"""
How Does DGL Represent A Graph?
===============================

By the end of this tutorial you will be able to:

-  Construct a graph in DGL from scratch.
-  Assign node and edge features to a graph.
-  Query properties of a DGL graph such as node degrees and
   connectivity.
-  Transform a DGL graph into another graph.
-  Load and save DGL graphs.

(Time estimate: 16 minutes)

"""


######################################################################
# DGL Graph Construction
# ----------------------
#
# DGL represents a directed graph as a ``DGLGraph`` object. You can
# construct a graph by specifying the number of nodes in the graph as well
# as the list of source and destination nodes.  Nodes in the graph have
# consecutive IDs starting from 0.
#
# For instance, the following code constructs a directed star graph with 5
# leaves. The center node's ID is 0. The edges go from the
# center node to the leaves.
#

import os
os.environ['DGLBACKEND'] = 'pytorch'
import numpy as np
import torch

import dgl

g = dgl.graph(([0, 0, 0, 0, 0], [1, 2, 3, 4, 5]), num_nodes=6)
# Equivalently, PyTorch LongTensors also work.
g = dgl.graph(
    (torch.LongTensor([0, 0, 0, 0, 0]), torch.LongTensor([1, 2, 3, 4, 5])),
    num_nodes=6,
)

# You can omit the number of nodes argument if you can tell the number of nodes from the edge list alone.
g = dgl.graph(([0, 0, 0, 0, 0], [1, 2, 3, 4, 5]))


######################################################################
# Edges in the graph have consecutive IDs starting from 0, and are
# in the same order as the list of source and destination nodes during
# creation.
#

# Print the source and destination nodes of every edge.
print(g.edges())


######################################################################
# .. note::
#
#    ``DGLGraph``'s are always directed to best fit the computation
#    pattern of graph neural networks, where the messages sent
#    from one node to the other are often different between both
#    directions. If you want to handle undirected graphs, you may consider
#    treating it as a bidirectional graph. See `Graph
#    Transformations`_ for an example of making
#    a bidirectional graph.
#


######################################################################
# Assigning Node and Edge Features to Graph
# -----------------------------------------
#
# Many graph data contain attributes on nodes and edges.
# Although the types of node and edge attributes can be arbitrary in real
# world, ``DGLGraph`` only accepts attributes stored in tensors (with
# numerical contents). Consequently, an attribute of all the nodes or
# edges must have the same shape. In the context of deep learning, those
# attributes are often called *features*.
#
# You can assign and retrieve node and edge features via ``ndata`` and
# ``edata`` interface.
#

# Assign a 3-dimensional node feature vector for each node.
g.ndata["x"] = torch.randn(6, 3)
# Assign a 4-dimensional edge feature vector for each edge.
g.edata["a"] = torch.randn(5, 4)
# Assign a 5x4 node feature matrix for each node.  Node and edge features in DGL can be multi-dimensional.
g.ndata["y"] = torch.randn(6, 5, 4)

print(g.edata["a"])


######################################################################
# .. note::
#
#    The vast development of deep learning has provided us many
#    ways to encode various types of attributes into numerical features.
#    Here are some general suggestions:
#
#    -  For categorical attributes (e.g. gender, occupation), consider
#       converting them to integers or one-hot encoding.
#    -  For variable length string contents (e.g. news article, quote),
#       consider applying a language model.
#    -  For images, consider applying a vision model such as CNNs.
#
#    You can find plenty of materials on how to encode such attributes
#    into a tensor in the `PyTorch Deep Learning
#    Tutorials <https://pytorch.org/tutorials/>`__.
#


######################################################################
# Querying Graph Structures
# -------------------------
#
# ``DGLGraph`` object provides various methods to query a graph structure.
#

print(g.num_nodes())
print(g.num_edges())
# Out degrees of the center node
print(g.out_degrees(0))
# In degrees of the center node - note that the graph is directed so the in degree should be 0.
print(g.in_degrees(0))


######################################################################
# Graph Transformations
# ---------------------
#


######################################################################
# DGL provides many APIs to transform a graph to another such as
# extracting a subgraph:
#

# Induce a subgraph from node 0, node 1 and node 3 from the original graph.
sg1 = g.subgraph([0, 1, 3])
# Induce a subgraph from edge 0, edge 1 and edge 3 from the original graph.
sg2 = g.edge_subgraph([0, 1, 3])


######################################################################
# You can obtain the node/edge mapping from the subgraph to the original
# graph by looking into the node feature ``dgl.NID`` or edge feature
# ``dgl.EID`` in the new graph.
#

# The original IDs of each node in sg1
print(sg1.ndata[dgl.NID])
# The original IDs of each edge in sg1
print(sg1.edata[dgl.EID])
# The original IDs of each node in sg2
print(sg2.ndata[dgl.NID])
# The original IDs of each edge in sg2
print(sg2.edata[dgl.EID])


######################################################################
# ``subgraph`` and ``edge_subgraph`` also copies the original features
# to the subgraph:
#

# The original node feature of each node in sg1
print(sg1.ndata["x"])
# The original edge feature of each node in sg1
print(sg1.edata["a"])
# The original node feature of each node in sg2
print(sg2.ndata["x"])
# The original edge feature of each node in sg2
print(sg2.edata["a"])


######################################################################
# Another common transformation is to add a reverse edge for each edge in
# the original graph with ``dgl.add_reverse_edges``.
#
# .. note::
#
#    If you have an undirected graph, it is better to convert it
#    into a bidirectional graph first via adding reverse edges.
#

newg = dgl.add_reverse_edges(g)
print(newg.edges())


######################################################################
# Loading and Saving Graphs
# -------------------------
#
# You can save a graph or a list of graphs via ``dgl.save_graphs`` and
# load them back with ``dgl.load_graphs``.
#

# Save graphs
dgl.save_graphs("graph.dgl", g)
dgl.save_graphs("graphs.dgl", [g, sg1, sg2])

# Load graphs
(g,), _ = dgl.load_graphs("graph.dgl")
print(g)
(g, sg1, sg2), _ = dgl.load_graphs("graphs.dgl")
print(g)
print(sg1)
print(sg2)


######################################################################
# What’s next?
# ------------
#
# -  See
#    :ref:`here <apigraph-querying-graph-structure>`
#    for a list of graph structure query APIs.
# -  See
#    :ref:`here <api-subgraph-extraction>`
#    for a list of subgraph extraction routines.
# -  See
#    :ref:`here <api-transform>`
#    for a list of graph transformation routines.
# -  API reference of :func:`dgl.save_graphs`
#    and
#    :func:`dgl.load_graphs`
#


# Thumbnail credits: Wikipedia
# sphinx_gallery_thumbnail_path = '_static/blitz_2_dglgraph.png'
