[Doc] API doc string for many components (#245)

* builtin function docstring

* init docstring

* view docstring

* add init to index and fix

* node /edge feature

* Example -> Examples

* fix func hyperlink

* traversal

* fix

* from/to_networkx docstring

* minor

* fix duplicate label

* comment about tensor framework

* from scipy sparse matrix

* fix format errors

docs/source/api/python/graph.rst

@@ -12,7 +12,6 @@ Adding nodes and edges
 .. autosummary::
     :toctree: ../../generated/
 
-    DGLGraph.__init__
     DGLGraph.add_nodes
     DGLGraph.add_edge
     DGLGraph.add_edges

docs/source/api/python/index.rst

@@ -5,6 +5,7 @@ API Reference
    :maxdepth: 2
 
    graph
+   init
    batch
    function
    traversal

docs/source/api/python/init.rst

+.. _apiinit:
+
+Feature Initializer
+===================
+
+.. automodule:: dgl.init
+.. autosummary::
+   :toctree: ../../generated/
+
+   base_initializer
+   zero_initializer

python/dgl/contrib/data/knowledge_graph.py

@@ -56,13 +56,13 @@ class RGCNEntityDataset(object):
     test_idx: numpy.array
         ids of entities used for testing
 
-    Usage
-    -----
-    Usually, user don't need to directly use this class. Instead, DGL provides
+    Usually, users don't need to directly use this class. Instead, DGL provides
     wrapper function to load data (see example below).
     When loading data, besides specifying dataset name, user can provide two
     optional arguments:
 
+    Parameters
+    ----------
     bfs_level: int
         prune out nodes that are more than ``bfs_level`` hops away from
         labeled nodes, i.e., nodes won't be touched during propagation. If set
@@ -71,8 +71,8 @@ class RGCNEntityDataset(object):
         After pruning, whether or not to relabel all nodes with consecutive
         node ids
 
-    Example
-    -------
+    Examples
+    --------
     Load aifb dataset, prune out nodes that are more than 3 hops away from
     labeled nodes, and relabel the remaining nodes with consecutive ids
 
@@ -157,13 +157,11 @@ class RGCNLinkDataset(object):
     test: numpy.array
         all relation triplets (src, rel, dst) for testing
 
-    Usage
-    -----
     Usually, user don't need to directly use this class. Instead, DGL provides
     wrapper function to load data (see example below).
 
-    Example
-    -------
+    Examples
+    --------
     Load FB15k-237 dataset
 
     >>> from dgl.contrib.data import load_data

python/dgl/function/message.py

@@ -113,17 +113,27 @@ class CopyEdgeMessageFunction(MessageFunction):
 
 
 def src_mul_edge(src, edge, out):
-    """Builtin message function that computes message by multiplying source node features
-    with edge features.
+    """Builtin message function that computes message by multiplying source
+    node features with edge features.
 
     Parameters
     ----------
     src : str
-        The source feature name.
+        The source feature field.
     edge : str
-        The edge feature name.
+        The edge feature field.
     out : str
-        The output message name.
+        The output message field.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> message_func = dgl.function.src_mul_edge(src='h', edge='w', out='m')
+
+    The above example is equivalent to the following user defined function:
+
+    >>> def message_func(edges):
+    >>>   return {'m': edges.src['h'] * edges.data['w']}
     """
     return SrcMulEdgeMessageFunction(operator.mul, src, edge, out)
 
@@ -133,9 +143,19 @@ def copy_src(src, out):
     Parameters
     ----------
     src : str
-        The source feature name.
+        The source feature field.
     out : str
-        The output message name.
+        The output message field.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> message_func = dgl.function.copy_src(src='h', out='m')
+
+    The above example is equivalent to the following user defined function:
+
+    >>> def message_func(edges):
+    >>>     return {'m': edges.src['h']}
     """
     return CopySrcMessageFunction(src, out)
 
@@ -145,8 +165,18 @@ def copy_edge(edge, out):
     Parameters
     ----------
     edge : str
-        The edge feature name.
+        The edge feature field.
     out : str
-        The output message name.
+        The output message field.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> message_func = dgl.function.copy_edge(edge='h', out='m')
+
+    The above example is equivalent to the following user defined function:
+
+    >>> def message_func(edges):
+    >>>     return {'m': edges.data['h']}
     """
     return CopyEdgeMessageFunction(edge, out)

python/dgl/function/reducer.py

@@ -52,9 +52,20 @@ def sum(msg, out):
     Parameters
     ----------
     msg : str
-        The message name.
+        The message field.
     out : str
-        The output node feature name.
+        The output node feature field.
+    Examples
+    --------
+    >>> import dgl
+    >>> reduce_func = dgl.function.sum(msg='m', out='h')
+
+    The above example is equivalent to the following user defined function
+    (if using PyTorch):
+
+    >>> import torch
+    >>> def reduce_func(nodes):
+    >>>     return {'h': torch.sum(nodes.mailbox['m'], dim=1)}
     """
     return SimpleReduceFunction("sum", F.sum, msg, out)
 
@@ -64,8 +75,20 @@ def max(msg, out):
     Parameters
     ----------
     msg : str
-        The message name.
+        The message field.
     out : str
-        The output node feature name.
+        The output node feature field.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> reduce_func = dgl.function.max(msg='m', out='h')
+
+    The above example is equivalent to the following user defined function
+    (if using PyTorch):
+
+    >>> import torch
+    >>> def reduce_func(nodes):
+    >>>     return {'h': torch.max(nodes.mailbox['m'], dim=1)}
     """
     return SimpleReduceFunction("max", F.max, msg, out)

python/dgl/graph.py

@@ -272,6 +272,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edge(0, 1)
@@ -326,6 +327,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(4)
         >>> G.add_edges([0, 2], [1, 3]) # add edges (0, 1) and (2, 3)
@@ -444,6 +446,7 @@ class DGLGraph(object):
         False
 
         Equivalently,
+
         >>> 0 in G
         True
 
@@ -466,9 +469,9 @@ class DGLGraph(object):
         return self._graph.has_node(vid)
 
     def has_nodes(self, vids):
-        """Return a 0-1 array `a` given the node ID array `vids`.
+        """Return a 0-1 array ``a`` given the node ID array ``vids``.
 
-        `a[i]` is 1 if the graph contains node `vids[i]`, 0 otherwise.
+        ``a[i]`` is 1 if the graph contains node ``vids[i]``, 0 otherwise.
 
         Parameters
         ----------
@@ -483,6 +486,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.has_nodes([0, 1, 2, 3, 4])
@@ -548,11 +552,13 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0], [1, 2]) # (0, 1), (0, 2)
 
         Check if (0, 1), (0, 2), (1, 0), (2, 0) exist in the graph above:
+
         >>> G.has_edges_between([0, 0, 1, 2], [1, 2, 0, 0])
         tensor([1, 1, 0, 0])
 
@@ -584,6 +590,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([1, 2], [0, 0]) # (1, 0), (2, 0)
@@ -615,6 +622,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0], [1, 2]) # (0, 1), (0, 2)
@@ -652,6 +660,7 @@ class DGLGraph(object):
         The following example uses PyTorch backend.
 
         For simple graphs:
+
         >>> G = dgl.DGLGraph()
         >>> G.add_node(3)
         >>> G.add_edges([0, 0], [1, 2]) # (0, 1), (0, 2)
@@ -661,11 +670,13 @@ class DGLGraph(object):
         0
 
         For multigraphs:
+
         >>> G = dgl.DGLGraph(multigraph=True)
         >>> G.add_nodes(3)
 
         Adding edges (0, 1), (0, 2), (0, 1), (0, 2), so edge ID 0 and 2 both
         connect from 0 and 1, while edge ID 1 and 3 both connect from 0 and 2.
+
         >>> G.add_edges([0, 0, 0, 0], [1, 2, 1, 2])
         >>> G.edge_id(0, 1)
         tensor([0, 2])
@@ -710,6 +721,7 @@ class DGLGraph(object):
         The following example uses PyTorch backend.
 
         For simple graphs:
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0], [1, 2]) # (0, 1), (0, 2)
@@ -718,12 +730,14 @@ class DGLGraph(object):
         tensor([1, 0])
 
         For multigraphs
+
         >>> G = dgl.DGLGraph(multigraph=True)
         >>> G.add_nodes(4)
         >>> G.add_edges([0, 0, 0], [1, 1, 2])   # (0, 1), (0, 1), (0, 2)
 
         Get all edges between (0, 1), (0, 2), (0, 3).  Note that there is no
         edge between 0 and 3:
+
         >>> G.edge_ids([0, 0, 0], [1, 2, 3])
         (tensor([0, 0, 0]), tensor([1, 1, 2]), tensor([0, 1, 2]))
 
@@ -779,29 +793,32 @@ class DGLGraph(object):
             The node(s).
         form : str, optional
             The return form. Currently support:
+
             - 'all' : a tuple (u, v, eid)
             - 'uv'  : a pair (u, v), default
             - 'eid' : one eid tensor
 
         Returns
         -------
-        A tuple of Tensors `(eu, ev, eid)` if `form == 'all'`.
-            `eid[i]` is the ID of an inbound edge to `ev[i]` from `eu[i]`.
-            All inbound edges to `v` are returned.
+        A tuple of Tensors ``(eu, ev, eid)`` if ``form == 'all'``.
+            ``eid[i]`` is the ID of an inbound edge to ``ev[i]`` from ``eu[i]``.
+            All inbound edges to ``v`` are returned.
         A pair of Tensors (eu, ev) if form == 'uv'
-            `eu[i]` is the source node of an inbound edge to `ev[i]`.
-            All inbound edges to `v` are returned.
+            ``eu[i]`` is the source node of an inbound edge to ``ev[i]``.
+            All inbound edges to ``v`` are returned.
         One Tensor if form == 'eid'
-            `eid[i]` is ID of an inbound edge to any of the nodes in `v`.
+            ``eid[i]`` is ID of an inbound edge to any of the nodes in ``v``.
 
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0, 1], [1, 2, 2])   # (0, 1), (0, 2), (1, 2)
 
         For a single node:
+
         >>> G.in_edges(2)
         (tensor([0, 1]), tensor([2, 2]))
         >>> G.in_edges(2, 'all')
@@ -810,6 +827,7 @@ class DGLGraph(object):
         tensor([1, 2])
 
         For multiple nodes:
+
         >>> G.in_edges([1, 2])
         (tensor([0, 0, 1]), tensor([1, 2, 2]))
         >>> G.in_edges([1, 2], 'all')
@@ -835,29 +853,32 @@ class DGLGraph(object):
             The node(s).
         form : str, optional
             The return form. Currently support:
+
             - 'all' : a tuple (u, v, eid)
             - 'uv'  : a pair (u, v), default
             - 'eid' : one eid tensor
 
         Returns
         -------
-        A tuple of Tensors `(eu, ev, eid)` if `form == 'all'`.
-            `eid[i]` is the ID of an outbound edge from `eu[i]` from `ev[i]`.
-            All outbound edges from `v` are returned.
+        A tuple of Tensors ``(eu, ev, eid)`` if ``form == 'all'``.
+            ``eid[i]`` is the ID of an outbound edge from ``eu[i]`` from ``ev[i]``.
+            All outbound edges from ``v`` are returned.
         A pair of Tensors (eu, ev) if form == 'uv'
-            `ev[i]` is the destination node of an outbound edge from `eu[i]`.
-            All outbound edges from `v` are returned.
+            ``ev[i]`` is the destination node of an outbound edge from ``eu[i]``.
+            All outbound edges from ``v`` are returned.
         One Tensor if form == 'eid'
-            `eid[i]` is ID of an outbound edge from any of the nodes in `v`.
+            ``eid[i]`` is ID of an outbound edge from any of the nodes in ``v``.
 
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0, 1], [1, 2, 2])   # (0, 1), (0, 2), (1, 2)
 
         For a single node:
+
         >>> G.out_edges(0)
         (tensor([0, 0]), tensor([1, 2]))
         >>> G.out_edges(0, 'all')
@@ -866,6 +887,7 @@ class DGLGraph(object):
         tensor([0, 1])
 
         For multiple nodes:
+
         >>> G.out_edges([0, 1])
         (tensor([0, 0, 1]), tensor([1, 2, 2]))
         >>> G.out_edges([0, 1], 'all')
@@ -889,6 +911,7 @@ class DGLGraph(object):
         ----------
         form : str, optional
             The return form. Currently support:
+
             - 'all' : a tuple (u, v, eid)
             - 'uv'  : a pair (u, v), default
             - 'eid' : one eid tensor
@@ -898,18 +921,19 @@ class DGLGraph(object):
         Returns
         -------
         A tuple of Tensors (u, v, eid) if form == 'all'
-            `eid[i]` is the ID of an edge between `u[i]` and `v[i]`.
+            ``eid[i]`` is the ID of an edge between ``u[i]`` and ``v[i]``.
             All edges are returned.
         A pair of Tensors (u, v) if form == 'uv'
-            An edge exists between `u[i]` and `v[i]`.
-            If `n` edges exist between `u` and `v`, then `u` and `v` as a pair
-            will appear `n` times.
+            An edge exists between ``u[i]`` and ``v[i]``.
+            If ``n`` edges exist between ``u`` and ``v``, then ``u`` and ``v`` as a pair
+            will appear ``n`` times.
         One Tensor if form == 'eid'
-            `eid[i]` is the ID of an edge in the graph.
+            ``eid[i]`` is the ID of an edge in the graph.
 
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0, 1], [1, 2, 2])   # (0, 1), (0, 2), (1, 2)
@@ -929,7 +953,7 @@ class DGLGraph(object):
             raise DGLError('Invalid form:', form)
 
     def in_degree(self, v):
-        """Return the in-degree of node `v`.
+        """Return the in-degree of node ``v``.
 
         Parameters
         ----------
@@ -973,6 +997,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0, 1], [1, 2, 2])   # (0, 1), (0, 2), (1, 2)
@@ -1034,6 +1059,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(3)
         >>> G.add_edges([0, 0, 1], [1, 2, 2])   # (0, 1), (0, 2), (1, 2)
@@ -1066,6 +1092,19 @@ class DGLGraph(object):
         -------
         networkx.DiGraph
             The nx graph
+
+        Examples
+        --------
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> g = DGLGraph()
+        >>> g.add_nodes(5, {'n1': th.randn(5, 10)})
+        >>> g.add_edges([0,1,3,4], [2,4,0,3], {'e1': th.randn(4, 6)})
+        >>> nxg = g.to_networkx(node_attrs=['n1'], edge_attrs=['e1'])
         """
         nx_graph = self._graph.to_networkx()
         if node_attrs is not None:
@@ -1093,6 +1132,28 @@ class DGLGraph(object):
             The node attributes needs to be copied.
         edge_attrs : iterable of str, optional
             The edge attributes needs to be copied.
+
+        Examples
+        --------
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> import networkx as nx
+        >>> nxg = nx.DiGraph()
+        >>> nxg.add_edge(0, 1, id=0, e1=5, e2=th.zeros(4))
+        >>> nxg.add_edge(2, 3, id=2, e1=6, e2=th.ones(4))
+        >>> nxg.add_edge(1, 2, id=1, e1=2, e2=th.full((4,), 2))
+        >>> g = dgl.DGLGraph()
+        >>> g.from_networkx(nxg, edge_attrs=['e1', 'e2'])
+        >>> g.edata['e1']
+        tensor([5, 2, 6])
+        >>> g.edata['e2']
+        tensor([[0., 0., 0., 0.],
+                [2., 2., 2., 2.],
+                [1., 1., 1., 1.]])
         """
         self.clear()
         self._graph.from_networkx(nx_graph)
@@ -1116,7 +1177,7 @@ class DGLGraph(object):
             has_edge_id = 'id' in next(iter(nx_graph.edges(data=True)))[-1]
             attr_dict = defaultdict(lambda: [None] * self.number_of_edges())
             if has_edge_id:
-                for u, v, attrs in nx_graph.edges(data=True):
+                for _, _, attrs in nx_graph.edges(data=True):
                     for key in edge_attrs:
                         attr_dict[key][attrs['id']] = attrs[key]
             else:
@@ -1134,6 +1195,16 @@ class DGLGraph(object):
         ----------
         a : scipy sparse matrix
             The graph's adjacency matrix
+
+        Examples
+        --------
+        >>> from scipy.sparse import coo_matrix
+        >>> row = np.array([0, 3, 1, 0])
+        >>> col = np.array([0, 3, 1, 2])
+        >>> data = np.array([4, 5, 7, 9])
+        >>> a = coo_matrix((data, (row, col)), shape=(4, 4))
+        >>> g = dgl.DGLGraph()
+        >>> g.from_scipy_sparse_matrix(a)
         """
         self.clear()
         self._graph.from_scipy_sparse_matrix(a)
@@ -1144,20 +1215,49 @@ class DGLGraph(object):
     def node_attr_schemes(self):
         """Return the node feature schemes.
 
+        Each feature scheme is a named tuple that stores the shape and data type
+        of the node feature
+
         Returns
         -------
         dict of str to schemes
             The schemes of node feature columns.
+
+        Examples
+        --------
+        >>> G = dgl.DGLGraph()
+        >>> G.add_nodes(3)
+        >>> G.ndata['x'] = torch.zeros((3,5))
+        >>> G.node_attr_schemes()
+        {'x': Scheme(shape=(5,), dtype=torch.float32)}
         """
         return self._node_frame.schemes
 
     def edge_attr_schemes(self):
         """Return the edge feature schemes.
 
+        Each feature scheme is a named tuple that stores the shape and data type
+        of the node feature
+
         Returns
         -------
         dict of str to schemes
             The schemes of edge feature columns.
+
+        Examples
+        --------
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> G = dgl.DGLGraph()
+        >>> G.add_nodes(3)
+        >>> G.add_edges([0, 1], 2)  # 0->2, 1->2
+        >>> G.edata['y'] = th.zeros((2, 4))
+        >>> G.edge_attr_schemes()
+        {'y': Scheme(shape=(4,), dtype=torch.float32)}
         """
         return self._edge_frame.schemes
 
@@ -1167,6 +1267,9 @@ class DGLGraph(object):
         Initializer is a callable that returns a tensor given the shape, data type
         and device context.
 
+        When a subset of the nodes are assigned a new feature, initializer is
+        used to create feature for rest of the nodes.
+
         Parameters
         ----------
         initializer : callable
@@ -1175,17 +1278,45 @@ class DGLGraph(object):
             The feature field name. Default is set an initializer for all the
             feature fields.
 
-        See Also
+        Examples
         --------
-        dgl.init.base_initializer
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> G = dgl.DGLGraph()
+        >>> G.add_nodes(3)
+
+        Set initializer for all node features
+
+        >>> G.set_n_initializer(dgl.init.zero_initializer)
+
+        Set feature for partial nodes
+
+        >>> G.nodes[[0, 2]].data['x'] = th.ones((2, 5))
+        >>> G.ndata
+        {'x' : tensor([[1., 1., 1., 1., 1.],
+                       [0., 0., 0., 0., 0.],
+                       [1., 1., 1., 1., 1.]])}
+
+        Note
+        -----
+        User defined initializer must follow the signature of
+        :func:`dgl.init.base_initializer() <dgl.init.base_initializer>`
+
         """
         self._node_frame.set_initializer(initializer, field)
 
     def set_e_initializer(self, initializer, field=None):
         """Set the initializer for empty edge features.
 
-        Initializer is a callable that returns a tensor given the shape, data type
-        and device context.
+        Initializer is a callable that returns a tensor given the shape, data
+        type and device context.
+
+        When a subset of the edges are assigned a new feature, initializer is
+        used to create feature for rest of the edges.
 
         Parameters
         ----------
@@ -1195,30 +1326,159 @@ class DGLGraph(object):
             The feature field name. Default is set an initializer for all the
             feature fields.
 
-        See Also
+        Examples
         --------
-        dgl.init.base_initializer
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> G = dgl.DGLGraph()
+        >>> G.add_nodes(3)
+        >>> G.add_edges([0, 1], 2)  # 0->2, 1->2
+
+        Set initializer for edge features
+
+        >>> G.set_e_initializer(dgl.init.zero_initializer)
+
+        Set feature for partial edges
+
+        >>> G.edges[1, 2].data['y'] = th.ones((1, 4))
+        >>> G.edata
+        {'y' : tensor([[0., 0., 0., 0.],
+                       [1., 1., 1., 1.]])}
+
+        Note
+        -----
+        User defined initializer must follow the signature of
+        :func:`dgl.init.base_initializer() <dgl.init.base_initializer>`
         """
         self._edge_frame.set_initializer(initializer, field)
 
     @property
     def nodes(self):
-        """Return a node view that can used to set/get feature data."""
+        """Return a node view that can used to set/get feature data.
+
+        Examples
+        --------
+
+        >>> G = dgl.DGLGraph()
+        >>> G.add_nodes(3)
+
+        Get nodes in graph G:
+
+        >>> G.nodes()
+        tensor([0, 1, 2])
+
+        Get feature dictionary of all nodes:
+
+        >>> G.nodes[:].data
+        {}
+
+        The above can be abbreviated as
+
+        >>> G.ndata
+        {}
+
+        Init all 3 nodes with zero vector(len=5)
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> G.ndata['x'] = th.zeros((3, 5))
+        >>> G.ndata['x']
+        {'x' : tensor([[0., 0., 0., 0., 0.],
+                       [0., 0., 0., 0., 0.],
+                       [0., 0., 0., 0., 0.]])}
+
+        Use G.nodes to get/set features for some nodes.
+
+        >>> G.nodes[[0, 2]].data['x'] = th.ones((2, 5))
+        >>> G.ndata
+        {'x' : tensor([[1., 1., 1., 1., 1.],
+                       [0., 0., 0., 0., 0.],
+                       [1., 1., 1., 1., 1.]])}
+
+        See Also
+        --------
+        dgl.DGLGraph.ndata
+
+        """
         return NodeView(self)
 
     @property
     def ndata(self):
-        """Return the data view of all the nodes."""
+        """Return the data view of all the nodes.
+
+        DGLGraph.ndata is an abbreviation of DGLGraph.nodes[:].data
+
+        See Also
+        --------
+        dgl.DGLGraph.nodes
+        """
         return self.nodes[:].data
 
     @property
     def edges(self):
-        """Return a edges view that can used to set/get feature data."""
+        """Return a edges view that can used to set/get feature data.
+
+        >>> G = dgl.DGLGraph()
+        >>> G.add_nodes(3)
+        >>> G.add_edges([0, 1], 2)  # 0->2, 1->2
+
+        Get edges in graph G:
+
+        >>> G.edges()
+        (tensor([0, 1]), tensor([2, 2]))
+
+        Get feature dictionary of all edges:
+
+        >>> G.edges[:].data
+        {}
+
+        The above can be abbreviated as
+
+        >>> G.edata
+        {}
+
+        Init 2 edges with zero vector(len=4)
+
+        .. note:: Here we use pytorch syntax for demo. The general idea applies
+            to other frameworks with minor syntax change (e.g. replace
+            ``torch.tensor`` with ``mxnet.ndarray``).
+
+        >>> import torch as th
+        >>> G.edata['y'] = th.zeros((2, 4))
+        >>> G.edata
+        {'y' : tensor([[0., 0., 0., 0.],
+                       [0., 0., 0., 0.]])}
+
+        Use G.edges to get/set features for some edges.
+
+        >>> G.edges[1, 2].data['y'] = th.ones((1, 4))
+        >>> G.edata
+        {'y' : tensor([[0., 0., 0., 0.],
+                       [1., 1., 1., 1.]])}
+
+        See Also
+        --------
+        dgl.DGLGraph.edata
+        """
         return EdgeView(self)
 
     @property
     def edata(self):
-        """Return the data view of all the edges."""
+        """Return the data view of all the edges.
+
+        DGLGraph.data is an abbreviation of DGLGraph.edges[:].data
+
+        See Also
+        --------
+        dgl.DGLGraph.edges
+        """
         return self.edges[:].data
 
     def set_n_repr(self, hu, u=ALL, inplace=False):
@@ -1516,9 +1776,7 @@ class DGLGraph(object):
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        >>> import dgl
         >>> import torch as th
-
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)
         >>> g.ndata['x'] = th.ones(3, 1)
@@ -1577,7 +1835,6 @@ class DGLGraph(object):
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        >>> import dgl
         >>> import torch as th
 
         >>> g = dgl.DGLGraph()
@@ -1725,7 +1982,6 @@ class DGLGraph(object):
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)
@@ -1831,7 +2087,6 @@ class DGLGraph(object):
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)
@@ -1936,13 +2191,12 @@ class DGLGraph(object):
 
         Examples
         --------
+        Create a graph for demo.
+
         .. note:: Here we use pytorch syntax for demo. The general idea applies
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        Create a graph for demo.
-
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)
@@ -2034,13 +2288,12 @@ class DGLGraph(object):
 
         Examples
         --------
+        Create a graph for demo.
+
         .. note:: Here we use pytorch syntax for demo. The general idea applies
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        Create a graph for demo.
-
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)
@@ -2179,13 +2432,12 @@ class DGLGraph(object):
 
         Examples
         --------
+        Create a graph for demo.
+
         .. note:: Here we use pytorch syntax for demo. The general idea applies
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        Create a graph for demo.
-
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(4)
@@ -2258,13 +2510,12 @@ class DGLGraph(object):
 
         Examples
         --------
+        Create a graph for demo.
+
         .. note:: Here we use pytorch syntax for demo. The general idea applies
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        Create a graph for demo.
-
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(4)
@@ -2333,6 +2584,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(5)
         >>> G.add_edges([0, 1, 2, 3, 4], [1, 2, 3, 4, 0])   # 5-node cycle
@@ -2361,9 +2613,7 @@ class DGLGraph(object):
         nodes in the list.
 
         Equivalent to
-        ``
-        [self.subgraph(nodes_list) for nodes_list in nodes]
-        ``
+        ``[self.subgraph(nodes_list) for nodes_list in nodes]``
 
         Parameters
         ----------
@@ -2409,6 +2659,7 @@ class DGLGraph(object):
         Examples
         --------
         The following example uses PyTorch backend.
+
         >>> G = dgl.DGLGraph()
         >>> G.add_nodes(5)
         >>> G.add_edges([0, 1, 2, 3, 4], [1, 2, 3, 4, 0])   # 5-node cycle
@@ -2434,16 +2685,16 @@ class DGLGraph(object):
     def adjacency_matrix(self, transpose=False, ctx=F.cpu()):
         """Return the adjacency matrix representation of this graph.
 
-        By default, a row of returned adjacency matrix represents the destination
-        of an edge and the column represents the source.
+        By default, a row of returned adjacency matrix represents the
+        destination of an edge and the column represents the source.
 
-        When transpose is True, a row represents the source and a column represents
-        a destination.
+        When transpose is True, a row represents the source and a column
+        represents a destination.
 
         Parameters
         ----------
         transpose : bool, optional (default=False)
-            A flag to tranpose the returned adjacency matrix.
+            A flag to transpose the returned adjacency matrix.
         ctx : context, optional (default=cpu)
             The context of returned adjacency matrix.
 
@@ -2537,13 +2788,12 @@ class DGLGraph(object):
 
         Examples
         --------
+        Construct a graph object for demo.
+
         .. note:: Here we use pytorch syntax for demo. The general idea applies
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
 
-        Construct a graph object for demo.
-
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)
@@ -2599,13 +2849,12 @@ class DGLGraph(object):
 
         Examples
         --------
+        Construct a graph object for demo.
 
         .. note:: Here we use pytorch syntax for demo. The general idea applies
             to other frameworks with minor syntax change (e.g. replace
             ``torch.tensor`` with ``mxnet.ndarray``).
-            Construct a graph object for demo.
 
-        >>> import dgl
         >>> import torch as th
         >>> g = dgl.DGLGraph()
         >>> g.add_nodes(3)

python/dgl/init.py

@@ -8,6 +8,9 @@ __all__ = ['base_initializer', 'zero_initializer']
 def base_initializer(shape, dtype, ctx, range):
     """The function signature for feature initializer.
 
+    Any customized feature initializer should follow this signature (see
+    example below).
+
     Parameters
     ----------
     shape : tuple of int
@@ -21,9 +24,38 @@ def base_initializer(shape, dtype, ctx, range):
         The start id and the end id of the features to be initialized.
         The id could be node or edge id depending on the scenario.
         Note that the step is always None.
+
+    Examples
+    --------
+    If PyTorch is used as backend, the following code defines an feature
+    initializer that initializes tensor value to 1
+
+    >>> import torch
+    >>> import dgl
+    >>> def initializer(shape, dtype, ctx, range):
+    >>>     return torch.ones(shape, dtype=dtype, device=ctx)
+    >>> g = dgl.DGLGraph()
+    >>> g.set_n_initializer(initializer)
+
+    See Also
+    --------
+    dgl.DGLGraph.set_n_initializer
+    dgl.DGLGraph.set_e_initializer
     """
     raise NotImplementedError
 
 def zero_initializer(shape, dtype, ctx, range):
-    """Initialize zero-value features."""
+    """Zero feature initializer
+
+    Examples
+    --------
+    >>> import dgl
+    >>> g = dgl.DGLGraph()
+    >>> g.set_n_initializer(dgl.init.zero_initializer)
+
+    See Also
+    --------
+    dgl.DGLGraph.set_n_initializer
+    dgl.DGLGraph.set_e_initializer
+    """
     return F.zeros(shape, dtype, ctx)

python/dgl/traversal.py

@@ -19,12 +19,12 @@ def bfs_nodes_generator(graph, source, reversed=False):
     source : list, tensor of nodes
         Source nodes.
     reversed : bool, default False
-        If true, traverse following the in-edge direction.
+        If True, traverse following the in-edge direction.
 
     Returns
     -------
     list of node frontiers
-        Each node frontier is a list, tensor of nodes.
+        Each node frontier is a list or tensor of node ids.
 
     Examples
     --------
@@ -32,10 +32,10 @@ def bfs_nodes_generator(graph, source, reversed=False):
     ::
 
               2 - 4
-             / \ 
+             / \\
         0 - 1 - 3 - 5
 
-    >>> g = ... # the graph above
+    >>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
     >>> list(dgl.bfs_nodes_generator(g, 0))
     [tensor([0]), tensor([1]), tensor([2, 3]), tensor([4, 5])]
     """
@@ -58,12 +58,12 @@ def bfs_edges_generator(graph, source, reversed=False):
     source : list, tensor of nodes
         Source nodes.
     reversed : bool, default False
-        If true, traverse following the in-edge direction.
+        If True, traverse following the in-edge direction.
 
     Returns
     -------
     list of edge frontiers
-        Each edge frontier is a list, tensor of edges.
+        Each edge frontier is a list or tensor of edge ids.
 
     Examples
     --------
@@ -72,10 +72,10 @@ def bfs_edges_generator(graph, source, reversed=False):
     ::
 
               2 - 4
-             / \ 
+             / \\
         0 - 1 - 3 - 5
 
-    >>> g = ... # the graph above
+    >>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
     >>> list(dgl.bfs_edges_generator(g, 0))
     [tensor([0]), tensor([1, 2]), tensor([4, 5])]
     """
@@ -96,12 +96,12 @@ def topological_nodes_generator(graph, reversed=False):
     graph : DGLGraph
         The graph object.
     reversed : bool, optional
-        If true, traverse following the in-edge direction.
+        If True, traverse following the in-edge direction.
 
     Returns
     -------
     list of node frontiers
-        Each node frontier is a list, tensor of nodes.
+        Each node frontier is a list or tensor of node ids.
 
     Examples
     --------
@@ -109,10 +109,10 @@ def topological_nodes_generator(graph, reversed=False):
     ::
 
               2 - 4
-             / \ 
+             / \\
         0 - 1 - 3 - 5
 
-    >>> g = ... # the graph above
+    >>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
     >>> list(dgl.topological_nodes_generator(g))
     [tensor([0]), tensor([1]), tensor([2]), tensor([3, 4]), tensor([5])]
     """
@@ -138,12 +138,12 @@ def dfs_edges_generator(graph, source, reversed=False):
     source : list, tensor of nodes
         Source nodes.
     reversed : bool, optional
-        If true, traverse following the in-edge direction.
+        If True, traverse following the in-edge direction.
 
     Returns
     -------
     list of edge frontiers
-        Each edge frontier is a list, tensor of edges.
+        Each edge frontier is a list or tensor of edge ids.
 
     Examples
     --------
@@ -151,14 +151,14 @@ def dfs_edges_generator(graph, source, reversed=False):
     ::
 
               2 - 4
-             / \ 
+             / \\
         0 - 1 - 3 - 5
 
     Edge addition order [(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)]
 
-    >>> g = ... # the graph above
-    >>> list(dgl.dfs_edges_generator(g))
-    [tensor([0]), tensor([1]), tensor([4]), tensor([3]), tensor([5]), tensor([2])]
+    >>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
+    >>> list(dgl.dfs_edges_generator(g, 0))
+    [tensor([0]), tensor([1]), tensor([3]), tensor([5]), tensor([4])]
     """
     ghandle = graph._graph._handle
     source = utils.toindex(source).todgltensor()
@@ -179,10 +179,10 @@ def dfs_labeled_edges_generator(
 
     There are three labels: FORWARD(0), REVERSE(1), NONTREE(2)
 
-    A FORWARD edge is one in which `u` has been visisted but `v` has not. A
-    REVERSE edge is one in which both `u` and `v` have been visisted and the
+    A FORWARD edge is one in which `u` has been visised but `v` has not. A
+    REVERSE edge is one in which both `u` and `v` have been visited and the
     edge is in the DFS tree. A NONTREE edge is one in which both `u` and `v`
-    have been visisted but the edge is NOT in the DFS tree.
+    have been visited but the edge is NOT in the DFS tree.
 
     See ``networkx``'s :func:`dfs_labeled_edges
     <networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges>`
@@ -211,9 +211,25 @@ def dfs_labeled_edges_generator(
     Returns
     -------
     list of edge frontiers
-        Each edge frontier is a list, tensor of edges.
+        Each edge frontier is a list or tensor of edge ids.
     list of list of int
-        Label of each edge, organized in the same as the edge frontiers.
+        Label of each edge, organized in the same order as the edge frontiers.
+
+    Examples
+    --------
+    Given a graph (directed, edges from small node id to large):
+    ::
+
+              2 - 4
+             / \\
+        0 - 1 - 3 - 5
+
+    Edge addition order [(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)]
+
+    >>> g = dgl.DGLGraph([(0, 1), (1, 2), (1, 3), (2, 3), (2, 4), (3, 5)])
+    >>> list(dgl.dfs_labeled_edges_generator(g, 0, has_nontree_edge=True))
+    (tensor([0]), tensor([1]), tensor([3]), tensor([5]), tensor([4]), tensor([2])),
+    (tensor([0]), tensor([0]), tensor([0]), tensor([0]), tensor([0]), tensor([2]))
     """
     ghandle = graph._graph._handle
     source = utils.toindex(source).todgltensor()

python/dgl/view.py

@@ -12,18 +12,11 @@ NodeSpace = namedtuple('NodeSpace', ['data'])
 class NodeView(object):
     """A NodeView class to act as G.nodes for a DGLGraph.
 
-    Compared with networkx's NodeView, DGL's NodeView is not
-    a map. DGL's NodeView supports creating data view from
-    a given list of nodes.
+    Can be used to get a list of current nodes and get and set node data.
 
-    Parameters
-    ----------
-    graph : DGLGraph
-        The graph.
-
-    Examples
+    See Also
     --------
-    TBD
+    dgl.DGLGraph.nodes
     """
     __slots__ = ['_graph']
 
@@ -79,6 +72,14 @@ class NodeDataView(MutableMapping):
 EdgeSpace = namedtuple('EdgeSpace', ['data'])
 
 class EdgeView(object):
+    """A EdgeView class to act as G.edges for a DGLGraph.
+
+    Can be used to get a list of current edges and get and set edge data.
+
+    See Also
+    --------
+    dgl.DGLGraph.edges
+    """
     __slots__ = ['_graph']
 
     def __init__(self, graph):