Batch the graphs and labels for mini-batch training
 
     >>> graphs, labels = zip(*[data[i] for i in range(16)])
     >>> batched_graphs = dgl.batch(graphs)
@@ -118,14 +116,14 @@ class GINDataset(DGLBuiltinDataset):
     def __getitem__(self, idx):
         """Get the idx-th sample.
 
-        Paramters
+        Parameters
         ---------
         idx : int
             The sample index.
 
         Returns
         -------
-        (dgl.Graph, int)
+        (:class:`dgl.Graph`, Tensor)
             The graph and its label.
         """
         return self.graphs[idx], self.labels[idx]

python/dgl/data/gnn_benckmark.py

@@ -118,10 +118,12 @@ class GNNBenchmarkDataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
-            graph structure, node features and node labels
-            - ndata['feat']: node features
-            - ndata['label']: node labels
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['feat']``: node features
+            - ``ndata['label']``: node labels
         """
         assert idx == 0, "This dataset has only one graph"
         return self._graph
@@ -135,7 +137,9 @@ class CoraFullDataset(GNNBenchmarkDataset):
     r"""CORA-Full dataset for node classification task.
 
     .. deprecated:: 0.5.0
-        `data` is deprecated, it is repalced by:
+
+        - ``data`` is deprecated, it is repalced by:
+
         >>> dataset = CoraFullDataset()
         >>> graph = dataset[0]
 
@@ -143,14 +147,14 @@ class CoraFullDataset(GNNBenchmarkDataset):
     Unsupervised Inductive Learning via Ranking`.
     Nodes represent paper and edges represent citations.
 
-    Reference: https://github.com/shchur/gnn-benchmark#datasets
+    Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
 
-    Statistics
-    ----------
-    Nodes: 19,793
-    Edges: 130,622
-    Number of Classes: 70
-    Node feature size: 8,710
+    Statistics:
+
+    - Nodes: 19,793
+    - Edges: 130,622
+    - Number of Classes: 70
+    - Node feature size: 8,710
 
     Parameters
     ----------
@@ -185,7 +189,12 @@ class CoraFullDataset(GNNBenchmarkDataset):
 
     @property
     def num_classes(self):
-        """Number of classes."""
+        """Number of classes.
+
+        Return
+        -------
+        int
+        """
         return 70
 
 
@@ -193,7 +202,9 @@ class CoauthorCSDataset(GNNBenchmarkDataset):
     r""" 'Computer Science (CS)' part of the Coauthor dataset for node classification task.
 
     .. deprecated:: 0.5.0
-        `data` is deprecated, it is repalced by:
+
+        - ``data`` is deprecated, it is repalced by:
+
         >>> dataset = CoauthorCSDataset()
         >>> graph = dataset[0]
 
@@ -202,14 +213,14 @@ class CoauthorCSDataset(GNNBenchmarkDataset):
     co-authored a paper; node features represent paper keywords for each author’s papers, and class
     labels indicate most active fields of study for each author.
 
-    Reference: https://github.com/shchur/gnn-benchmark#datasets
+    Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
 
-    Statistics
-    ----------
-    Nodes: 18,333
-    Edges: 327,576
-    Number of classes: 15
-    Node feature size: 6,805
+    Statistics:
+
+    - Nodes: 18,333
+    - Edges: 327,576
+    - Number of classes: 15
+    - Node feature size: 6,805
 
     Parameters
     ----------
@@ -244,7 +255,12 @@ class CoauthorCSDataset(GNNBenchmarkDataset):
 
     @property
     def num_classes(self):
-        """Number of classes."""
+        """Number of classes.
+
+        Return
+        -------
+        int
+        """
         return 15
 
 
@@ -252,7 +268,9 @@ class CoauthorPhysicsDataset(GNNBenchmarkDataset):
     r""" 'Physics' part of the Coauthor dataset for node classification task.
 
     .. deprecated:: 0.5.0
-        `data` is deprecated, it is repalced by:
+
+        - ``data`` is deprecated, it is repalced by:
+
         >>> dataset = CoauthorPhysicsDataset()
         >>> graph = dataset[0]
 
@@ -261,14 +279,14 @@ class CoauthorPhysicsDataset(GNNBenchmarkDataset):
     co-authored a paper; node features represent paper keywords for each author’s papers, and class
     labels indicate most active fields of study for each author.
 
-    Reference: https://github.com/shchur/gnn-benchmark#datasets
+    Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
 
     Statistics
-    ----------
-    Nodes: 34,493
-    Edges: 991,848
-    Number of classes: 5
-    Node feature size: 8,415
+
+    - Nodes: 34,493
+    - Edges: 991,848
+    - Number of classes: 5
+    - Node feature size: 8,415
 
     Parameters
     ----------
@@ -303,7 +321,12 @@ class CoauthorPhysicsDataset(GNNBenchmarkDataset):
 
     @property
     def num_classes(self):
-        """Number of classes."""
+        """Number of classes.
+
+        Return
+        -------
+        int
+        """
         return 5
 
 
@@ -311,7 +334,9 @@ class AmazonCoBuyComputerDataset(GNNBenchmarkDataset):
     r""" 'Computer' part of the AmazonCoBuy dataset for node classification task.
 
     .. deprecated:: 0.5.0
-        `data` is deprecated, it is repalced by:
+
+        - ``data`` is deprecated, it is repalced by:
+
         >>> dataset = AmazonCoBuyComputerDataset()
         >>> graph = dataset[0]
 
@@ -319,14 +344,14 @@ class AmazonCoBuyComputerDataset(GNNBenchmarkDataset):
     where nodes represent goods, edges indicate that two goods are frequently bought together, node
     features are bag-of-words encoded product reviews, and class labels are given by the product category.
 
-    Reference: https://github.com/shchur/gnn-benchmark#datasets
+    Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
 
-    Statistics
-    ----------
-    Nodes: 13,752
-    Edges: 574,418
-    Number of classes: 5
-    Node feature size: 767
+    Statistics:
+
+    - Nodes: 13,752
+    - Edges: 574,418
+    - Number of classes: 5
+    - Node feature size: 767
 
     Parameters
     ----------
@@ -361,7 +386,12 @@ class AmazonCoBuyComputerDataset(GNNBenchmarkDataset):
 
     @property
     def num_classes(self):
-        """Number of classes."""
+        """Number of classes.
+
+        Return
+        -------
+        int
+        """
         return 5
 
 
@@ -369,7 +399,9 @@ class AmazonCoBuyPhotoDataset(GNNBenchmarkDataset):
     r"""AmazonCoBuy dataset for node classification task.
 
     .. deprecated:: 0.5.0
-        `data` is deprecated, it is repalced by:
+
+        - ``data`` is deprecated, it is repalced by:
+
         >>> dataset = AmazonCoBuyPhotoDataset()
         >>> graph = dataset[0]
 
@@ -377,14 +409,14 @@ class AmazonCoBuyPhotoDataset(GNNBenchmarkDataset):
     where nodes represent goods, edges indicate that two goods are frequently bought together, node
     features are bag-of-words encoded product reviews, and class labels are given by the product category.
 
-    Reference: https://github.com/shchur/gnn-benchmark#datasets
+    Reference: `<https://github.com/shchur/gnn-benchmark#datasets>`_
 
     Statistics
-    ----------
-    Nodes: 7,650
-    Edges: 287,326
-    Number of classes: 5
-    Node feature size: 745
+
+    - Nodes: 7,650
+    - Edges: 287,326
+    - Number of classes: 5
+    - Node feature size: 745
 
     Parameters
     ----------
@@ -419,7 +451,12 @@ class AmazonCoBuyPhotoDataset(GNNBenchmarkDataset):
 
     @property
     def num_classes(self):
-        """Number of classes."""
+        """Number of classes.
+
+        Return
+        -------
+        int
+        """
         return 5
 
 

python/dgl/data/icews18.py

@@ -12,22 +12,23 @@ class ICEWS18Dataset(DGLBuiltinDataset):
     r""" ICEWS18 dataset for temporal graph
 
     Integrated Crisis Early Warning System (ICEWS18)
+
     Event data consists of coded interactions between socio-political
     actors (i.e., cooperative or hostile actions between individuals,
     groups, sectors and nation states). This Dataset consists of events
     from 1/1/2018 to 10/31/2018 (24 hours time granularity).
+
     Reference:
-        - `Recurrent Event Network for Reasoning over Temporal
-        Knowledge Graphs <https://arxiv.org/abs/1904.05530>`_
-        - `ICEWS Coded Event Data
-           <https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/28075>`_
 
-    Statistics
-    ----------
-    Train examples: 240
-    Valid examples: 30
-    Test examples: 34
-    Nodes per graph: 23033
+        - `Recurrent Event Network for Reasoning over Temporal Knowledge Graphs <https://arxiv.org/abs/1904.05530>`_
+        - `ICEWS Coded Event Data <https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/28075>`_
+
+    Statistics：
+
+    - Train examples: 240
+    - Valid examples: 30
+    - Test examples: 34
+    - Nodes per graph: 23033
 
     Parameters
     ----------
@@ -111,14 +112,21 @@ class ICEWS18Dataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
-            graph structure and edge feature
-            - edata['rel_type']: edge type
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``edata['rel_type']``: edge type
         """
         return self._graphs[idx]
 
     def __len__(self):
-        r"""Number of graphs in the dataset"""
+        r"""Number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return len(self._graphs)
 
     @property

python/dgl/data/karate.py

@@ -15,7 +15,9 @@ class KarateClubDataset(DGLDataset):
     r""" Karate Club dataset for Node Classification
 
     .. deprecated:: 0.5.0
-        `data` is deprecated, it is replaced by:
+
+        ``data`` is deprecated, it is replaced by:
+
             >>> dataset = KarateClubDataset()
             >>> g = dataset[0]
 
@@ -24,19 +26,20 @@ class KarateClubDataset(DGLDataset):
     Model for Conflict and Fission in Small Groups" by Wayne W. Zachary.
     The network became a popular example of community structure in
     networks after its use by Michelle Girvan and Mark Newman in 2002.
-    Official website: http://konect.cc/networks/ucidata-zachary/
+    Official website: `<http://konect.cc/networks/ucidata-zachary/>`_
 
     Karate Club dataset statistics:
-    Nodes: 34
-    Edges: 156
-    Number of Classes: 2
+
+    - Nodes: 34
+    - Edges: 156
+    - Number of Classes: 2
 
     Attributes
     ----------
     num_classes : int
         Number of node classes
     data : list
-        A list of DGLGraph objects
+        A list of :class:`dgl.DGLGraph` objects
 
     Examples
     --------
@@ -78,9 +81,11 @@ class KarateClubDataset(DGLDataset):
 
         Returns
         -------
-        dgl.DGLGraph
+        :class:`dgl.DGLGraph`
+
             graph structure and labels.
-            - ndata['label']: ground truth labels
+
+            - ``ndata['label']``: ground truth labels
         """
         assert idx == 0, "This dataset has only one graph"
         return self._graph

python/dgl/data/knowledge_graph.py

@@ -336,21 +336,27 @@ class FB15k237Dataset(KnowledgeGraphDataset):
     r"""FB15k237 link prediction dataset.
 
     .. deprecated:: 0.5.0
-        `train` is deprecated, it is replaced by:
+
+        - ``train`` is deprecated, it is replaced by:
+
             >>> dataset = FB15k237Dataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.edata['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
             >>> src, dst = graph.edges(train_idx)
             >>> rel = graph.edata['etype'][train_idx]
-        `valid` is deprecated, it is replaced by:
+
+        - ``valid`` is deprecated, it is replaced by:
+
             >>> dataset = FB15k237Dataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.edata['val_mask']
             >>> val_idx = th.nonzero(val_mask).squeeze()
             >>> src, dst = graph.edges(val_idx)
             >>> rel = graph.edata['etype'][val_idx]
-        `test` is deprecated, it is replaced by:
+
+        - ``test`` is deprecated, it is replaced by:
+
             >>> dataset = FB15k237Dataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.edata['test_mask']
@@ -364,10 +370,15 @@ class FB15k237Dataset(KnowledgeGraphDataset):
     created for each edge by default.
 
     FB15k237 dataset statistics:
-    Nodes: 14541
-    Number of relation types: 237
-    Number of reversed relation types: 237
-    Label Split: Train: 272115 ,Valid: 17535, Test: 20466
+
+    - Nodes: 14541
+    - Number of relation types: 237
+    - Number of reversed relation types: 237
+    - Label Split:
+
+        - Train: 272115
+        - Valid: 17535
+        - Test: 20466
 
     Parameters
     ----------
@@ -387,11 +398,11 @@ class FB15k237Dataset(KnowledgeGraphDataset):
         Number of nodes
     num_rels: int
         Number of relation types
-    train: numpy array
+    train: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the training graph
-    valid: numpy array
+    valid: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the validation graph
-    test: numpy array
+    test: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the test graph
 
     Examples
@@ -421,7 +432,6 @@ class FB15k237Dataset(KnowledgeGraphDataset):
     >>> val_g.edata['e_type'] = e_type[val_edges];
     >>>
     >>> # Train, Validation and Test
-    >>>
     """
     def __init__(self, reverse=True, raw_dir=None, force_reload=False, verbose=True):
         name = 'FB15k-237'
@@ -437,16 +447,18 @@ class FB15k237Dataset(KnowledgeGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            The graph contain
-            - edata['e_type']: edge relation type
-            - edata['train_edge_mask']: positive training edge mask
-            - edata['val_edge_mask']: positive validation edge mask
-            - edata['test_edge_mask']: positive testing edge mask
-            - edata['train_mask']: training edge set mask (include reversed training edges)
-            - edata['val_mask']: validation edge set mask (include reversed validation edges)
-            - edata['test_mask']: testing edge set mask (include reversed testing edges)
-            - ndata['ntype']: node type. All 0 in this dataset
+        :class:`dgl.DGLGraph`
+
+            The graph contains
+
+            - ``edata['e_type']``: edge relation type
+            - ``edata['train_edge_mask']``: positive training edge mask
+            - ``edata['val_edge_mask']``: positive validation edge mask
+            - ``edata['test_edge_mask']``: positive testing edge mask
+            - ``edata['train_mask']``: training edge set mask (include reversed training edges)
+            - ``edata['val_mask']``: validation edge set mask (include reversed validation edges)
+            - ``edata['test_mask']``: testing edge set mask (include reversed testing edges)
+            - ``ndata['ntype']``: node type. All 0 in this dataset
         """
         return super(FB15k237Dataset, self).__getitem__(idx)
 
@@ -458,21 +470,27 @@ class FB15kDataset(KnowledgeGraphDataset):
     r"""FB15k link prediction dataset.
 
     .. deprecated:: 0.5.0
-        `train` is deprecated, it is replaced by:
+
+        - ``train`` is deprecated, it is replaced by:
+
             >>> dataset = FB15kDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.edata['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
             >>> src, dst = graph.edges(train_idx)
             >>> rel = graph.edata['etype'][train_idx]
-        `valid` is deprecated, it is replaced by:
+
+        - ``valid`` is deprecated, it is replaced by:
+
             >>> dataset = FB15kDataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.edata['val_mask']
             >>> val_idx = th.nonzero(val_mask).squeeze()
             >>> src, dst = graph.edges(val_idx)
             >>> rel = graph.edata['etype'][val_idx]
-        `test` is deprecated, it is replaced by:
+
+        - ``test`` is deprecated, it is replaced by:
+
             >>> dataset = FB15kDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.edata['test_mask']
@@ -480,7 +498,8 @@ class FB15kDataset(KnowledgeGraphDataset):
             >>> src, dst = graph.edges(test_idx)
             >>> rel = graph.edata['etype'][test_idx]
 
-    The FB15K dataset was introduced in http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf,
+    The FB15K dataset was introduced in `Translating Embeddings for Modeling
+    Multi-relational Data <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_.
     It is a subset of Freebase which contains about
     14,951 entities with 1,345 different relations.
     When creating the dataset, a reverse edge with
@@ -488,10 +507,15 @@ class FB15kDataset(KnowledgeGraphDataset):
     by default.
 
     FB15k dataset statistics:
-    Nodes: 14,951
-    Number of relation types: 1,345
-    Number of reversed relation types: 1,345
-    Label Split: Train: 483142 ,Valid: 50000, Test: 59071
+
+    - Nodes: 14,951
+    - Number of relation types: 1,345
+    - Number of reversed relation types: 1,345
+    - Label Split:
+
+        - Train: 483142
+        - Valid: 50000
+        - Test: 59071
 
     Parameters
     ----------
@@ -511,11 +535,11 @@ class FB15kDataset(KnowledgeGraphDataset):
         Number of nodes
     num_rels: int
         Number of relation types
-    train: numpy array
+    train: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the training graph
-    valid: numpy array
+    valid: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the validation graph
-    test: numpy array
+    test: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the test graph
 
     Examples
@@ -560,16 +584,18 @@ class FB15kDataset(KnowledgeGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            The graph contain
-            - edata['e_type']: edge relation type
-            - edata['train_edge_mask']: positive training edge mask
-            - edata['val_edge_mask']: positive validation edge mask
-            - edata['test_edge_mask']: positive testing edge mask
-            - edata['train_mask']: training edge set mask (include reversed training edges)
-            - edata['val_mask']: validation edge set mask (include reversed validation edges)
-            - edata['test_mask']: testing edge set mask (include reversed testing edges)
-            - ndata['ntype']: node type. All 0 in this dataset
+        :class:`dgl.DGLGraph`
+
+            The graph contains
+
+            - ``edata['e_type']``: edge relation type
+            - ``edata['train_edge_mask']``: positive training edge mask
+            - ``edata['val_edge_mask']``: positive validation edge mask
+            - ``edata['test_edge_mask']``: positive testing edge mask
+            - ``edata['train_mask']``: training edge set mask (include reversed training edges)
+            - ``edata['val_mask']``: validation edge set mask (include reversed validation edges)
+            - ``edata['test_mask']``: testing edge set mask (include reversed testing edges)
+            - ``ndata['ntype']``: node type. All 0 in this dataset
         """
         return super(FB15kDataset, self).__getitem__(idx)
 
@@ -581,21 +607,27 @@ class WN18Dataset(KnowledgeGraphDataset):
     r""" WN18 link prediction dataset.
 
     .. deprecated:: 0.5.0
-        `train` is deprecated, it is replaced by:
+
+        - ``train`` is deprecated, it is replaced by:
+
             >>> dataset = WN18Dataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.edata['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
             >>> src, dst = graph.edges(train_idx)
             >>> rel = graph.edata['etype'][train_idx]
-        `valid` is deprecated, it is replaced by:
+
+        - ``valid`` is deprecated, it is replaced by:
+
             >>> dataset = WN18Dataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.edata['val_mask']
             >>> val_idx = th.nonzero(val_mask).squeeze()
             >>> src, dst = graph.edges(val_idx)
             >>> rel = graph.edata['etype'][val_idx]
-        `test` is deprecated, it is replaced by:
+
+        - ``test`` is deprecated, it is replaced by:
+
             >>> dataset = WN18Dataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.edata['test_mask']
@@ -603,17 +635,23 @@ class WN18Dataset(KnowledgeGraphDataset):
             >>> src, dst = graph.edges(test_idx)
             >>> rel = graph.edata['etype'][test_idx]
 
-    The WN18 dataset was introduced in http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf,
+    The WN18 dataset was introduced in `Translating Embeddings for Modeling
+    Multi-relational Data <http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf>`_.
     It included the full 18 relations scraped from
     WordNet for roughly 41,000 synsets. When creating
     the dataset, a reverse edge with reversed relation
     types are created for each edge by default.
 
-    WN18 dataset tatistics:
-    Nodes: 40943
-    Number of relation types: 18
-    Number of reversed relation types: 18
-    Label Split: Train: 141442 ,Valid: 5000, Test: 5000
+    WN18 dataset statistics:
+
+    - Nodes: 40943
+    - Number of relation types: 18
+    - Number of reversed relation types: 18
+    - Label Split:
+
+        - Train: 141442
+        - Valid: 5000
+        - Test: 5000
 
     Parameters
     ----------
@@ -633,11 +671,11 @@ class WN18Dataset(KnowledgeGraphDataset):
         Number of nodes
     num_rels: int
         Number of relation types
-    train: numpy array
+    train: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the training graph
-    valid: numpy array
+    valid: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the validation graph
-    test: numpy array
+    test: numpy.ndarray
         A numpy array of triplets (src, rel, dst) for the test graph
 
     Examples
@@ -682,16 +720,18 @@ class WN18Dataset(KnowledgeGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            The graph contain
-            - edata['e_type']: edge relation type
-            - edata['train_edge_mask']: positive training edge mask
-            - edata['val_edge_mask']: positive validation edge mask
-            - edata['test_edge_mask']: positive testing edge mask
-            - edata['train_mask']: training edge set mask (include reversed training edges)
-            - edata['val_mask']: validation edge set mask (include reversed validation edges)
-            - edata['test_mask']: testing edge set mask (include reversed testing edges)
-            - ndata['ntype']: node type. All 0 in this dataset
+        :class:`dgl.DGLGraph`
+
+            The graph contains
+
+            - ``edata['e_type']``: edge relation type
+            - ``edata['train_edge_mask']``: positive training edge mask
+            - ``edata['val_edge_mask']``: positive validation edge mask
+            - ``edata['test_edge_mask']``: positive testing edge mask
+            - ``edata['train_mask']``: training edge set mask (include reversed training edges)
+            - ``edata['val_mask']``: validation edge set mask (include reversed validation edges)
+            - ``edata['test_mask']``: testing edge set mask (include reversed testing edges)
+            - ``ndata['ntype']``: node type. All 0 in this dataset
         """
         return super(WN18Dataset, self).__getitem__(idx)
 

python/dgl/data/minigc.py

@@ -12,17 +12,18 @@ from ..transform import add_self_loop
 __all__ = ['MiniGCDataset']
 
 class MiniGCDataset(DGLDataset):
-    """The dataset class.
+    """The synthetic graph classification dataset class.
 
     The datset contains 8 different types of graphs.
-    * class 0 : cycle graph
-    * class 1 : star graph
-    * class 2 : wheel graph
-    * class 3 : lollipop graph
-    * class 4 : hypercube graph
-    * class 5 : grid graph
-    * class 6 : clique graph
-    * class 7 : circular ladder graph
+
+    - class 0 : cycle graph
+    - class 1 : star graph
+    - class 2 : wheel graph
+    - class 3 : lollipop graph
+    - class 4 : hypercube graph
+    - class 5 : grid graph
+    - class 6 : clique graph
+    - class 7 : circular ladder graph
 
     Parameters
     ----------
@@ -50,7 +51,7 @@ class MiniGCDataset(DGLDataset):
     --------
     >>> data = MiniGCDataset(100, 16, 32, seed=0)
 
-    **The dataset instance is an iterable**
+    The dataset instance is an iterable
 
     >>> len(data)
     100
@@ -62,7 +63,7 @@ class MiniGCDataset(DGLDataset):
     >>> label
     tensor(5)
 
-    **Batch the graphs and labels for mini-batch training**
+    Batch the graphs and labels for mini-batch training
 
     >>> graphs, labels = zip(*[data[i] for i in range(16)])
     >>> batched_graphs = dgl.batch(graphs)
@@ -96,13 +97,15 @@ class MiniGCDataset(DGLDataset):
 
     def __getitem__(self, idx):
         """Get the idx-th sample.
-        Paramters
+
+        Parameters
         ---------
         idx : int
             The sample index.
+
         Returns
         -------
-        (dgl.Graph, int)
+        (:class:`dgl.Graph`, Tensor)
             The graph and its label.
         """
         return self.graphs[idx], self.labels[idx]
@@ -143,7 +146,7 @@ class MiniGCDataset(DGLDataset):
         self._gen_circular_ladder(self.num_graphs - len(self.graphs))
         # preprocess
         for i in range(self.num_graphs):
-            # convert to Graph, and add self loops
+            # convert to DGLGraph, and add self loops
             self.graphs[i] = add_self_loop(dgl_graph(self.graphs[i]))
         self.labels = F.tensor(np.array(self.labels).astype(np.int))
 

python/dgl/data/ppi.py

@@ -15,13 +15,17 @@ class PPIDataset(DGLBuiltinDataset):
     r""" Protein-Protein Interaction dataset for inductive node classification
 
     .. deprecated:: 0.5.0
-        `lables` is deprecated, it is replaced by:
+
+        - ``lables`` is deprecated, it is replaced by:
+
             >>> dataset = PPIDataset()
             >>> for g in dataset:
             ....    labels = g.ndata['label']
             ....
             >>>
-        `features` is deprecated, it is replaced by:
+
+        - ``features`` is deprecated, it is replaced by:
+
             >>> dataset = PPIDataset()
             >>> for g in dataset:
             ....    features = g.ndata['feat']
@@ -33,12 +37,13 @@ class PPIDataset(DGLBuiltinDataset):
     50 features and 121 labels. 20 graphs for training, 2 for validation
     and 2 for testing.
 
-    Reference: http://snap.stanford.edu/graphsage/
+    Reference: `<http://snap.stanford.edu/graphsage/>`_
+
+    Statistics:
 
-    PPI dataset statistics:
-    Train examples: 20
-    Valid examples: 2
-    Test examples: 2
+    - Train examples: 20
+    - Valid examples: 2
+    - Test examples: 2
 
     Parameters
     ----------
@@ -167,10 +172,11 @@ class PPIDataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
+        :class:`dgl.DGLGraph`
             graph structure, node features and node labels.
-            - ndata['feat']: node features
-            - ndata['label']: nodel labels
+
+            - ``ndata['feat']``: node features
+            - ``ndata['label']``: node labels
         """
         return self.graphs[item]
 

python/dgl/data/qm7b.py

@@ -16,15 +16,16 @@ class QM7bDataset(DGLDataset):
     This dataset consists of 7,211 molecules with 14 regression targets.
     Nodes means atoms and edges means bonds. Edge data 'h' means
     the entry of Coulomb matrix.
-    Reference: http://quantum-machine.org/datasets/
 
-    Statistics
-    ----------
-    Number of graphs: 7,211
-    Number of regression targets: 14
-    Average number of nodes: 15
-    Average number of edges: 245
-    Edge feature size: 1
+    Reference: `<http://quantum-machine.org/datasets/>`_
+
+    Statistics:
+
+    - Number of graphs: 7,211
+    - Number of regression targets: 14
+    - Average number of nodes: 15
+    - Average number of edges: 245
+    - Edge feature size: 1
 
     Parameters
     ----------
@@ -73,10 +74,6 @@ class QM7bDataset(DGLDataset):
 
     def process(self):
         mat_path = self.raw_path + '.mat'
-        if not check_sha1(mat_path, self._sha1_str):
-            raise UserWarning('File {} is downloaded but the content hash does not match.'
-                              'The repo may be outdated or download may be incomplete. '
-                              'Otherwise you can create an issue for it.'.format(self.name))
         self.graphs, self.label = self._load_graph(mat_path)
 
     def _load_graph(self, filename):
@@ -110,6 +107,10 @@ class QM7bDataset(DGLDataset):
     def download(self):
         file_path = os.path.join(self.raw_dir, self.name + '.mat')
         download(self.url, path=file_path)
+        if not check_sha1(file_path, self._sha1_str):
+            raise UserWarning('File {} is downloaded but the content hash does not match.'
+                              'The repo may be outdated or download may be incomplete. '
+                              'Otherwise you can create an issue for it.'.format(self.name))
 
     @property
     def num_labels(self):
@@ -126,12 +127,17 @@ class QM7bDataset(DGLDataset):
 
         Returns
         -------
-        (dgl.DGLGraph, Tensor)
+        (:class:`dgl.DGLGraph`, Tensor)
         """
         return self.graphs[idx], self.label[idx]
 
     def __len__(self):
-        r"""Number of graphs in the dataset"""
+        r"""Number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return len(self.graphs)
 
 

python/dgl/data/rdf.py

@@ -8,7 +8,10 @@ from collections import OrderedDict
 import itertools
 import abc
 import re
-import rdflib as rdf
+try:
+    import rdflib as rdf
+except ImportError:
+    pass
 
 import networkx as nx
 import numpy as np
@@ -112,6 +115,7 @@ class RDFGraphDataset(DGLBuiltinDataset):
                  raw_dir=None,
                  force_reload=False,
                  verbose=True):
+        import rdflib as rdf
         self._insert_reverse = insert_reverse
         self._print_every = print_every
         self._predict_category = predict_category
@@ -542,15 +546,21 @@ class AIFBDataset(RDFGraphDataset):
     r"""AIFB dataset for node classification task
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        - ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = AIFBDataset()
             >>> graph = dataset[0]
-        `train_idx` is deprecated, it can be replaced by:
+
+        - ``train_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = AIFBDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.nodes[dataset.category].data['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
-        `test_idx` is deprecated, it can be replaced by:
+
+        - ``test_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = AIFBDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.nodes[dataset.category].data['test_mask']
@@ -561,11 +571,15 @@ class AIFBDataset(RDFGraphDataset):
     University of Karlsruhe.
 
     AIFB dataset statistics:
-    Nodes: 7262
-    Edges: 48810 (including reverse edges)
-    Target Category: Personen
-    Number of Classes: 4
-    Label Split: Train: 140, Test: 36
+
+    - Nodes: 7262
+    - Edges: 48810 (including reverse edges)
+    - Target Category: Personen
+    - Number of Classes: 4
+    - Label Split:
+
+        - Train: 140
+        - Test: 36
 
     Parameters
     -----------
@@ -589,7 +603,7 @@ class AIFBDataset(RDFGraphDataset):
         The entity category (node type) that has labels for prediction
     labels : Tensor
         All the labels of the entities in ``predict_category``
-    graph : dgl.DGLGraph
+    graph : :class:`dgl.DGLGraph`
         Graph structure
     train_idx : Tensor
         Entity IDs for training. All IDs are local IDs w.r.t. to ``predict_category``.
@@ -608,8 +622,6 @@ class AIFBDataset(RDFGraphDataset):
     >>> labels = g.nodes[category].data.pop('labels')
     """
 
-    employs = rdf.term.URIRef("http://swrc.ontoware.org/ontology#employs")
-    affiliation = rdf.term.URIRef("http://swrc.ontoware.org/ontology#affiliation")
     entity_prefix = 'http://www.aifb.uni-karlsruhe.de/'
     relation_prefix = 'http://swrc.ontoware.org/'
 
@@ -619,6 +631,9 @@ class AIFBDataset(RDFGraphDataset):
                  raw_dir=None,
                  force_reload=False,
                  verbose=True):
+        import rdflib as rdf
+        self.employs = rdf.term.URIRef("http://swrc.ontoware.org/ontology#employs")
+        self.affiliation = rdf.term.URIRef("http://swrc.ontoware.org/ontology#affiliation")
         url = _get_dgl_url('dataset/rdf/aifb-hetero.zip')
         name = 'aifb-hetero'
         predict_category = 'Personen'
@@ -639,16 +654,23 @@ class AIFBDataset(RDFGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            graph structure, node features and labels.
-            - ndata['train_mask']: mask for training node set
-            - ndata['test_mask']: mask for testing node set
-            - ndata['labels']: mask for labels
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['train_mask']``: mask for training node set
+            - ``ndata['test_mask']``: mask for testing node set
+            - ``ndata['labels']``: mask for labels
         """
         return super(AIFBDataset, self).__getitem__(idx)
 
     def __len__(self):
-        r"""The number of graphs in the dataset."""
+        r"""The number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return super(AIFBDataset, self).__len__()
 
     def parse_entity(self, term):
@@ -703,26 +725,36 @@ class MUTAGDataset(RDFGraphDataset):
     r"""MUTAG dataset for node classification task
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        - ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = MUTAGDataset()
             >>> graph = dataset[0]
-        `train_idx` is deprecated, it can be replaced by:
+
+        - ``train_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = MUTAGDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.nodes[dataset.category].data['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
-        `test_idx` is deprecated, it can be replaced by:
+
+        - ``test_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = MUTAGDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.nodes[dataset.category].data['test_mask']
             >>> test_idx = th.nonzero(test_mask).squeeze()
 
     Mutag dataset statistics:
-    Nodes: 27163
-    Edges: 148100 (including reverse edges)
-    Target Category: d
-    Number of Classes: 2
-    Label Split: Train: 272, Test: 68
+
+    - Nodes: 27163
+    - Edges: 148100 (including reverse edges)
+    - Target Category: d
+    - Number of Classes: 2
+    - Label Split:
+
+        - Train: 272
+        - Test: 68
 
     Parameters
     -----------
@@ -746,7 +778,7 @@ class MUTAGDataset(RDFGraphDataset):
         The entity category (node type) that has labels for prediction
     labels : Tensor
         All the labels of the entities in ``predict_category``
-    graph : dgl.DGLGraph
+    graph : :class:`dgl.DGLGraph`
         Graph structure
     train_idx : Tensor
         Entity IDs for training. All IDs are local IDs w.r.t. to ``predict_category``.
@@ -768,11 +800,6 @@ class MUTAGDataset(RDFGraphDataset):
     d_entity = re.compile("d[0-9]")
     bond_entity = re.compile("bond[0-9]")
 
-    is_mutagenic = rdf.term.URIRef("http://dl-learner.org/carcinogenesis#isMutagenic")
-    rdf_type = rdf.term.URIRef("http://www.w3.org/1999/02/22-rdf-syntax-ns#type")
-    rdf_subclassof = rdf.term.URIRef("http://www.w3.org/2000/01/rdf-schema#subClassOf")
-    rdf_domain = rdf.term.URIRef("http://www.w3.org/2000/01/rdf-schema#domain")
-
     entity_prefix = 'http://dl-learner.org/carcinogenesis#'
     relation_prefix = entity_prefix
 
@@ -782,6 +809,12 @@ class MUTAGDataset(RDFGraphDataset):
                  raw_dir=None,
                  force_reload=False,
                  verbose=True):
+        import rdflib as rdf
+        self.is_mutagenic = rdf.term.URIRef("http://dl-learner.org/carcinogenesis#isMutagenic")
+        self.rdf_type = rdf.term.URIRef("http://www.w3.org/1999/02/22-rdf-syntax-ns#type")
+        self.rdf_subclassof = rdf.term.URIRef("http://www.w3.org/2000/01/rdf-schema#subClassOf")
+        self.rdf_domain = rdf.term.URIRef("http://www.w3.org/2000/01/rdf-schema#domain")
+
         url = _get_dgl_url('dataset/rdf/mutag-hetero.zip')
         name = 'mutag-hetero'
         predict_category = 'd'
@@ -802,16 +835,23 @@ class MUTAGDataset(RDFGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            graph structure, node features and labels.
-            - ndata['train_mask']: mask for training node set
-            - ndata['test_mask']: mask for testing node set
-            - ndata['labels']: mask for labels
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['train_mask']``: mask for training node set
+            - ``ndata['test_mask']``: mask for testing node set
+            - ``ndata['labels']``: mask for labels
         """
         return super(MUTAGDataset, self).__getitem__(idx)
 
     def __len__(self):
-        r"""The number of graphs in the dataset."""
+        r"""The number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return super(MUTAGDataset, self).__len__()
 
     def parse_entity(self, term):
@@ -882,32 +922,42 @@ class BGSDataset(RDFGraphDataset):
     r"""BGS dataset for node classification task
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        - ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = BGSDataset()
             >>> graph = dataset[0]
-        `train_idx` is deprecated, it can be replaced by:
+
+        - ``train_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = BGSDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.nodes[dataset.category].data['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
-        `test_idx` is deprecated, it can be replaced by:
+
+        - ``test_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = BGSDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.nodes[dataset.category].data['test_mask']
             >>> test_idx = th.nonzero(test_mask).squeeze()
 
     BGS namespace convention:
-    http://data.bgs.ac.uk/(ref|id)/<Major Concept>/<Sub Concept>/INSTANCE
+    ``http://data.bgs.ac.uk/(ref|id)/<Major Concept>/<Sub Concept>/INSTANCE``.
     We ignored all literal nodes and the relations connecting them in the
     output graph. We also ignored the relation used to mark whether a
     term is CURRENT or DEPRECATED.
 
     BGS dataset statistics:
-    Nodes: 94806
-    Edges: 672884 (including reverse edges)
-    Target Category: Lexicon/NamedRockUnit
-    Number of Classes: 2
-    Label Split: Train: 117, Test: 29
+
+    - Nodes: 94806
+    - Edges: 672884 (including reverse edges)
+    - Target Category: Lexicon/NamedRockUnit
+    - Number of Classes: 2
+    - Label Split:
+
+        - Train: 117
+        - Test: 29
 
     Parameters
     -----------
@@ -931,7 +981,7 @@ class BGSDataset(RDFGraphDataset):
         The entity category (node type) that has labels for prediction
     labels : Tensor
         All the labels of the entities in ``predict_category``
-    graph : dgl.DGLGraph
+    graph : :class:`dgl.DGLGraph`
         Graph structure
     train_idx : Tensor
         Entity IDs for training. All IDs are local IDs w.r.t. to ``predict_category``.
@@ -950,7 +1000,6 @@ class BGSDataset(RDFGraphDataset):
     >>> labels = g.nodes[category].data.pop('labels')
     """
 
-    lith = rdf.term.URIRef("http://data.bgs.ac.uk/ref/Lexicon/hasLithogenesis")
     entity_prefix = 'http://data.bgs.ac.uk/'
     status_prefix = 'http://data.bgs.ac.uk/ref/CurrentStatus'
     relation_prefix = 'http://data.bgs.ac.uk/ref'
@@ -961,9 +1010,11 @@ class BGSDataset(RDFGraphDataset):
                  raw_dir=None,
                  force_reload=False,
                  verbose=True):
+        import rdflib as rdf
         url = _get_dgl_url('dataset/rdf/bgs-hetero.zip')
         name = 'bgs-hetero'
         predict_category = 'Lexicon/NamedRockUnit'
+        self.lith = rdf.term.URIRef("http://data.bgs.ac.uk/ref/Lexicon/hasLithogenesis")
         super(BGSDataset, self).__init__(name, url, predict_category,
                                          print_every=print_every,
                                          insert_reverse=insert_reverse,
@@ -981,16 +1032,23 @@ class BGSDataset(RDFGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            graph structure, node features and labels.
-            - ndata['train_mask']: mask for training node set
-            - ndata['test_mask']: mask for testing node set
-            - ndata['labels']: mask for labels
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['train_mask']``: mask for training node set
+            - ``ndata['test_mask']``: mask for testing node set
+            - ``ndata['labels']``: mask for labels
         """
         return super(BGSDataset, self).__getitem__(idx)
 
     def __len__(self):
-        r"""The number of graphs in the dataset."""
+        r"""The number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return super(BGSDataset, self).__len__()
 
     def parse_entity(self, term):
@@ -1057,32 +1115,44 @@ class AMDataset(RDFGraphDataset):
     """AM dataset. for node classification task
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        - ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = AMDataset()
             >>> graph = dataset[0]
-        `train_idx` is deprecated, it can be replaced by:
+
+        - ``train_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = AMDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.nodes[dataset.category].data['train_mask']
             >>> train_idx = th.nonzero(train_mask).squeeze()
-        `test_idx` is deprecated, it can be replaced by:
+
+        - ``test_idx`` is deprecated, it can be replaced by:
+
             >>> dataset = AMDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.nodes[dataset.category].data['test_mask']
             >>> test_idx = th.nonzero(test_mask).squeeze()
 
     Namespace convention:
-    Instance: http://purl.org/collections/nl/am/<type>-<id>
-    Relation: http://purl.org/collections/nl/am/<name>
+
+    - Instance: ``http://purl.org/collections/nl/am/<type>-<id>``
+    - Relation: ``http://purl.org/collections/nl/am/<name>``
+
     We ignored all literal nodes and the relations connecting them in the
     output graph.
 
     AM dataset statistics:
-    Nodes: 881680
-    Edges: 5668682 (including reverse edges)
-    Target Category: proxy
-    Number of Classes: 11
-    Label Split: Train: 802, Test: 198
+
+    - Nodes: 881680
+    - Edges: 5668682 (including reverse edges)
+    - Target Category: proxy
+    - Number of Classes: 11
+    - Label Split:
+
+        - Train: 802
+        - Test: 198
 
     Parameters
     -----------
@@ -1106,7 +1176,7 @@ class AMDataset(RDFGraphDataset):
         The entity category (node type) that has labels for prediction
     labels : Tensor
         All the labels of the entities in ``predict_category``
-    graph : dgl.DGLGraph
+    graph : :class:`dgl.DGLGraph`
         Graph structure
     train_idx : Tensor
         Entity IDs for training. All IDs are local IDs w.r.t. to ``predict_category``.
@@ -1125,8 +1195,6 @@ class AMDataset(RDFGraphDataset):
     >>> labels = g.nodes[category].data.pop('labels')
     """
 
-    objectCategory = rdf.term.URIRef("http://purl.org/collections/nl/am/objectCategory")
-    material = rdf.term.URIRef("http://purl.org/collections/nl/am/material")
     entity_prefix = 'http://purl.org/collections/nl/am/'
     relation_prefix = entity_prefix
 
@@ -1136,6 +1204,9 @@ class AMDataset(RDFGraphDataset):
                  raw_dir=None,
                  force_reload=False,
                  verbose=True):
+        import rdflib as rdf
+        self.objectCategory = rdf.term.URIRef("http://purl.org/collections/nl/am/objectCategory")
+        self.material = rdf.term.URIRef("http://purl.org/collections/nl/am/material")
         url = _get_dgl_url('dataset/rdf/am-hetero.zip')
         name = 'am-hetero'
         predict_category = 'proxy'
@@ -1156,16 +1227,23 @@ class AMDataset(RDFGraphDataset):
 
         Return
         -------
-        dgl.DGLGraph
-            graph structure, node features and labels.
-            - ndata['train_mask']: mask for training node set
-            - ndata['test_mask']: mask for testing node set
-            - ndata['labels']: mask for labels
+        :class:`dgl.DGLGraph`
+
+            The graph contains:
+
+            - ``ndata['train_mask']``: mask for training node set
+            - ``ndata['test_mask']``: mask for testing node set
+            - ``ndata['labels']``: mask for labels
         """
         return super(AMDataset, self).__getitem__(idx)
 
     def __len__(self):
-        r"""The number of graphs in the dataset."""
+        r"""The number of graphs in the dataset.
+
+        Return
+        -------
+        int
+        """
         return super(AMDataset, self).__len__()
 
     def parse_entity(self, term):

python/dgl/data/reddit.py

@@ -15,29 +15,43 @@ class RedditDataset(DGLBuiltinDataset):
     r""" Reddit dataset for community detection (node classification)
 
     .. deprecated:: 0.5.0
-        `graph` is deprecated, it is replaced by:
+
+        - ``graph`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> graph = dataset[0]
-        `num_labels` is deprecated, it is replaced by:
+
+        - ``num_labels`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> num_classes = dataset.num_classes
-        `train_mask` is deprecated, it is replaced by:
+
+        - ``train_mask`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> graph = dataset[0]
             >>> train_mask = graph.ndata['train_mask']
-        `val_mask` is deprecated, it is replaced by:
+
+        - ``val_mask`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> graph = dataset[0]
             >>> val_mask = graph.ndata['val_mask']
-        `test_mask` is deprecated, it is replaced by:
+
+        - ``test_mask`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> graph = dataset[0]
             >>> test_mask = graph.ndata['test_mask']
-        `features` is deprecated, it is replaced by:
+
+        - ``features`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> graph = dataset[0]
             >>> features = graph.ndata['feat']
-        `labels` is deprecated, it is replaced by:
+
+        - ``labels`` is deprecated, it is replaced by:
+
             >>> dataset = RedditDataset()
             >>> graph = dataset[0]
             >>> labels = graph.ndata['label']
@@ -49,16 +63,16 @@ class RedditDataset(DGLBuiltinDataset):
     posts with an average degree of 492. We use the first 20 days for training and the
     remaining days for testing (with 30% used for validation).
 
-    Reference: http://snap.stanford.edu/graphsage/
+    Reference: `<http://snap.stanford.edu/graphsage/>`_
 
     Statistics
-    ----------
-    Nodes: 232,965
-    Edges: 114,615,892
-    Node feature size: 602
-    Number of training samples: 153,431
-    Number of validation samples: 23,831
-    Number of test samples: 55,703
+
+    - Nodes: 232,965
+    - Edges: 114,615,892
+    - Node feature size: 602
+    - Number of training samples: 153,431
+    - Number of validation samples: 23,831
+    - Number of test samples: 55,703
 
     Parameters
     ----------
@@ -76,7 +90,7 @@ class RedditDataset(DGLBuiltinDataset):
     ----------
     num_classes : int
         Number of classes for each node
-    graph : dgl.DGLGraph
+    graph : :class:`dgl.DGLGraph`
         Graph of the dataset
     num_labels : int
         Number of classes for each node
@@ -220,13 +234,14 @@ class RedditDataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
-            graph structure, node labels, node features and splitting masks
-            - ndata['label']: node label
-            - ndata['feat']: node feature
-            - ndata['train_mask']： mask for training node set
-            - ndata['val_mask']: mask for validation node set
-            - ndata['test_mask']: mask for test node set
+        :class:`dgl.DGLGraph`
+            graph structure, node labels, node features and splitting masks:
+
+            - ``ndata['label']``: node label
+            - ``ndata['feat']``: node feature
+            - ``ndata['train_mask']``： mask for training node set
+            - ``ndata['val_mask']``: mask for validation node set
+            - ``ndata['test_mask']:`` mask for test node set
         """
         assert idx == 0, "Reddit Dataset only has one graph"
         return self._graph

python/dgl/data/sbm.py

@@ -59,8 +59,7 @@ def sbm(n_blocks, block_size, p, q, rng=None):
 class SBMMixtureDataset(DGLDataset):
     r""" Symmetric Stochastic Block Model Mixture
 
-    Reference: Appendix C of "Supervised Community Detection with Hierarchical
-               Graph Neural Networks" (https://arxiv.org/abs/1705.08415).
+    Reference: Appendix C of `Supervised Community Detection with Hierarchical Graph Neural Networks <https://arxiv.org/abs/1705.08415>`_
 
     Parameters
     ----------
@@ -175,15 +174,15 @@ class SBMMixtureDataset(DGLDataset):
 
         Returns
         -------
-        graph : dgl.DGLGraph
+        graph: :class:`dgl.DGLGraph`
             The original graph
-        line_graph : dgl.DGLGraph
+        line_graph: :class:`dgl.DGLGraph`
             The line graph of `graph`
-        graph_degree : numpy.ndarray
+        graph_degree: numpy.ndarray
             In degrees for each node in `graph`
-        line_graph_degree : numpy.ndarray
+        line_graph_degree: numpy.ndarray
             In degrees for each node in `line_graph`
-        pm_pd : numpy.ndarray
+        pm_pd: numpy.ndarray
             Edge indicator matrices Pm and Pd
         """
         return self._graphs[idx], self._line_graphs[idx], \
@@ -203,29 +202,30 @@ class SBMMixtureDataset(DGLDataset):
         Parameters
         ----------
         x : tuple
-            a batch of data that contains
-            graph : dgl.DGLGraph
+            a batch of data that contains:
+
+            - graph: :class:`dgl.DGLGraph`
                 The original graph
-            line_graph : dgl.DGLGraph
+            - line_graph: :class:`dgl.DGLGraph`
                 The line graph of `graph`
-            graph_degree : numpy.ndarray
+            - graph_degree: numpy.ndarray
                 In degrees for each node in `graph`
-            line_graph_degree : numpy.ndarray
+            - line_graph_degree: numpy.ndarray
                 In degrees for each node in `line_graph`
-            pm_pd : numpy.ndarray
+            - pm_pd: numpy.ndarray
                 Edge indicator matrices Pm and Pd
 
         Returns
         -------
-        g_batch : dgl.DGLGraph
+        g_batch: :class:`dgl.DGLGraph`
             Batched graphs
-        lg_batch : dgl.DGLGraph
+        lg_batch: :class:`dgl.DGLGraph`
             Batched line graphs
-        degg_batch : numpy.ndarray
+        degg_batch: numpy.ndarray
             A batch of in degrees for each node in `g_batch`
-        deglg_batch : numpy.ndarray
+        deglg_batch: numpy.ndarray
             A batch of in degrees for each node in `lg_batch`
-        pm_pd_batch : numpy.ndarray
+        pm_pd_batch: numpy.ndarray
             A batch of edge indicator matrices Pm and Pd
         """
         g, lg, deg_g, deg_lg, pm_pd = zip(*x)

python/dgl/data/tree.py

@@ -23,13 +23,14 @@ class SSTDataset(DGLBuiltinDataset):
     r"""Stanford Sentiment Treebank dataset.
 
     .. deprecated:: 0.5.0
-        `trees` is deprecated, it is replaced by:
+        
+        - ``trees`` is deprecated, it is replaced by:
+
             >>> dataset = SSTDataset()
             >>> for tree in dataset:
             ....    # your code here
-            ....
-            >>>
-        `num_vocabs` is deprecated, it is replaced by `vocab_size`
+
+        - ``num_vocabs`` is deprecated, it is replaced by ``vocab_size``.
 
     Each sample is the constituency tree of a sentence. The leaf nodes
     represent words. The word is a int value stored in the ``x`` feature field.
@@ -37,14 +38,14 @@ class SSTDataset(DGLBuiltinDataset):
     Each node also has a sentiment annotation: 5 classes (very negative,
     negative, neutral, positive and very positive). The sentiment label is a
     int value stored in the ``y`` feature field.
-    Official site: http://nlp.stanford.edu/sentiment/index.html
+    Official site: `<http://nlp.stanford.edu/sentiment/index.html>`_
 
-    Statistics
-    ----------
-    Train examples: 8,544
-    Dev examples: 1,101
-    Test examples: 2,210
-    Number of classes for each node: 5
+    Statistics:
+
+    - Train examples: 8,544
+    - Dev examples: 1,101
+    - Test examples: 2,210
+    - Number of classes for each node: 5
 
     Parameters
     ----------
@@ -100,16 +101,14 @@ class SSTDataset(DGLBuiltinDataset):
     >>> train_data.vocab_size
     19536
     >>> train_data[0]
-    DGLGraph(num_nodes=71, num_edges=70,
-         ndata_schemes={'x': Scheme(shape=(), dtype=torch.int64), 'y': Scheme(shape=(),
-          dtype=torch.int64), 'mask': Scheme(shape=(), dtype=torch.int64)}
-         edata_schemes={})
+    Graph(num_nodes=71, num_edges=70,
+      ndata_schemes={'x': Scheme(shape=(), dtype=torch.int64), 'y': Scheme(shape=(), dtype=torch.int64), 'mask': Scheme(shape=(), dtype=torch.int64)}
+      edata_schemes={})
     >>> for tree in train_data:
     ...     input_ids = tree.ndata['x']
     ...     labels = tree.ndata['y']
     ...     mask = tree.ndata['mask']
     ...     # your code here
-    >>>
     """
 
     PAD_WORD = -1  # special pad word id
@@ -247,11 +246,13 @@ class SSTDataset(DGLBuiltinDataset):
 
         Returns
         -------
-        dgl.DGLGraph
-            graph structure, word id for each node, node labels and masks
-            - ndata['x']: word id of the node
-            - ndata['y']: label of the node
-            - ndata['mask']: 1 if the node is a leaf, otherwise 0
+        :class:`dgl.DGLGraph`
+
+            graph structure, word id for each node, node labels and masks.
+
+            - ``ndata['x']``: word id of the node
+            - ``ndata['y']:`` label of the node
+            - ``ndata['mask']``: 1 if the node is a leaf, otherwise 0
         """
         return self._trees[idx]
 

python/dgl/data/tu.py

@@ -16,17 +16,18 @@ class LegacyTUDataset(DGLBuiltinDataset):
     Parameters
     ----------
     name : str
-        Dataset Name, such as `ENZYMES`, `DD`, `COLLAB`
+        Dataset Name, such as ``ENZYMES``, ``DD``, ``COLLAB``, ``MUTAG``, can be the 
+        datasets name on `<https://chrsmrrs.github.io/datasets/docs/datasets/>`_.
     use_pandas : bool
         Numpy's file read function has performance issue when file is large,
         using pandas can be faster.
         Default: False
     hidden_size : int
         Some dataset doesn't contain features.
-        Use constant node features initialization instead, with hidden size as `hidden_size`.
+        Use constant node features initialization instead, with hidden size as ``hidden_size``.
         Default : 10
     max_allow_node : int
-        Remove graphs that contains more nodes than `max_allow_node`.
+        Remove graphs that contains more nodes than ``max_allow_node``.
         Default : None
 
     Attributes
@@ -40,7 +41,7 @@ class LegacyTUDataset(DGLBuiltinDataset):
     --------
     >>> data = LegacyTUDataset('DD')
 
-    **The dataset instance is an iterable**
+    The dataset instance is an iterable
 
     >>> len(data)
     1178
@@ -52,7 +53,7 @@ class LegacyTUDataset(DGLBuiltinDataset):
     >>> label
     tensor(1)
 
-    **Batch the graphs and labels for mini-batch training*
+    Batch the graphs and labels for mini-batch training
 
     >>> graphs, labels = zip(*[data[i] for i in range(16)])
     >>> batched_graphs = dgl.batch(graphs)
@@ -190,20 +191,23 @@ class LegacyTUDataset(DGLBuiltinDataset):
 
     def __getitem__(self, idx):
         """Get the idx-th sample.
-        Paramters
+
+        Parameters
         ---------
         idx : int
             The sample index.
+
         Returns
         -------
-        (dgl.Graph, int)
-            Graph with node feature stored in `feat` field and node label in `node_label` if available.
+        (:class:`dgl.DGLGraph`, Tensor)
+            Graph with node feature stored in ``feat`` field and node label in ``node_label`` if available.
             And its label.
         """
         g = self.graph_lists[idx]
         return g, self.graph_labels[idx]
 
     def __len__(self):
+        """Return the number of graphs in the dataset."""
         return len(self.graph_lists)
 
     def _file_path(self, category):
@@ -234,8 +238,8 @@ class TUDataset(DGLBuiltinDataset):
     Parameters
     ----------
     name : str
-        Dataset Name, such as `ENZYMES`, `DD`, `COLLAB`, `MUTAG`, can be the 
-        datasets name on https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets.
+        Dataset Name, such as ``ENZYMES``, ``DD``, ``COLLAB``, ``MUTAG``, can be the 
+        datasets name on `<https://chrsmrrs.github.io/datasets/docs/datasets/>`_.
 
     Attributes
     ----------
@@ -248,7 +252,7 @@ class TUDataset(DGLBuiltinDataset):
     --------
     >>> data = TUDataset('DD')
 
-    **The dataset instance is an iterable**
+    The dataset instance is an iterable
 
     >>> len(data)
     188
@@ -260,7 +264,7 @@ class TUDataset(DGLBuiltinDataset):
     >>> label
     tensor([1])
 
-    **Batch the graphs and labels for mini-batch training*
+    Batch the graphs and labels for mini-batch training
 
     >>> graphs, labels = zip(*[data[i] for i in range(16)])
     >>> batched_graphs = dgl.batch(graphs)
@@ -355,20 +359,23 @@ class TUDataset(DGLBuiltinDataset):
 
     def __getitem__(self, idx):
         """Get the idx-th sample.
-        Paramters
+
+        Parameters
         ---------
         idx : int
             The sample index.
+
         Returns
         -------
-        (dgl.Graph, int)
-            Graph with node feature stored in `feat` field and node label in `node_label` if available.
+        (:class:`dgl.DGLGraph`, Tensor)
+            Graph with node feature stored in ``feat`` field and node label in ``node_label`` if available.
             And its label.
         """
         g = self.graph_lists[idx]
         return g, self.graph_labels[idx]
 
     def __len__(self):
+        """Return the number of graphs in the dataset."""
         return len(self.graph_lists)
 
     def _file_path(self, category):