[Doc] docstring for BatchedDGLGraph + some fix (#184)

* Update doc

* Update batched_graph.py

* Fix

* Fix

* Fix

* Fix

docs/source/api/python/batch.rst

-BatchedDGLGraph
-===============
+.. _apibatch:
 
-.. automodule:: dgl.batched_graph
+BatchedDGLGraph -- Enable batched graph operations
+==================================================
+
+.. currentmodule:: dgl
 .. autoclass:: BatchedDGLGraph
 
+Merge and decompose
+-------------------
+
 .. autosummary::
     :toctree: ../../generated/
 
     batch
     unbatch
+
+Query batch summary
+----------------------
+
+.. autosummary::
+    :toctree: ../../generated/
+
+    BatchedDGLGraph.batch_size
+    BatchedDGLGraph.batch_num_nodes
+    BatchedDGLGraph.batch_num_edges
+
+Graph Readout
+-------------
+
+.. autosummary::
+    :toctree: ../../generated/
+
     sum_nodes
     sum_edges
     mean_nodes

python/dgl/batched_graph.py

@@ -2,6 +2,7 @@
 from __future__ import absolute_import
 
 import numpy as np
+from collections import Iterable
 
 from .base import ALL, is_all
 from .frame import FrameRef, Frame
@@ -16,18 +17,165 @@ __all__ = ['BatchedDGLGraph', 'batch', 'unbatch', 'split',
 class BatchedDGLGraph(DGLGraph):
     """Class for batched DGL graphs.
 
-    The batched graph is read-only.
+    A :class:`BatchedDGLGraph` basically merges a list of small graphs into a giant
+    graph so that one can perform message passing and readout over a batch of graphs
+    simultaneously.
+
+    The nodes and edges are re-indexed with a new id in the batched graph with the
+    rule below:
+
+           |   Graph 1  |         Graph 2          |...| Graph k
+    --------------------------------------------------------------------------------
+    raw id | 0, ..., N1 |   0   , ...,      N2     |...| ..., Nk
+    new id | 0, ..., N1 | N1 + 1, ..., N1 + N2 + 1 |...| ..., N1 + ... + Nk + k - 1
+
+    The batched graph is read-only, i.e. one cannot further add nodes and edges.
+    A RuntimeError will be raised if one attempts.
+
+    To modify the features in :class:`BatchedDGLGraph` has no effect on the original
+    graphs. See the examples below about how to work around.
 
     Parameters
     ----------
     graph_list : iterable
-        A list of DGLGraphs to be batched.
-    node_attrs : str or iterable
-        The node attributes to also be batched.
-    edge_attrs : str or iterable, optional
-        The edge attributes to also be batched.
+        A collection of :class:`~dgl.DGLGraphs` to be batched.
+    node_attrs : None, str or iterable, optional
+        The node attributes to be batched. If ``None``, the :class:`BatchedDGLGraph` object
+        will not have any node attributes. By default, all node attributes will be batched.
+        An error will be raised if graphs having nodes have different attributes. If ``str``
+        or ``iterable``, this should specify exactly what node attributes to be batched.
+    edge_attrs : None, str or iterable, optional
+        Same as for the case of :attr:`node_attrs`
+
+    Examples
+    --------
+    Create two :class:`~dgl.DGLGraphs` objects.
+
+    **Instantiation:**
+
+    >>> import dgl
+    >>> import torch as th
+    >>> g1 = dgl.DGLGraph()
+    >>> g1.add_nodes(2)                                # Add 2 nodes
+    >>> g1.add_edge(0, 1)                              # Add edge 0 -> 1
+    >>> g1.ndata['hv'] = th.tensor([[0.], [1.]])       # Initialize node features
+    >>> g1.edata['he'] = th.tensor([[0.]])             # Initialize edge features
+
+    >>> g2 = dgl.DGLGraph()
+    >>> g2.add_nodes(3)                                # Add 3 nodes
+    >>> g2.add_edges([0, 2], [1, 1])                   # Add edges 0 -> 1, 2 -> 1
+    >>> g2.ndata['hv'] = th.tensor([[2.], [3.], [4.]]) # Initialize node features
+    >>> g2.edata['he'] = th.tensor([[1.], [2.]])       # Initialize edge features
+
+    Merge two :class:`~dgl.DGLGraphs` objects into one :class:`BatchedDGLGraph` object.
+    When merging a list of graphs, we can choose to include only a subset of the attributes.
+
+    >>> bg = dgl.batch([g1, g2], edge_attrs=None)
+    >>> bg.edata
+    {}
+
+    Below one can see that the nodes are re-indexed. The edges are re-indexed in
+    the same way.
+
+    >>> bg.nodes()
+    tensor([0, 1, 2, 3, 4])
+    >>> bg.ndata['hv']
+    tensor([[0.],
+            [1.],
+            [2.],
+            [3.],
+            [4.]])
+
+    **Property:**
+
+    We can still get a brief summary of the graphs that constitute the batched graph.
+    >>> bg.batch_size
+    2
+    >>> bg.batch_num_nodes
+    [2, 3]
+    >>> bg.batch_num_edges
+    [1, 2]
+
+    **Readout:**
+
+    Another common demand for graph neural networks is graph readout, which is a
+    function that takes in the node attributes and/or edge attributes for a graph
+    and outputs a vector summarizing the information in the graph. `BatchedDGLGraph`
+    also supports performing readout for a batch of graphs at once.
+
+    Below we take the built-in readout function :func:`sum_nodes` as an example, which
+    sums a particular node attribute for each graph.
+
+    >>> dgl.sum_nodes(bg, 'hv') # Sum the node attribute 'hv' for each graph.
+    tensor([[1.],               # 0 + 1
+            [9.]])              # 2 + 3 + 4
+
+    **Message passing:**
+
+    For message passing and related operations, :class:`BatchedDGLGraph` acts exactly
+    the same as :class:`~dgl.DGLGraphs`.
+
+    **Update Attributes:**
+
+    Updating the attributes of the batched graph has no effect on the original graphs.
+
+    >>> bg.edata['he'] = th.zeros(3, 2)
+    >>> g2.edata['he']
+    tensor([[1.],
+            [2.]])}
+
+    Instead, we can decompose the batched graph back into a list of graphs and use them
+    to replace the original graphs.
+    >>> g1, g2 = dgl.unbatch(bg)    # returns a list of DGLGraphs
+    >>> g2.edata['he']
+    tensor([[0., 0.],
+            [0., 0.]])}
     """
     def __init__(self, graph_list, node_attrs, edge_attrs):
+
+        def _get_num_item_and_attr_types(g, mode):
+            if mode == 'node':
+                num_items = g.number_of_nodes()
+                attr_types = set(g.node_attr_schemes().keys())
+            elif mode == 'edge':
+                num_items = g.number_of_edges()
+                attr_types = set(g.edge_attr_schemes().keys())
+            return num_items, attr_types
+
+        def _init_attrs(attrs, mode):
+            if attrs is None:
+                return []
+            elif is_all(attrs):
+                attrs = set()
+                # Check if at least a graph has mode items and associated features.
+                for i in range(len(graph_list)):
+                    g = graph_list[i]
+                    g_num_items, g_attrs = _get_num_item_and_attr_types(g, mode)
+                    if g_num_items > 0 and len(g_attrs) > 0:
+                        attrs = g_attrs
+                        ref_g_index = i
+                        break
+                # Check if all the graphs with mode items have the same associated features.
+                if len(attrs) > 0:
+                    for i in range(len(graph_list)):
+                        g = graph_list[i]
+                        g_num_items, g_attrs = _get_num_item_and_attr_types(g, mode)
+                        if g_attrs != attrs and g_num_items > 0:
+                            raise ValueError('Expect graph {} and {} to have the same {} '
+                                             'attributes when {}_attrs=ALL, got {} and '
+                                             '{}'.format(ref_g_index, i, mode, mode, attrs, g_attrs))
+                return attrs
+            elif isinstance(attrs, str):
+                return [attrs]
+            elif isinstance(attrs, Iterable):
+                return attrs
+            else:
+                raise ValueError('Expected {} attrs to be of type None str or Iterable, '
+                                 'got type {}'.format(mode, type(attrs)))
+
+        node_attrs = _init_attrs(node_attrs, 'node')
+        edge_attrs = _init_attrs(edge_attrs, 'edge')
+
         # create batched graph index
         batched_index = gi.disjoint_union([g._graph for g in graph_list])
         # create batched node and edge frames
@@ -70,17 +218,32 @@ class BatchedDGLGraph(DGLGraph):
 
     @property
     def batch_size(self):
-        """Number of graphs in this batch."""
+        """Number of graphs in this batch.
+
+        Returns
+        -------
+        int
+            Number of graphs in this batch."""
         return self._batch_size
 
     @property
     def batch_num_nodes(self):
-        """Number of nodes of each graph in this batch."""
+        """Number of nodes of each graph in this batch.
+
+        Returns
+        -------
+        list
+            Number of nodes of each graph in this batch."""
         return self._batch_num_nodes
 
     @property
     def batch_num_edges(self):
-        """Number of edges of each graph in this batch."""
+        """Number of edges of each graph in this batch.
+
+        Returns
+        -------
+        list
+            Number of edges of each graph in this batch."""
         return self._batch_num_edges
 
     # override APIs
@@ -113,20 +276,31 @@ def split(graph_batch, num_or_size_splits):
     pass
 
 def unbatch(graph):
-    """Unbatch and return the list of graphs in this batch.
+    """Return the list of graphs in this batch.
 
     Parameters
     ----------
     graph : BatchedDGLGraph
         The batched graph.
 
+    Returns
+    -------
+    list
+        A list of :class:`~dgl.DGLGraphs` objects whose attributes are obtained
+        by partitioning the attributes of the :attr:`graph`. The length of the
+        list is the same as the batch size of :attr:`graph`.
+
     Notes
     -----
-    Unbatching will partition each field tensor of the batched graph into
-    smaller partitions.  This is usually wasteful.
+    Unbatching will break each field tensor of the batched graph into smaller
+    partitions.
 
-    For simpler tasks such as node/edge state aggregation by example,
-    try to use readout functions.
+    For simpler tasks such as node/edge state aggregation, try to use
+    readout functions.
+
+    See Also
+    --------
+    batch
     """
     assert isinstance(graph, BatchedDGLGraph)
     bsize = graph.batch_size
@@ -149,37 +323,31 @@ def unbatch(graph):
                      edge_frame=edge_frames[i]) for i in range(bsize)]
 
 def batch(graph_list, node_attrs=ALL, edge_attrs=ALL):
-    """Batch a list of DGLGraphs into one single graph.
-
-    Once batch is called, the structure of both merged graph and graphs in graph_list
-    must not be mutated, or unbatch's behavior will be undefined.
+    """Batch a collection of :class:`~dgl.DGLGraphs` and return a
+    :class:`BatchedDGLGraph` object that is independent of the :attr:`graph_list`.
 
     Parameters
     ----------
     graph_list : iterable
-        A list of DGLGraphs to be batched.
-    node_attrs : str or iterable, optional
-        The node attributes to also be batched. Specify None to not batch any attributes.
-    edge_attrs : str or iterable, optional
-        The edge attributes to also be batched. Specify None to not batch any attributes.
+        A collection of :class:`~dgl.DGLGraphs` to be batched.
+    node_attrs : None, str or iterable
+        The node attributes to be batched. If ``None``, the :class:`BatchedDGLGraph`
+        object will not have any node attributes. By default, all node attributes will
+        be batched. If ``str`` or iterable, this should specify exactly what node
+        attributes to be batched.
+    edge_attrs : None, str or iterable, optional
+        Same as for the case of :attr:`node_attrs`
 
     Returns
     -------
-    newgrh: BatchedDGLGraph
+    BatchedDGLGraph
         one single batched graph
+
+    See Also
+    --------
+    BatchedDGLGraph
+    unbatch
     """
-    if node_attrs is None:
-        node_attrs = []
-    elif is_all(node_attrs):
-        node_attrs = graph_list[0].node_attr_schemes()
-    elif isinstance(node_attrs, str):
-        node_attrs = [node_attrs]
-    if edge_attrs is None:
-        edge_attrs = []
-    elif is_all(edge_attrs):
-        edge_attrs = graph_list[0].edge_attr_schemes()
-    elif isinstance(edge_attrs, str):
-        edge_attrs = [edge_attrs]
     return BatchedDGLGraph(graph_list, node_attrs, edge_attrs)
 
 
@@ -212,8 +380,8 @@ def _sum_on(graph, on, input, weight):
         return F.sum(input, 0)
 
 def sum_nodes(graph, input, weight=None):
-    """Sums all the values of node field `input` in `graph`, optionally
-    multiplies the field by a scalar node field `weight`.
+    """Sums all the values of node field :attr:`input` in :attr:`graph`, optionally
+    multiplies the field by a scalar node field :attr`weight`.
 
     Parameters
     ----------
@@ -221,8 +389,11 @@ def sum_nodes(graph, input, weight=None):
         The graph
     input : str
         The input field
-    weight : optional, str
-        The weight field.  Default is all 1 (i.e. not weighting)
+    weight : str, optional
+        The weight field. If None, no weighting will be performed,
+        otherwise, weight each node feature with field :attr:`input`.
+        for summation. The weight feature associated in the :attr:`graph`
+        should be a tensor of shape [graph.number_of_nodes(), 1].
 
     Returns
     -------
@@ -231,17 +402,53 @@ def sum_nodes(graph, input, weight=None):
 
     Notes
     -----
-    If graph is a BatchedDGLGraph, a stacked tensor is returned instead,
-    i.e. having an extra first dimension.
-    Each row of the stacked tensor contains the readout result of
+    If graph is a :class:`BatchedDGLGraph` object, a stacked tensor is
+    returned instead, i.e. having an extra first dimension.
+    Each row of the stacked tensor contains the readout result of the
     corresponding example in the batch. If an example has no nodes,
     a zero tensor with the same shape is returned at the corresponding row.
+
+    Examples
+    --------
+
+    >>> import dgl
+    >>> import torch as th
+
+    Create two :class:`~dgl.DGLGraphs` objects and initialize their
+    node features.
+
+    >>> g1 = dgl.DGLGraph()                           # Graph 1
+    >>> g1.add_nodes(2)
+    >>> g1.ndata['h'] = th.tensor([[1.], [2.]])
+    >>> g1.ndata['w'] = th.tensor([[3.], [6.]])
+
+    >>> g2 = dgl.DGLGraph()                           # Graph 2
+    >>> g2.add_nodes(3)
+    >>> g2.ndata['h'] = th.tensor([[1.], [2.], [3.]])
+
+    Sum over node attribute 'h' without weighting for each graph in a
+    batched graph.
+    >>> bg = dgl.batch([g1, g2], node_attrs='h')
+    >>> dgl.sum_nodes(bg, 'h')
+    tensor([[3.],   # 1 + 2
+            [6.]])  # 1 + 2 + 3
+
+    Sum node attribute 'h' with weight from node attribute 'w' for a single
+    graph.
+    >>> dgl.sum_nodes(g1, 'h', 'w')
+    tensor([15.])   # 1 * 3 + 2 * 6
+
+    See Also
+    --------
+    mean_nodes
+    sum_edges
+    mean_edges
     """
     return _sum_on(graph, 'nodes', input, weight)
 
 def sum_edges(graph, input, weight=None):
-    """Sums all the values of edge field `input` in `graph`, optionally
-    multiplies the field by a scalar edge field `weight`.
+    """Sums all the values of edge field :attr:`input` in :attr:`graph`,
+    optionally multiplies the field by a scalar edge field :attr:`weight`.
 
     Parameters
     ----------
@@ -249,8 +456,11 @@ def sum_edges(graph, input, weight=None):
         The graph
     input : str
         The input field
-    weight : optional, str
-        The weight field.  Default is all 1 (i.e. not weighting)
+    weight : str, optional
+        The weight field. If None, no weighting will be performed,
+        otherwise, weight each edge feature with field :attr:`input`.
+        for summation. The weight feature associated in the :attr:`graph`
+        should be a tensor of shape [graph.number_of_edges(), 1].
 
     Returns
     -------
@@ -259,11 +469,49 @@ def sum_edges(graph, input, weight=None):
 
     Notes
     -----
-    If graph is a BatchedDGLGraph, a stacked tensor is returned instead,
-    i.e. having an extra first dimension.
-    Each row of the stacked tensor contains the readout result of
+    If graph is a :class:`BatchedDGLGraph` object, a stacked tensor is
+    returned instead, i.e. having an extra first dimension.
+    Each row of the stacked tensor contains the readout result of the
     corresponding example in the batch.  If an example has no edges,
     a zero tensor with the same shape is returned at the corresponding row.
+
+    Examples
+    --------
+
+    >>> import dgl
+    >>> import torch as th
+
+    Create two :class:`~dgl.DGLGraphs` objects and initialize their
+    edge features.
+
+    >>> g1 = dgl.DGLGraph()                           # Graph 1
+    >>> g1.add_nodes(2)
+    >>> g1.add_edges([0, 1], [1, 0])
+    >>> g1.edata['h'] = th.tensor([[1.], [2.]])
+    >>> g1.edata['w'] = th.tensor([[3.], [6.]])
+
+    >>> g2 = dgl.DGLGraph()                           # Graph 2
+    >>> g2.add_nodes(3)
+    >>> g2.add_edges([0, 1, 2], [1, 2, 0])
+    >>> g2.edata['h'] = th.tensor([[1.], [2.], [3.]])
+
+    Sum over edge attribute 'h' without weighting for each graph in a
+    batched graph.
+    >>> bg = dgl.batch([g1, g2], edge_attrs='h')
+    >>> dgl.sum_edges(bg, 'h')
+    tensor([[3.],   # 1 + 2
+            [6.]])  # 1 + 2 + 3
+
+    Sum edge attribute 'h' with weight from edge attribute 'w' for a single
+    graph.
+    >>> dgl.sum_edges(g1, 'h', 'w')
+    tensor([15.])   # 1 * 3 + 2 * 6
+
+    See Also
+    --------
+    sum_nodes
+    mean_nodes
+    mean_edges
     """
     return _sum_on(graph, 'edges', input, weight)
 
@@ -301,8 +549,8 @@ def _mean_on(graph, on, input, weight):
             return y
 
 def mean_nodes(graph, input, weight=None):
-    """Averages all the values of node field `input` in `graph`, optionally
-    multiplies the field by a scalar node field `weight`.
+    """Averages all the values of node field :attr:`input` in :attr:`graph`,
+    optionally multiplies the field by a scalar node field :attr:`weight`.
 
     Parameters
     ----------
@@ -310,8 +558,11 @@ def mean_nodes(graph, input, weight=None):
         The graph
     input : str
         The input field
-    weight : optional, str
-        The weight field.  Default is all 1 (i.e. not weighting)
+    weight : str, optional
+        The weight field. If None, no weighting will be performed,
+        otherwise, weight each node feature with field :attr:`input`.
+        for calculating mean. The weight feature associated in the :attr:`graph`
+        should be a tensor of shape [graph.number_of_nodes(), 1].
 
     Returns
     -------
@@ -320,17 +571,53 @@ def mean_nodes(graph, input, weight=None):
 
     Notes
     -----
-    If graph is a BatchedDGLGraph, a stacked tensor is returned instead,
-    i.e. having an extra first dimension.
+    If graph is a :class:`BatchedDGLGraph` object, a stacked tensor is
+    returned instead, i.e. having an extra first dimension.
     Each row of the stacked tensor contains the readout result of
     corresponding example in the batch. If an example has no nodes,
     a zero tensor with the same shape is returned at the corresponding row.
+
+    Examples
+    --------
+
+    >>> import dgl
+    >>> import torch as th
+
+    Create two :class:`~dgl.DGLGraphs` objects and initialize their
+    node features.
+
+    >>> g1 = dgl.DGLGraph()                           # Graph 1
+    >>> g1.add_nodes(2)
+    >>> g1.ndata['h'] = th.tensor([[1.], [2.]])
+    >>> g1.ndata['w'] = th.tensor([[3.], [6.]])
+
+    >>> g2 = dgl.DGLGraph()                           # Graph 2
+    >>> g2.add_nodes(3)
+    >>> g2.ndata['h'] = th.tensor([[1.], [2.], [3.]])
+
+    Average over node attribute 'h' without weighting for each graph in a
+    batched graph.
+    >>> bg = dgl.batch([g1, g2], node_attrs='h')
+    >>> dgl.mean_nodes(bg, 'h')
+    tensor([[1.5000],    # (1 + 2) / 2
+            [2.0000]])   # (1 + 2 + 3) / 3
+
+    Sum node attribute 'h' with normalized weight from node attribute 'w'
+    for a single graph.
+    >>> dgl.mean_nodes(g1, 'h', 'w') # h1 * (w1 / (w1 + w2)) + h2 * (w2 / (w1 + w2))
+    tensor([1.6667])                 # 1 * (3 / (3 + 6)) + 2 * (6 / (3 + 6))
+
+    See Also
+    --------
+    sum_nodes
+    sum_edges
+    mean_edges
     """
     return _mean_on(graph, 'nodes', input, weight)
 
 def mean_edges(graph, input, weight=None):
-    """Averages all the values of edge field `input` in `graph`, optionally
-    multiplies the field by a scalar edge field `weight`.
+    """Averages all the values of edge field :attr:`input` in :attr:`graph`,
+    optionally multiplies the field by a scalar edge field :attr:`weight`.
 
     Parameters
     ----------
@@ -339,7 +626,10 @@ def mean_edges(graph, input, weight=None):
     input : str
         The input field
     weight : optional, str
-        The weight field.  Default is all 1 (i.e. not weighting)
+        The weight field. If None, no weighting will be performed,
+        otherwise, weight each edge feature with field :attr:`input`.
+        for calculating mean. The weight feature associated in the :attr:`graph`
+        should be a tensor of shape [graph.number_of_edges(), 1].
 
     Returns
     -------
@@ -348,10 +638,48 @@ def mean_edges(graph, input, weight=None):
 
     Notes
     -----
-    If graph is a BatchedDGLGraph, a stacked tensor is returned instead,
-    i.e. having an extra first dimension.
+    If graph is a :class:`BatchedDGLGraph` object, a stacked tensor is
+    returned instead, i.e. having an extra first dimension.
     Each row of the stacked tensor contains the readout result of
     corresponding example in the batch.  If an example has no edges,
     a zero tensor with the same shape is returned at the corresponding row.
+
+    Examples
+    --------
+
+    >>> import dgl
+    >>> import torch as th
+
+    Create two :class:`~dgl.DGLGraphs` objects and initialize their
+    edge features.
+
+    >>> g1 = dgl.DGLGraph()                           # Graph 1
+    >>> g1.add_nodes(2)
+    >>> g1.add_edges([0, 1], [1, 0])
+    >>> g1.edata['h'] = th.tensor([[1.], [2.]])
+    >>> g1.edata['w'] = th.tensor([[3.], [6.]])
+
+    >>> g2 = dgl.DGLGraph()                           # Graph 2
+    >>> g2.add_nodes(3)
+    >>> g2.add_edges([0, 1, 2], [1, 2, 0])
+    >>> g2.edata['h'] = th.tensor([[1.], [2.], [3.]])
+
+    Average over edge attribute 'h' without weighting for each graph in a
+    batched graph.
+    >>> bg = dgl.batch([g1, g2], edge_attrs='h')
+    >>> dgl.mean_edges(bg, 'h')
+    tensor([[1.5000],    # (1 + 2) / 2
+            [2.0000]])   # (1 + 2 + 3) / 3
+
+    Sum edge attribute 'h' with normalized weight from edge attribute 'w'
+    for a single graph.
+    >>> dgl.mean_edges(g1, 'h', 'w') # h1 * (w1 / (w1 + w2)) + h2 * (w2 / (w1 + w2))
+    tensor([1.6667])                 # 1 * (3 / (3 + 6)) + 2 * (6 / (3 + 6))
+
+    See Also
+    --------
+    sum_nodes
+    mean_nodes
+    sum_edges
     """
     return _mean_on(graph, 'edges', input, weight)