mirrors / dmlc--dgl

项目文件夹

dmlc--dgl
文件
0.3.x
T
Quan (Andy) Gan 52d4535b61 [Hetero][RFC] Heterogeneous graph Python interfaces & Message Passing (#752) 
* moving heterograph index to another file

* node view

* python interfaces

* heterograph init

* bug fixes

* docstring for readonly

* more docstring

* unit tests & lint

* oops

* oops x2

* removed node/edge addition

* addressed comments

* lint

* rw on frames with one node/edge type

* homograph with underlying heterograph demo

* view is not necessary

* bugfix

* replace

* scheduler, builtins not working yet

* moving bipartite.h to header

* moving back bipartite to bipartite.h

* oops

* asbits and copyto for bipartite

* tested update_all and send_and_recv

* lightweight node & edge type retrieval

* oops

* sorry

* removing obsolete code

* oops

* lint

* various bug fixes & more tests

* UDF tests

* multiple type number_of_nodes and number_of_edges

* docstring fixes

* more tests

* going for dict in initialization

* lint

* updated api as per discussions

* lint

* bug

* bugfix

* moving back bipartite impl to cc

* note on views

* fix
2019-08-23 15:45:37 -04:00

2563 行
86 KiB
Python
原始文件 永久链接 Blame 文件历史

"""Classes for heterogeneous graphs."""
from collections import defaultdict
import networkx as nx
import scipy.sparse as ssp
from . import heterograph_index, graph_index
from . import utils
from . import backend as F
from . import init
from .runtime import ir, scheduler, Runtime
from .frame import Frame, FrameRef
from .view import HeteroNodeView, HeteroNodeDataView, HeteroEdgeView, HeteroEdgeDataView
from .base import ALL, is_all, DGLError
__all__ = ['DGLHeteroGraph']
# TODO: depending on the progress of unifying DGLGraph and Bipartite, we may or may not
# need the code of heterogeneous graph views.
# pylint: disable=unnecessary-pass
class DGLBaseHeteroGraph(object):
"""Base Heterogeneous graph class.
Parameters
----------
graph : graph index, optional
The graph index
ntypes : list[str]
The node type names
etypes : list[str]
The edge type names
_ntypes_invmap, _etypes_invmap, _view_ntype_idx, _view_etype_idx :
Internal arguments
"""
# pylint: disable=unused-argument
def __init__(self, graph, ntypes, etypes,
_ntypes_invmap=None, _etypes_invmap=None,
_view_ntype_idx=None, _view_etype_idx=None):
super(DGLBaseHeteroGraph, self).__init__()
self._graph = graph
self._ntypes = ntypes
self._etypes = etypes
# inverse mapping from ntype str to int
self._ntypes_invmap = _ntypes_invmap or \
{ntype: i for i, ntype in enumerate(ntypes)}
# inverse mapping from etype str to int
self._etypes_invmap = _etypes_invmap or \
{etype: i for i, etype in enumerate(etypes)}
# Indicates which node/edge type (int) it is viewing.
self._view_ntype_idx = _view_ntype_idx
self._view_etype_idx = _view_etype_idx
self._cache = {}
def _create_view(self, ntype_idx, etype_idx):
return DGLBaseHeteroGraph(
self._graph, self._ntypes, self._etypes,
self._ntypes_invmap, self._etypes_invmap,
ntype_idx, etype_idx)
@property
def is_node_type_view(self):
"""Whether this is a node type view of a heterograph."""
return self._view_ntype_idx is not None
@property
def is_edge_type_view(self):
"""Whether this is an edge type view of a heterograph."""
return self._view_etype_idx is not None
@property
def is_view(self):
"""Whether this is a node/view of a heterograph."""
return self.is_node_type_view or self.is_edge_type_view
@property
def all_node_types(self):
"""Return the list of node types of the entire heterograph."""
return self._ntypes
@property
def all_edge_types(self):
"""Return the list of edge types of the entire heterograph."""
return self._etypes
@property
def metagraph(self):
"""Return the metagraph as networkx.MultiDiGraph.
The nodes are labeled with node type names.
The edges have their keys holding the edge type names.
"""
nx_graph = self._graph.metagraph.to_networkx()
nx_return_graph = nx.MultiDiGraph()
for u_v in nx_graph.edges:
etype = self._etypes[nx_graph.edges[u_v]['id']]
srctype = self._ntypes[u_v[0]]
dsttype = self._ntypes[u_v[1]]
assert etype[0] == srctype
assert etype[2] == dsttype
nx_return_graph.add_edge(srctype, dsttype, etype[1])
return nx_return_graph
def _endpoint_types(self, etype):
"""Return the source and destination node type (int) of given edge
type (int)."""
return self._graph.metagraph.find_edge(etype)
def _node_types(self):
if self.is_node_type_view:
return [self._view_ntype_idx]
elif self.is_edge_type_view:
srctype_idx, dsttype_idx = self._endpoint_types(self._view_etype_idx)
return [srctype_idx, dsttype_idx] if srctype_idx != dsttype_idx else [srctype_idx]
else:
return range(len(self._ntypes))
def node_types(self):
"""Return the list of node types appearing in the current view.
Returns
-------
list[str]
List of node types
Examples
--------
Getting all node types.
>>> g.node_types()
['user', 'game', 'developer']
Getting all node types appearing in the subgraph induced by "users"
(which should only yield "user").
>>> g['user'].node_types()
['user']
The node types appearing in subgraph induced by "plays" relationship,
which should only give "user" and "game".
>>> g['plays'].node_types()
['user', 'game']
"""
ntypes = self._node_types()
if isinstance(ntypes, range):
# assuming that the range object always covers the entire node type list
return self._ntypes
else:
return [self._ntypes[i] for i in ntypes]
def _edge_types(self):
if self.is_node_type_view:
etype_indices = self._graph.metagraph.edge_id(
self._view_ntype_idx, self._view_ntype_idx)
return etype_indices
elif self.is_edge_type_view:
return [self._view_etype_idx]
else:
return range(len(self._etypes))
def edge_types(self):
"""Return the list of edge types appearing in the current view.
Returns
-------
list[str]
List of edge types
Examples
--------
Getting all edge types.
>>> g.edge_types()
['follows', 'plays', 'develops']
Getting all edge types appearing in subgraph induced by "users".
>>> g['user'].edge_types()
['follows']
The edge types appearing in subgraph induced by "plays" relationship,
which should only give "plays".
>>> g['plays'].edge_types()
['plays']
"""
etypes = self._edge_types()
if isinstance(etypes, range):
return self._etypes
else:
return [self._etypes[i] for i in etypes]
@property
@utils.cached_member('_cache', '_current_ntype_idx')
def _current_ntype_idx(self):
"""Checks the uniqueness of node type in the view and get the index
of that node type.
This allows reading/writing node frame data.
"""
node_types = self._node_types()
assert len(node_types) == 1, "only available for subgraphs with one node type"
return node_types[0]
@property
@utils.cached_member('_cache', '_current_etype_idx')
def _current_etype_idx(self):
"""Checks the uniqueness of edge type in the view and get the index
of that edge type.
This allows reading/writing edge frame data and message passing routines.
"""
edge_types = self._edge_types()
assert len(edge_types) == 1, "only available for subgraphs with one edge type"
return edge_types[0]
@property
@utils.cached_member('_cache', '_current_srctype_idx')
def _current_srctype_idx(self):
"""Checks the uniqueness of edge type in the view and get the index
of the source type.
This allows reading/writing edge frame data and message passing routines.
"""
srctype_idx, _ = self._endpoint_types(self._current_etype_idx)
return srctype_idx
@property
@utils.cached_member('_cache', '_current_dsttype_idx')
def _current_dsttype_idx(self):
"""Checks the uniqueness of edge type in the view and get the index
of the destination type.
This allows reading/writing edge frame data and message passing routines.
"""
_, dsttype_idx = self._endpoint_types(self._current_etype_idx)
return dsttype_idx
def number_of_nodes(self, ntype):
"""Return the number of nodes of the given type in the heterograph.
Parameters
----------
ntype : str
The node type
Returns
-------
int
The number of nodes
Examples
--------
>>> g['user'].number_of_nodes()
3
"""
return self._graph.number_of_nodes(self._ntypes_invmap[ntype])
def _number_of_src_nodes(self):
"""Return number of source nodes (only used in scheduler)"""
return self._graph.number_of_nodes(self._current_srctype_idx)
def _number_of_dst_nodes(self):
"""Return number of destination nodes (only used in scheduler)"""
return self._graph.number_of_nodes(self._current_dsttype_idx)
@property
def is_multigraph(self):
"""True if the graph is a multigraph, False otherwise.
"""
assert not self.is_view, 'not supported on views'
return self._graph.is_multigraph()
@property
def is_readonly(self):
"""True if the graph is readonly, False otherwise.
"""
return self._graph.is_readonly()
def _number_of_edges(self):
"""Return number of edges in the current view (only used for scheduler)"""
return self._graph.number_of_edges(self._current_etype_idx)
def number_of_edges(self, etype):
"""Return the number of edges of the given type in the heterograph.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
Returns
-------
int
The number of edges
Examples
--------
>>> g.number_of_edges(('user', 'plays', 'game'))
4
"""
return self._graph.number_of_edges(self._etypes_invmap[etype])
def has_node(self, ntype, vid):
"""Return True if the graph contains node `vid` of type `ntype`.
Parameters
----------
ntype : str
The node type.
vid : int
The node ID.
Returns
-------
bool
True if the node exists
Examples
--------
>>> g.has_node('user', 0)
True
>>> g.has_node('user', 4)
False
See Also
--------
has_nodes
"""
return self._graph.has_node(self._ntypes_invmap[ntype], vid)
def has_nodes(self, ntype, vids):
"""Return a 0-1 array ``a`` given the node ID array ``vids``.
``a[i]`` is 1 if the graph contains node ``vids[i]`` of type ``ntype``, 0 otherwise.
Parameters
----------
ntype : str
The node type.
vid : list or tensor
The array of node IDs.
Returns
-------
a : tensor
0-1 array indicating existence
Examples
--------
The following example uses PyTorch backend.
>>> g.has_nodes('user', [0, 1, 2, 3, 4])
tensor([1, 1, 1, 0, 0])
See Also
--------
has_node
"""
vids = utils.toindex(vids)
rst = self._graph.has_nodes(self._ntypes_invmap[ntype], vids)
return rst.tousertensor()
def has_edge_between(self, etype, u, v):
"""Return True if the edge (u, v) of type ``etype`` is in the graph.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
u : int
The node ID of source type.
v : int
The node ID of destination type.
Returns
-------
bool
True if the edge is in the graph, False otherwise.
Examples
--------
Check whether Alice plays Tetris
>>> g.has_edge_between(('user', 'plays', 'game'), 0, 1)
True
And whether Alice plays Minecraft
>>> g.has_edge_between(('user', 'plays', 'game'), 0, 2)
False
See Also
--------
has_edges_between
"""
return self._graph.has_edge_between(self._etypes_invmap[etype], u, v)
def has_edges_between(self, etype, u, v):
"""Return a 0-1 array ``a`` given the source node ID array ``u`` and
destination node ID array ``v``.
``a[i]`` is 1 if the graph contains edge ``(u[i], v[i])`` of type ``etype``, 0 otherwise.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
u : list, tensor
The node ID array of source type.
v : list, tensor
The node ID array of destination type.
Returns
-------
a : tensor
0-1 array indicating existence.
Examples
--------
The following example uses PyTorch backend.
>>> g.has_edges_between(('user', 'plays', 'game'), [0, 0], [1, 2])
tensor([1, 0])
See Also
--------
has_edge_between
"""
u = utils.toindex(u)
v = utils.toindex(v)
rst = self._graph.has_edges_between(self._etypes_invmap[etype], u, v)
return rst.tousertensor()
def predecessors(self, etype, v):
"""Return the predecessors of node `v` in the graph with the same
edge type.
Node `u` is a predecessor of `v` if an edge `(u, v)` exist in the
graph.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : int
The node of destination type.
Returns
-------
tensor
Array of predecessor node IDs of source node type.
Examples
--------
The following example uses PyTorch backend.
Query who plays Tetris:
>>> g.predecessors(('user', 'plays', 'game'), 0)
tensor([0, 1])
This indicates User #0 (Alice) and User #1 (Bob).
See Also
--------
successors
"""
return self._graph.predecessors(self._etypes_invmap[etype], v).tousertensor()
def successors(self, etype, v):
"""Return the successors of node `v` in the graph with the same edge
type.
Node `u` is a successor of `v` if an edge `(v, u)` exist in the
graph.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : int
The node of source type.
Returns
-------
tensor
Array of successor node IDs of destination node type.
Examples
--------
The following example uses PyTorch backend.
Asks which game Alice plays:
>>> g.successors(('user', 'plays', 'game'), 0)
tensor([0])
This indicates Game #0 (Tetris).
See Also
--------
predecessors
"""
return self._graph.successors(self._etypes_invmap[etype], v).tousertensor()
def edge_id(self, etype, u, v, force_multi=False):
"""Return the edge ID, or an array of edge IDs, between source node
`u` and destination node `v`.
Only works if the graph has one edge type. For multiple types,
query with
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
u : int
The node ID of source type.
v : int
The node ID of destination type.
force_multi : bool
If False, will return a single edge ID if the graph is a simple graph.
If True, will always return an array.
Returns
-------
int or tensor
The edge ID if force_multi == True and the graph is a simple graph.
The edge ID array otherwise.
Examples
--------
The following example uses PyTorch backend.
Find the edge ID of "Bob plays Tetris"
>>> g.edge_id(('user', 'plays', 'game'), 1, 0)
1
See Also
--------
edge_ids
"""
idx = self._graph.edge_id(self._etypes_invmap[etype], u, v)
return idx.tousertensor() if force_multi or self._graph.is_multigraph() else idx[0]
def edge_ids(self, etype, u, v, force_multi=False):
"""Return all edge IDs between source node array `u` and destination
node array `v`.
Only works if the graph has one edge type. For multiple types,
query with
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
u : list, tensor
The node ID array of source type.
v : list, tensor
The node ID array of destination type.
force_multi : bool
Whether to always treat the graph as a multigraph.
Returns
-------
tensor, or (tensor, tensor, tensor)
If the graph is a simple graph and `force_multi` is False, return
a single edge ID array `e`. `e[i]` is the edge ID between `u[i]`
and `v[i]`.
Otherwise, return three arrays `(eu, ev, e)`. `e[i]` is the ID
of an edge between `eu[i]` and `ev[i]`. All edges between `u[i]`
and `v[i]` are returned.
Notes
-----
If the graph is a simple graph, `force_multi` is False, and no edge
exist between some pairs of `u[i]` and `v[i]`, the result is undefined.
Examples
--------
The following example uses PyTorch backend.
Find the edge IDs of "Alice plays Tetris" and "Bob plays Minecraft".
>>> g.edge_ids(('user', 'plays', 'game'), [0, 1], [0, 1])
tensor([0, 2])
See Also
--------
edge_id
"""
u = utils.toindex(u)
v = utils.toindex(v)
src, dst, eid = self._graph.edge_ids(self._etypes_invmap[etype], u, v)
if force_multi or self._graph.is_multigraph():
return src.tousertensor(), dst.tousertensor(), eid.tousertensor()
else:
return eid.tousertensor()
def find_edges(self, etype, eid):
"""Given an edge ID array, return the source and destination node ID
array `s` and `d`. `s[i]` and `d[i]` are source and destination node
ID for edge `eid[i]`.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
eid : list, tensor
The edge ID array.
Returns
-------
tensor
The source node ID array.
tensor
The destination node ID array.
Examples
--------
The following example uses PyTorch backend.
Find the user and game of gameplay #0 and #2:
>>> g.find_edges(('user', 'plays', 'game'), [0, 2])
(tensor([0, 1]), tensor([0, 1]))
"""
eid = utils.toindex(eid)
src, dst, _ = self._graph.find_edges(self._etypes_invmap[etype], eid)
return src.tousertensor(), dst.tousertensor()
def in_edges(self, etype, v, form='uv'):
"""Return the inbound edges of the node(s).
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : int, list, tensor
The node(s) of destination type.
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 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.
One Tensor if form == 'eid'
``eid[i]`` is ID of an inbound edge to any of the nodes in ``v``.
Examples
--------
The following example uses PyTorch backend.
Find the gameplay IDs of game #0 (Tetris)
>>> g.in_edges(('user', 'plays', 'game'), 0, 'eid')
tensor([0, 1])
"""
v = utils.toindex(v)
src, dst, eid = self._graph.in_edges(self._etypes_invmap[etype], v)
if form == 'all':
return (src.tousertensor(), dst.tousertensor(), eid.tousertensor())
elif form == 'uv':
return (src.tousertensor(), dst.tousertensor())
elif form == 'eid':
return eid.tousertensor()
else:
raise DGLError('Invalid form:', form)
def out_edges(self, etype, v, form='uv'):
"""Return the outbound edges of the node(s).
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : int, list, tensor
The node(s) of source type.
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]`` to ``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.
One Tensor if form == 'eid'
``eid[i]`` is ID of an outbound edge from any of the nodes in ``v``.
Examples
--------
The following example uses PyTorch backend.
Find the gameplay IDs of user #0 (Alice)
>>> g.out_edges(('user', 'plays', 'game'), 0, 'eid')
tensor([0])
"""
v = utils.toindex(v)
src, dst, eid = self._graph.out_edges(self._etypes_invmap[etype], v)
if form == 'all':
return (src.tousertensor(), dst.tousertensor(), eid.tousertensor())
elif form == 'uv':
return (src.tousertensor(), dst.tousertensor())
elif form == 'eid':
return eid.tousertensor()
else:
raise DGLError('Invalid form:', form)
def all_edges(self, etype, form='uv', order=None):
"""Return all the edges.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
form : str, optional
The return form. Currently support:
- 'all' : a tuple (u, v, eid)
- 'uv' : a pair (u, v), default
- 'eid' : one eid tensor
order : string
The order of the returned edges. Currently support:
- 'srcdst' : sorted by their src and dst ids.
- 'eid' : sorted by edge Ids.
- None : the arbitrary order.
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]``.
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.
One Tensor if form == 'eid'
``eid[i]`` is the ID of an edge in the graph.
Examples
--------
The following example uses PyTorch backend.
Find the user-game pairs for all gameplays:
>>> g.all_edges(('user', 'plays', 'game'), 'uv')
(tensor([0, 1, 1, 2]), tensor([0, 0, 1, 1]))
"""
src, dst, eid = self._graph.edges(self._etypes_invmap[etype], order)
if form == 'all':
return (src.tousertensor(), dst.tousertensor(), eid.tousertensor())
elif form == 'uv':
return (src.tousertensor(), dst.tousertensor())
elif form == 'eid':
return eid.tousertensor()
else:
raise DGLError('Invalid form:', form)
def in_degree(self, etype, v):
"""Return the in-degree of node ``v``.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : int
The node ID of destination type.
Returns
-------
etype : (str, str, str)
The source-edge-destination type triplet
int
The in-degree.
Examples
--------
Find how many users are playing Game #0 (Tetris):
>>> g.in_degree(('user', 'plays', 'game'), 0)
2
See Also
--------
in_degrees
"""
return self._graph.in_degree(self._etypes_invmap[etype], v)
def in_degrees(self, etype, v=ALL):
"""Return the array `d` of in-degrees of the node array `v`.
`d[i]` is the in-degree of node `v[i]`.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : list, tensor, optional.
The node ID array of destination type. Default is to return the
degrees of all the nodes.
Returns
-------
d : tensor
The in-degree array.
Examples
--------
The following example uses PyTorch backend.
Find how many users are playing Game #0 and #1 (Tetris and Minecraft):
>>> g.in_degrees(('user', 'plays', 'game'), [0, 1])
tensor([2, 2])
See Also
--------
in_degree
"""
etype_idx = self._etypes_invmap[etype]
_, dsttype_idx = self._endpoint_types(etype_idx)
if is_all(v):
v = utils.toindex(slice(0, self._graph.number_of_nodes(dsttype_idx)))
else:
v = utils.toindex(v)
return self._graph.in_degrees(etype_idx, v).tousertensor()
def out_degree(self, etype, v):
"""Return the out-degree of node `v`.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : int
The node ID of source type.
Returns
-------
int
The out-degree.
Examples
--------
Find how many games User #0 Alice is playing
>>> g.out_degree(('user', 'plays', 'game'), 0)
1
See Also
--------
out_degrees
"""
return self._graph.out_degree(self._etypes_invmap[etype], v)
def out_degrees(self, etype, v=ALL):
"""Return the array `d` of out-degrees of the node array `v`.
`d[i]` is the out-degree of node `v[i]`.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
v : list, tensor
The node ID array of source type. Default is to return the degrees
of all the nodes.
Returns
-------
d : tensor
The out-degree array.
Examples
--------
The following example uses PyTorch backend.
Find how many games User #0 and #1 (Alice and Bob) are playing
>>> g.out_degrees(('user', 'plays', 'game'), [0, 1])
tensor([1, 2])
See Also
--------
out_degree
"""
etype_idx = self._etypes_invmap[etype]
srctype_idx, _ = self._endpoint_types(etype_idx)
if is_all(v):
v = utils.toindex(slice(0, self._graph.number_of_nodes(srctype_idx)))
else:
v = utils.toindex(v)
return self._graph.out_degrees(etype_idx, v).tousertensor()
def bipartite_from_edge_list(u, v, num_src=None, num_dst=None):
"""Create a bipartite graph component of a heterogeneous graph with a
list of edges.
Parameters
----------
u, v : list[int]
List of source and destination node IDs.
num_src : int, optional
The number of nodes of source type.
By default, the value is the maximum of the source node IDs in the
edge list plus 1.
num_dst : int, optional
The number of nodes of destination type.
By default, the value is the maximum of the destination node IDs in
the edge list plus 1.
"""
num_src = num_src or (max(u) + 1)
num_dst = num_dst or (max(v) + 1)
u = utils.toindex(u)
v = utils.toindex(v)
return heterograph_index.create_bipartite_from_coo(num_src, num_dst, u, v)
def bipartite_from_scipy(spmat, with_edge_id=False):
"""Create a bipartite graph component of a heterogeneous graph with a
scipy sparse matrix.
Parameters
----------
spmat : scipy sparse matrix
The bipartite graph matrix whose rows represent sources and columns
represent destinations.
with_edge_id : bool
If True, the entries in the sparse matrix are treated as edge IDs.
Otherwise, the entries are ignored and edges will be added in
(source, destination) order.
"""
spmat = spmat.tocsr()
num_src, num_dst = spmat.shape
indptr = utils.toindex(spmat.indptr)
indices = utils.toindex(spmat.indices)
data = utils.toindex(spmat.data if with_edge_id else list(range(len(indices))))
return heterograph_index.create_bipartite_from_csr(num_src, num_dst, indptr, indices, data)
class DGLHeteroGraph(DGLBaseHeteroGraph):
"""Base heterogeneous graph class.
A Heterogeneous graph is defined as a graph with node types and edge
types.
If two edges share the same edge type, then their source nodes, as well
as their destination nodes, also have the same type (the source node
types don't have to be the same as the destination node types).
Parameters
----------
graph_data :
The graph data. It can be one of the followings:
* (nx.MultiDiGraph, dict[str, list[tuple[int, int]]])
* (nx.MultiDiGraph, dict[str, scipy.sparse.matrix])
The first element is the metagraph of the heterogeneous graph, as a
networkx directed graph. Its nodes represent the node types, and
its edges represent the edge types. The edge type name should be
stored as edge keys.
The second element is a mapping from edge type to edge list. The
edge list can be either a list of (u, v) pairs, or a scipy sparse
matrix whose rows represents sources and columns represents
destinations. The edges will be added in the (source, destination)
order.
node_frames : dict[str, dict[str, Tensor]]
The node frames for each node type
edge_frames : dict[str, dict[str, Tensor]]
The edge frames for each edge type
multigraph : bool
Whether the heterogeneous graph is a multigraph.
readonly : bool
Whether the heterogeneous graph is readonly.
Examples
--------
Suppose that we want to construct the following heterogeneous graph:
.. graphviz::
digraph G {
Alice -> Bob [label=follows]
Bob -> Carol [label=follows]
Alice -> Tetris [label=plays]
Bob -> Tetris [label=plays]
Bob -> Minecraft [label=plays]
Carol -> Minecraft [label=plays]
Nintendo -> Tetris [label=develops]
Mojang -> Minecraft [label=develops]
{rank=source; Alice; Bob; Carol}
{rank=sink; Nintendo; Mojang}
}
One can analyze the graph and figure out the metagraph as follows:
.. graphviz::
digraph G {
User -> User [label=follows]
User -> Game [label=plays]
Developer -> Game [label=develops]
}
Suppose that one maps the users, games and developers to the following
IDs:
User name Alice Bob Carol
User ID 0 1 2
Game name Tetris Minecraft
Game ID 0 1
Developer name Nintendo Mojang
Developer ID 0 1
One can construct the graph as follows:
>>> mg = nx.MultiDiGraph([('user', 'user', 'follows'),
... ('user', 'game', 'plays'),
... ('developer', 'game', 'develops')])
>>> g = DGLHeteroGraph(
... mg, {
... 'follows': [(0, 1), (1, 2)],
... 'plays': [(0, 0), (1, 0), (1, 1), (2, 1)],
... 'develops': [(0, 0), (1, 1)]})
Then one can query the graph structure as follows:
>>> g['user'].number_of_nodes()
3
>>> g['plays'].number_of_edges()
4
>>> g['develops'].out_degrees() # out-degrees of source nodes of 'develops' relation
tensor([1, 1])
>>> g['develops'].in_edges(0) # in-edges of destination node 0 of 'develops' relation
(tensor([0]), tensor([0]))
Notes
-----
Currently, all heterogeneous graphs are readonly.
"""
# pylint: disable=unused-argument
def __init__(
self,
graph_data=None,
node_frames=None,
edge_frames=None,
multigraph=None,
readonly=True,
_view_ntype_idx=None,
_view_etype_idx=None):
assert readonly, "Only readonly heterogeneous graphs are supported"
# Creating a view of another graph?
if isinstance(graph_data, DGLHeteroGraph):
super(DGLHeteroGraph, self).__init__(
graph_data._graph, graph_data._ntypes, graph_data._etypes,
graph_data._ntypes_invmap, graph_data._etypes_invmap,
graph_data._view_ntype_idx, graph_data._view_etype_idx)
self._node_frames = graph_data._node_frames
self._edge_frames = graph_data._edge_frames
self._msg_frames = graph_data._msg_frames
self._msg_indices = graph_data._msg_indices
self._view_ntype_idx = _view_ntype_idx
self._view_etype_idx = _view_etype_idx
return
if isinstance(graph_data, tuple):
metagraph, edges_by_type = graph_data
if not isinstance(metagraph, nx.MultiDiGraph):
raise TypeError('Metagraph should be networkx.MultiDiGraph')
# create metagraph graph index
srctypes, dsttypes, etypes = [], [], []
ntypes = []
ntypes_invmap = {}
etypes_invmap = {}
for srctype, dsttype, etype in metagraph.edges(keys=True):
srctypes.append(srctype)
dsttypes.append(dsttype)
etypes_invmap[(srctype, etype, dsttype)] = len(etypes_invmap)
etypes.append((srctype, etype, dsttype))
if srctype not in ntypes_invmap:
ntypes_invmap[srctype] = len(ntypes_invmap)
ntypes.append(srctype)
if dsttype not in ntypes_invmap:
ntypes_invmap[dsttype] = len(ntypes_invmap)
ntypes.append(dsttype)
srctypes = [ntypes_invmap[srctype] for srctype in srctypes]
dsttypes = [ntypes_invmap[dsttype] for dsttype in dsttypes]
metagraph_index = graph_index.create_graph_index(
list(zip(srctypes, dsttypes)), None, True) # metagraph is always immutable
# create base bipartites
bipartites = []
num_nodes = defaultdict(int)
# count the number of nodes for each type
for etype_triplet in etypes:
srctype, etype, dsttype = etype_triplet
edges = edges_by_type[etype_triplet]
if ssp.issparse(edges):
num_src, num_dst = edges.shape
elif isinstance(edges, list):
u, v = zip(*edges)
num_src = max(u) + 1
num_dst = max(v) + 1
else:
raise TypeError('unknown edge list type %s' % type(edges))
num_nodes[srctype] = max(num_nodes[srctype], num_src)
num_nodes[dsttype] = max(num_nodes[dsttype], num_dst)
# create actual objects
for etype_triplet in etypes:
srctype, etype, dsttype = etype_triplet
edges = edges_by_type[etype_triplet]
if ssp.issparse(edges):
bipartite = bipartite_from_scipy(edges)
elif isinstance(edges, list):
u, v = zip(*edges)
bipartite = bipartite_from_edge_list(
u, v, num_nodes[srctype], num_nodes[dsttype])
bipartites.append(bipartite)
hg_index = heterograph_index.create_heterograph(metagraph_index, bipartites)
super(DGLHeteroGraph, self).__init__(hg_index, ntypes, etypes)
else:
raise TypeError('Unrecognized graph data type %s' % type(graph_data))
# node and edge frame
if node_frames is None:
self._node_frames = [
FrameRef(Frame(num_rows=self._graph.number_of_nodes(i)))
for i in range(len(self._ntypes))]
else:
self._node_frames = node_frames
if edge_frames is None:
self._edge_frames = [
FrameRef(Frame(num_rows=self._graph.number_of_edges(i)))
for i in range(len(self._etypes))]
else:
self._edge_frames = edge_frames
# message indicators
self._msg_indices = [None] * len(self._etypes)
self._msg_frames = []
for i in range(len(self._etypes)):
frame = FrameRef(Frame(num_rows=self._graph.number_of_edges(i)))
frame.set_initializer(init.zero_initializer)
self._msg_frames.append(frame)
def _create_view(self, ntype_idx, etype_idx):
return DGLHeteroGraph(
graph_data=self, _view_ntype_idx=ntype_idx, _view_etype_idx=etype_idx)
def _get_msg_index(self):
if self._msg_indices[self._current_etype_idx] is None:
self._msg_indices[self._current_etype_idx] = utils.zero_index(
size=self._graph.number_of_edges(self._current_etype_idx))
return self._msg_indices[self._current_etype_idx]
def _set_msg_index(self, index):
self._msg_indices[self._current_etype_idx] = index
def __getitem__(self, key):
if key in self._etypes_invmap:
return self._create_view(None, self._etypes_invmap[key])
else:
raise KeyError(key)
@property
def _node_frame(self):
# overrides DGLGraph._node_frame
return self._node_frames[self._current_ntype_idx]
@property
def _edge_frame(self):
# overrides DGLGraph._edge_frame
return self._edge_frames[self._current_etype_idx]
@property
def _src_frame(self):
# overrides DGLGraph._src_frame
return self._node_frames[self._current_srctype_idx]
@property
def _dst_frame(self):
# overrides DGLGraph._dst_frame
return self._node_frames[self._current_dsttype_idx]
@property
def _msg_frame(self):
# overrides DGLGraph._msg_frame
return self._msg_frames[self._current_etype_idx]
def add_nodes(self, node_type, num, data=None):
"""Add multiple new nodes of the same node type
Parameters
----------
node_type : str
Type of the added nodes. Must appear in the metagraph.
num : int
Number of nodes to be added.
data : dict, optional
Feature data of the added nodes.
Examples
--------
The variable ``g`` is constructed from the example in
DGLBaseHeteroGraph.
>>> g['game'].number_of_nodes()
2
>>> g.add_nodes(3, 'game') # add 3 new games
>>> g['game'].number_of_nodes()
5
"""
pass
def add_edge(self, etype, u, v, data=None):
"""Add an edge of ``etype`` between u of the source node type, and v
of the destination node type..
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
u : int
The source node ID of type ``utype``. Must exist in the graph.
v : int
The destination node ID of type ``vtype``. Must exist in the
graph.
data : dict, optional
Feature data of the added edge.
Examples
--------
The variable ``g`` is constructed from the example in
DGLBaseHeteroGraph.
>>> g['plays'].number_of_edges()
4
>>> g.add_edge(2, 0, 'plays')
>>> g['plays'].number_of_edges()
5
"""
pass
def add_edges(self, u, v, etype, data=None):
"""Add multiple edges of ``etype`` between list of source nodes ``u``
and list of destination nodes ``v`` of type ``vtype``. A single edge
is added between every pair of ``u[i]`` and ``v[i]``.
Parameters
----------
u : list, tensor
The source node IDs of type ``utype``. Must exist in the graph.
v : list, tensor
The destination node IDs of type ``vtype``. Must exist in the
graph.
etype : (str, str, str)
The source-edge-destination type triplet
data : dict, optional
Feature data of the added edge.
Examples
--------
The variable ``g`` is constructed from the example in
DGLBaseHeteroGraph.
>>> g['plays'].number_of_edges()
4
>>> g.add_edges([0, 2], [1, 0], 'plays')
>>> g['plays'].number_of_edges()
6
"""
pass
def from_networkx(
self,
nx_graph,
node_type_attr_name='type',
edge_type_attr_name='type',
node_id_attr_name='id',
edge_id_attr_name='id',
node_attrs=None,
edge_attrs=None):
"""Convert from networkx graph.
The networkx graph must satisfy the metagraph. That is, for any
edge in the networkx graph, the source/destination node type must
be the same as the source/destination node of the edge type in
the metagraph. An error will be raised otherwise.
Parameters
----------
nx_graph : networkx.DiGraph
The networkx graph.
node_type_attr_name : str
The node attribute name for the node type.
The attribute contents must be strings.
edge_type_attr_name : str
The edge attribute name for the edge type.
The attribute contents must be strings.
node_id_attr_name : str
The node attribute name for node type-specific IDs.
The attribute contents must be integers.
If the IDs of the same type are not consecutive integers, its
nodes will be relabeled using consecutive integers. The new
node ordering will inherit that of the sorted IDs.
edge_id_attr_name : str or None
The edge attribute name for edge type-specific IDs.
The attribute contents must be integers.
If the IDs of the same type are not consecutive integers, its
nodes will be relabeled using consecutive integers. The new
node ordering will inherit that of the sorted IDs.
If None is provided, the edge order would be arbitrary.
node_attrs : iterable of str, optional
The node attributes whose data would be copied.
edge_attrs : iterable of str, optional
The edge attributes whose data would be copied.
"""
pass
def node_attr_schemes(self, ntype):
"""Return the node feature schemes.
Each feature scheme is a named tuple that stores the shape and data type
of the node feature.
Parameters
----------
ntype : str
The node type
Returns
-------
dict of str to schemes
The schemes of node feature columns.
Examples
--------
The following uses PyTorch backend.
>>> g.ndata['user']['h'] = torch.randn(3, 4)
>>> g.node_attr_schemes('user')
{'h': Scheme(shape=(4,), dtype=torch.float32)}
"""
return self._node_frames[self._ntypes_invmap[ntype]].schemes
def edge_attr_schemes(self, etype):
"""Return the edge feature schemes.
Each feature scheme is a named tuple that stores the shape and data type
of the edge feature.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
Returns
-------
dict of str to schemes
The schemes of node feature columns.
Examples
--------
The following uses PyTorch backend.
>>> g.edata['user', 'plays', 'game']['h'] = torch.randn(4, 4)
>>> g.edge_attr_schemes(('user', 'plays', 'game'))
{'h': Scheme(shape=(4,), dtype=torch.float32)}
"""
return self._edge_frames[self._etypes_invmap[etype]].schemes
@property
def nodes(self):
"""Return a node view that can used to set/get feature data of a
single node type.
Examples
--------
To set features of User #0 and #2 in a heterogeneous graph:
>>> g.nodes['user'][[0, 2]].data['h'] = torch.zeros(2, 5)
"""
return HeteroNodeView(self)
@property
def ndata(self):
"""Return the data view of all the nodes of a single node type.
Examples
--------
To set features of games in a heterogeneous graph:
>>> g.ndata['game']['h'] = torch.zeros(2, 5)
"""
return HeteroNodeDataView(self)
@property
def edges(self):
"""Return an edges view that can used to set/get feature data of a
single edge type.
Examples
--------
To set features of gameplays #1 (Bob -> Tetris) and #3 (Carol ->
Minecraft) in a heterogeneous graph:
>>> g.edges['user', 'plays', 'game'][[1, 3]].data['h'] = torch.zeros(2, 5)
"""
return HeteroEdgeView(self)
@property
def edata(self):
"""Return the data view of all the edges of a single edge type.
Examples
--------
>>> g.edata['developer', 'develops', 'game']['h'] = torch.zeros(2, 5)
"""
return HeteroEdgeDataView(self)
def set_n_repr(self, ntype, data, u=ALL, inplace=False):
"""Set node(s) representation of a single node type.
`data` is a dictionary from the feature name to feature tensor. Each tensor
is of shape (B, D1, D2, ...), where B is the number of nodes to be updated,
and (D1, D2, ...) be the shape of the node representation tensor. The
length of the given node ids must match B (i.e, len(u) == B).
All update will be done out of place to work with autograd unless the
inplace flag is true.
Parameters
----------
ntype : str
The node type
data : dict of tensor
Node representation.
u : node, container or tensor
The node(s).
inplace : bool
If True, update will be done in place, but autograd will break.
"""
ntype = self._ntypes_invmap[ntype]
if is_all(u):
num_nodes = self._graph.number_of_nodes(ntype)
else:
u = utils.toindex(u)
num_nodes = len(u)
for key, val in data.items():
nfeats = F.shape(val)[0]
if nfeats != num_nodes:
raise DGLError('Expect number of features to match number of nodes (len(u)).'
' Got %d and %d instead.' % (nfeats, num_nodes))
if is_all(u):
for key, val in data.items():
self._node_frames[ntype][key] = val
else:
self._node_frames[ntype].update_rows(u, data, inplace=inplace)
def get_n_repr(self, ntype, u=ALL):
"""Get node(s) representation of a single node type.
The returned feature tensor batches multiple node features on the first dimension.
Parameters
----------
ntype : str
The node type
u : node, container or tensor
The node(s).
Returns
-------
dict
Representation dict from feature name to feature tensor.
"""
if len(self.node_attr_schemes(ntype)) == 0:
return dict()
ntype_idx = self._ntypes_invmap[ntype]
if is_all(u):
return dict(self._node_frames[ntype_idx])
else:
u = utils.toindex(u)
return self._node_frames[ntype_idx].select_rows(u)
def pop_n_repr(self, ntype, key):
"""Get and remove the specified node repr of a given node type.
Parameters
----------
ntype : str
The node type
key : str
The attribute name.
Returns
-------
Tensor
The popped representation
"""
ntype = self._ntypes_invmap[ntype]
return self._node_frames[ntype].pop(key)
def set_e_repr(self, etype, data, edges=ALL, inplace=False):
"""Set edge(s) representation of a single edge type.
`data` is a dictionary from the feature name to feature tensor. Each tensor
is of shape (B, D1, D2, ...), where B is the number of edges to be updated,
and (D1, D2, ...) be the shape of the edge representation tensor.
All update will be done out of place to work with autograd unless the
inplace flag is true.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
data : tensor or dict of tensor
Edge representation.
edges : edges
Edges can be either
* A pair of endpoint nodes (u, v), where u is the node ID of source
node type and v is that of destination node type.
* A tensor of edge ids of the given type.
The default value is all the edges.
inplace : bool
If True, update will be done in place, but autograd will break.
"""
etype_idx = self._etypes_invmap[etype]
# parse argument
if is_all(edges):
eid = ALL
elif isinstance(edges, tuple):
u, v = edges
u = utils.toindex(u)
v = utils.toindex(v)
# Rewrite u, v to handle edge broadcasting and multigraph.
_, _, eid = self._graph.edge_ids(etype_idx, u, v)
else:
eid = utils.toindex(edges)
# sanity check
if not utils.is_dict_like(data):
raise DGLError('Expect dictionary type for feature data.'
' Got "%s" instead.' % type(data))
if is_all(eid):
num_edges = self._graph.number_of_edges(etype_idx)
else:
eid = utils.toindex(eid)
num_edges = len(eid)
for key, val in data.items():
nfeats = F.shape(val)[0]
if nfeats != num_edges:
raise DGLError('Expect number of features to match number of edges.'
' Got %d and %d instead.' % (nfeats, num_edges))
# set
if is_all(eid):
# update column
for key, val in data.items():
self._edge_frames[etype_idx][key] = val
else:
# update row
self._edge_frames[etype_idx].update_rows(eid, data, inplace=inplace)
def get_e_repr(self, etype, edges=ALL):
"""Get edge(s) representation.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
edges : edges
Edges can be a pair of endpoint nodes (u, v), or a
tensor of edge ids. The default value is all the edges.
Returns
-------
dict
Representation dict
"""
etype_idx = self._etypes_invmap[etype]
if len(self.edge_attr_schemes(etype)) == 0:
return dict()
# parse argument
if is_all(edges):
eid = ALL
elif isinstance(edges, tuple):
u, v = edges
u = utils.toindex(u)
v = utils.toindex(v)
# Rewrite u, v to handle edge broadcasting and multigraph.
_, _, eid = self._graph.edge_ids(etype_idx, u, v)
else:
eid = utils.toindex(edges)
if is_all(eid):
return dict(self._edge_frames[etype_idx])
else:
eid = utils.toindex(eid)
return self._edge_frames[etype_idx].select_rows(eid)
def pop_e_repr(self, etype, key):
"""Get and remove the specified edge repr of a single edge type.
Parameters
----------
etype : (str, str, str)
The source-edge-destination type triplet
key : str
The attribute name.
Returns
-------
Tensor
The popped representation
"""
etype = self._etypes_invmap[etype]
self._edge_frames[etype].pop(key)
def register_message_func(self, func):
"""Register global message function for each edge type provided.
Once registered, ``func`` will be used as the default
message function in message passing operations, including
:func:`send`, :func:`send_and_recv`, :func:`pull`,
:func:`push`, :func:`update_all`.
Parameters
----------
func : callable
Message function on the edge. The function should be
an :mod:`Edge UDF <dgl.udf>`.
See Also
--------
send
send_and_recv
pull
push
update_all
"""
raise NotImplementedError
def register_reduce_func(self, func):
"""Register global message reduce function for each edge type provided.
Once registered, ``func`` will be used as the default
message reduce function in message passing operations, including
:func:`recv`, :func:`send_and_recv`, :func:`push`, :func:`pull`,
:func:`update_all`.
Parameters
----------
func : callable
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
See Also
--------
recv
send_and_recv
push
pull
update_all
"""
raise NotImplementedError
def register_apply_node_func(self, func):
"""Register global node apply function for each node type provided.
Once registered, ``func`` will be used as the default apply
node function. Related operations include :func:`apply_nodes`,
:func:`recv`, :func:`send_and_recv`, :func:`push`, :func:`pull`,
:func:`update_all`.
Parameters
----------
func : callable
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
See Also
--------
apply_nodes
register_apply_edge_func
"""
raise NotImplementedError
def register_apply_edge_func(self, func):
"""Register global edge apply function for each edge type provided.
Once registered, ``func`` will be used as the default apply
edge function in :func:`apply_edges`.
Parameters
----------
func : callable
Apply function on the edge. The function should be
an :mod:`Edge UDF <dgl.udf>`.
See Also
--------
apply_edges
register_apply_node_func
"""
raise NotImplementedError
def apply_nodes(self, func, v=ALL, inplace=False):
"""Apply the function on the nodes with the same type to update their
features.
If None is provided for ``func``, nothing will happen.
Parameters
----------
func : dict[str, callable] or None
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
v : dict[str, int or iterable of int or tensor], optional
The (type-specific) node (ids) on which to apply ``func``.
inplace : bool, optional
If True, update will be done in place, but autograd will break.
Examples
--------
>>> g.ndata['user']['h'] = torch.ones(3, 5)
>>> g.apply_nodes({'user': lambda nodes: {'h': nodes.data['h'] * 2}})
>>> g.ndata['user']['h']
tensor([[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.]])
"""
for ntype, nfunc in func.items():
if is_all(v):
v_ntype = utils.toindex(slice(0, self.number_of_nodes(ntype)))
else:
v_ntype = utils.toindex(v[ntype])
with ir.prog() as prog:
scheduler.schedule_apply_nodes(
graph=self._create_view(self._ntypes_invmap[ntype], None),
v=v_ntype,
apply_func=nfunc,
inplace=inplace)
Runtime.run(prog)
def apply_edges(self, func, edges=ALL, inplace=False):
"""Apply the function on the edges with the same type to update their
features.
If None is provided for ``func``, nothing will happen.
Parameters
----------
func : dict[(str, str, str), callable] or None
Apply function on the edge. The function should be
an :mod:`Edge UDF <dgl.udf>`.
edges : dict[(str, str, str), any valid edge specification], optional
Edges on which to apply ``func``. See :func:`send` for valid
edge specification.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
Examples
--------
>>> g.edata['user', 'plays', 'game']['h'] = torch.ones(4, 5)
>>> g.apply_edges(
... {('user', 'plays', 'game'): lambda edges: {'h': edges.data['h'] * 2}})
>>> g.edata['user', 'plays', 'game']['h']
tensor([[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.]])
"""
for etype, efunc in func.items():
etype_idx = self._etypes_invmap[etype]
if is_all(edges):
u, v, _ = self._graph.edges(etype_idx, 'eid')
eid = utils.toindex(slice(0, self.number_of_edges(etype)))
elif isinstance(edges, tuple):
u, v = edges
u = utils.toindex(u)
v = utils.toindex(v)
# Rewrite u, v to handle edge broadcasting and multigraph.
u, v, eid = self._graph.edge_ids(etype_idx, u, v)
else:
eid = utils.toindex(edges)
u, v, _ = self._graph.find_edges(etype_idx, eid)
with ir.prog() as prog:
scheduler.schedule_apply_edges(
graph=self._create_view(None, etype_idx),
u=u,
v=v,
eid=eid,
apply_func=efunc,
inplace=inplace)
Runtime.run(prog)
def group_apply_edges(self, group_by, func, edges=ALL, inplace=False):
"""Group the edges by nodes and apply the function of the grouped
edges to update their features. The edges are of the same edge type
(hence having the same source and destination node type).
Parameters
----------
group_by : str
Specify how to group edges. Expected to be either 'src' or 'dst'
func : dict[(str, str, str), callable]
Apply function on the edge. The function should be
an :mod:`Edge UDF <dgl.udf>`. The input of `Edge UDF` should
be (bucket_size, degrees, *feature_shape), and
return the dict with values of the same shapes.
edges : dict[(str, str, str), valid edges type], optional
Edges on which to group and apply ``func``. See :func:`send` for valid
edges type. Default is all the edges.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
"""
if group_by not in ('src', 'dst'):
raise DGLError("Group_by should be either src or dst")
for etype, efunc in func.items():
etype_idx = self._etypes_invmap[etype]
if is_all(edges):
u, v, _ = self._graph.edges(etype_idx)
eid = utils.toindex(slice(0, self.number_of_edges(etype)))
elif isinstance(edges, tuple):
u, v = edges
u = utils.toindex(u)
v = utils.toindex(v)
# Rewrite u, v to handle edge broadcasting and multigraph.
u, v, eid = self._graph.edge_ids(etype_idx, u, v)
else:
eid = utils.toindex(edges)
u, v, _ = self._graph.find_edges(etype_idx, eid)
with ir.prog() as prog:
scheduler.schedule_group_apply_edge(
graph=self._create_view(None, etype_idx),
u=u,
v=v,
eid=eid,
apply_func=efunc,
group_by=group_by,
inplace=inplace)
Runtime.run(prog)
def send(self, edges=ALL, message_func=None):
"""Send messages along the given edges with the same edge type.
``edges`` can be any of the following types:
* ``int`` : Specify one edge using its edge id (of the given edge type).
* ``pair of int`` : Specify one edge using its endpoints (of source node type
and destination node type respectively).
* ``int iterable`` / ``tensor`` : Specify multiple edges using their edge ids.
* ``pair of int iterable`` / ``pair of tensors`` :
Specify multiple edges using their endpoints.
Only works if the graph has one edge type. For multiple types,
use
.. code::
g['edgetype'].send(edges, message_func)
The UDF returns messages on the edges and can be later fetched in
the destination node's ``mailbox``. Receiving will consume the messages.
See :func:`recv` for example.
If multiple ``send`` are triggered on the same edge without ``recv``. Messages
generated by the later ``send`` will overwrite previous messages.
Parameters
----------
edges : valid edges type, optional
Edges on which to apply ``message_func``. Default is sending along all
the edges.
message_func : callable
Message function on the edges. The function should be
an :mod:`Edge UDF <dgl.udf>`.
Notes
-----
On multigraphs, if :math:`u` and :math:`v` are specified, then the messages will be sent
along all edges between :math:`u` and :math:`v`.
"""
assert not utils.is_dict_like(message_func), \
"multiple-type message passing is not implemented"
assert message_func is not None
if is_all(edges):
eid = utils.toindex(slice(0, self._graph.number_of_edges(self._current_etype_idx)))
u, v, _ = self._graph.edges(self._current_etype_idx)
elif isinstance(edges, tuple):
u, v = edges
u = utils.toindex(u)
v = utils.toindex(v)
# Rewrite u, v to handle edge broadcasting and multigraph.
u, v, eid = self._graph.edge_ids(self._current_etype_idx, u, v)
else:
eid = utils.toindex(edges)
u, v, _ = self._graph.find_edges(self._current_etype_idx, eid)
if len(eid) == 0:
# no edge to be triggered
return
with ir.prog() as prog:
scheduler.schedule_send(graph=self, u=u, v=v, eid=eid,
message_func=message_func)
Runtime.run(prog)
def recv(self,
v=ALL,
reduce_func=None,
apply_node_func=None,
inplace=False):
"""Receive and reduce incoming messages and update the features of node(s) :math:`v`.
Optionally, apply a function to update the node features after receive.
* `reduce_func` will be skipped for nodes with no incoming message.
* If all ``v`` have no incoming message, this will downgrade to an :func:`apply_nodes`.
* If some ``v`` have no incoming message, their new feature value will be calculated
by the column initializer (see :func:`set_n_initializer`). The feature shapes and
dtypes will be inferred.
The node features will be updated by the result of the ``reduce_func``.
Messages are consumed once received.
The provided UDF maybe called multiple times so it is recommended to provide
function with no side effect.
Only works if the graph has one edge type. For multiple types,
use
.. code::
g['edgetype'].recv(v, reduce_func, apply_node_func, inplace)
Parameters
----------
v : int, container or tensor, optional
The node(s) to be updated. Default is receiving all the nodes.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
"""
assert not utils.is_dict_like(reduce_func) and \
not utils.is_dict_like(apply_node_func), \
"multiple-type message passing is not implemented"
assert reduce_func is not None
if is_all(v):
v = F.arange(0, self._graph.number_of_nodes(self._current_dsttype_idx))
elif isinstance(v, int):
v = [v]
v = utils.toindex(v)
if len(v) == 0:
# no vertex to be triggered.
return
with ir.prog() as prog:
scheduler.schedule_recv(graph=self,
recv_nodes=v,
reduce_func=reduce_func,
apply_func=apply_node_func,
inplace=inplace)
Runtime.run(prog)
def send_and_recv(self,
edges,
message_func=None,
reduce_func=None,
apply_node_func=None,
inplace=False):
"""Send messages along edges with the same edge type, and let destinations
receive them.
Optionally, apply a function to update the node features after receive.
This is a convenient combination for performing
``send(self, self.edges, message_func)`` and
``recv(self, dst, reduce_func, apply_node_func)``, where ``dst``
are the destinations of the ``edges``.
Only works if the graph has one edge type. For multiple types,
use
.. code::
g['edgetype'].send_and_recv(edges, message_func, reduce_func, apply_node_func, inplace)
Parameters
----------
edges : valid edges type
Edges on which to apply ``func``. See :func:`send` for valid
edges type.
message_func : callable, optional
Message function on the edges. The function should be
an :mod:`Edge UDF <dgl.udf>`.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable, optional
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
"""
assert not utils.is_dict_like(message_func) and \
not utils.is_dict_like(reduce_func) and \
not utils.is_dict_like(apply_node_func), \
"multiple-type message passing is not implemented"
assert message_func is not None
assert reduce_func is not None
if isinstance(edges, tuple):
u, v = edges
u = utils.toindex(u)
v = utils.toindex(v)
# Rewrite u, v to handle edge broadcasting and multigraph.
u, v, eid = self._graph.edge_ids(self._current_etype_idx, u, v)
else:
eid = utils.toindex(edges)
u, v, _ = self._graph.find_edges(self._current_etype_idx, eid)
if len(u) == 0:
# no edges to be triggered
return
with ir.prog() as prog:
scheduler.schedule_snr(graph=self,
edge_tuples=(u, v, eid),
message_func=message_func,
reduce_func=reduce_func,
apply_func=apply_node_func,
inplace=inplace)
Runtime.run(prog)
def pull(self,
v,
message_func=None,
reduce_func=None,
apply_node_func=None,
inplace=False):
"""Pull messages from the node(s)' predecessors and then update their features.
Optionally, apply a function to update the node features after receive.
* `reduce_func` will be skipped for nodes with no incoming message.
* If all ``v`` have no incoming message, this will downgrade to an :func:`apply_nodes`.
* If some ``v`` have no incoming message, their new feature value will be calculated
by the column initializer (see :func:`set_n_initializer`). The feature shapes and
dtypes will be inferred.
Only works if the graph has one edge type. For multiple types,
use
.. code::
g['edgetype'].pull(v, message_func, reduce_func, apply_node_func, inplace)
Parameters
----------
v : int, container or tensor, optional
The node(s) to be updated. Default is receiving all the nodes.
message_func : callable, optional
Message function on the edges. The function should be
an :mod:`Edge UDF <dgl.udf>`.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable, optional
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
"""
assert not utils.is_dict_like(message_func) and \
not utils.is_dict_like(reduce_func) and \
not utils.is_dict_like(apply_node_func), \
"multiple-type message passing is not implemented"
assert message_func is not None
assert reduce_func is not None
v = utils.toindex(v)
if len(v) == 0:
return
with ir.prog() as prog:
scheduler.schedule_pull(graph=self,
pull_nodes=v,
message_func=message_func,
reduce_func=reduce_func,
apply_func=apply_node_func,
inplace=inplace)
Runtime.run(prog)
def push(self,
u,
message_func=None,
reduce_func=None,
apply_node_func=None,
inplace=False):
"""Send message from the node(s) to their successors and update them.
Optionally, apply a function to update the node features after receive.
Only works if the graph has one edge type. For multiple types,
use
.. code::
g['edgetype'].push(e, message_func, reduce_func, apply_node_func, inplace)
Parameters
----------
u : int, container or tensor
The node(s) to push messages out.
message_func : callable, optional
Message function on the edges. The function should be
an :mod:`Edge UDF <dgl.udf>`.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable, optional
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
inplace: bool, optional
If True, update will be done in place, but autograd will break.
"""
assert not utils.is_dict_like(message_func) and \
not utils.is_dict_like(reduce_func) and \
not utils.is_dict_like(apply_node_func), \
"multiple-type message passing is not implemented"
assert message_func is not None
assert reduce_func is not None
u = utils.toindex(u)
if len(u) == 0:
return
with ir.prog() as prog:
scheduler.schedule_push(graph=self,
u=u,
message_func=message_func,
reduce_func=reduce_func,
apply_func=apply_node_func,
inplace=inplace)
Runtime.run(prog)
def update_all(self,
message_func=None,
reduce_func=None,
apply_node_func=None):
"""Send messages through all edges and update all nodes.
Optionally, apply a function to update the node features after receive.
This is a convenient combination for performing
``send(self, self.edges(), message_func)`` and
``recv(self, self.nodes(), reduce_func, apply_node_func)``.
Only works if the graph has one edge type. For multiple types,
use
.. code::
g['edgetype'].update_all(message_func, reduce_func, apply_node_func)
Parameters
----------
message_func : callable, optional
Message function on the edges. The function should be
an :mod:`Edge UDF <dgl.udf>`.
reduce_func : callable, optional
Reduce function on the node. The function should be
a :mod:`Node UDF <dgl.udf>`.
apply_node_func : callable, optional
Apply function on the nodes. The function should be
a :mod:`Node UDF <dgl.udf>`.
"""
assert not utils.is_dict_like(message_func) and \
not utils.is_dict_like(reduce_func) and \
not utils.is_dict_like(apply_node_func), \
"multiple-type message passing is not implemented"
assert message_func is not None
assert reduce_func is not None
with ir.prog() as prog:
scheduler.schedule_update_all(graph=self,
message_func=message_func,
reduce_func=reduce_func,
apply_func=apply_node_func)
Runtime.run(prog)
def prop_nodes(self,
nodes_generator,
message_func=None,
reduce_func=None,
apply_node_func=None):
"""Node propagation in heterogeneous graph is not supported.
"""
raise NotImplementedError('not supported')
def prop_edges(self,
edges_generator,
message_func=None,
reduce_func=None,
apply_node_func=None):
"""Edge propagation in heterogeneous graph is not supported.
"""
raise NotImplementedError('not supported')
def subgraph(self, nodes):
"""Return the subgraph induced on given nodes.
Parameters
----------
nodes : dict[str, list or iterable]
A dictionary of node types to node ID array to construct
subgraph.
All nodes must exist in the graph.
Returns
-------
G : DGLHeteroSubGraph
The subgraph.
The nodes are relabeled so that node `i` of type `t` in the
subgraph is mapped to the ``nodes[i]`` of type `t` in the
original graph.
The edges are also relabeled.
One can retrieve the mapping from subgraph node/edge ID to parent
node/edge ID via `parent_nid` and `parent_eid` properties of the
subgraph.
"""
pass
def subgraphs(self, nodes):
"""Return a list of subgraphs, each induced in the corresponding given
nodes in the list.
Equivalent to
``[self.subgraph(nodes_list) for nodes_list in nodes]``
Parameters
----------
nodes : a list of dict[str, list or iterable]
A list of type-ID dictionaries to construct corresponding
subgraphs. The dictionaries are of the same form as
:func:`subgraph`.
All nodes in all the list items must exist in the graph.
Returns
-------
G : A list of DGLHeteroSubGraph
The subgraphs.
"""
pass
def edge_subgraph(self, edges):
"""Return the subgraph induced on given edges.
Parameters
----------
edges : dict[etype, list or iterable]
A dictionary of edge types to edge ID array to construct
subgraph.
All edges must exist in the subgraph.
The edge type is characterized by a triplet of source type name,
destination type name, and edge type name.
Returns
-------
G : DGLHeteroSubGraph
The subgraph.
The edges are relabeled so that edge `i` of type `t` in the
subgraph is mapped to the ``edges[i]`` of type `t` in the
original graph.
One can retrieve the mapping from subgraph node/edge ID to parent
node/edge ID via `parent_nid` and `parent_eid` properties of the
subgraph.
"""
pass
def adjacency_matrix_scipy(self, etype, transpose=False, fmt='csr'):
"""Return the scipy adjacency matrix representation of edges with the
given edge type.
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.
The elements in the adajency matrix are edge ids.
Parameters
----------
etype : tuple[str, str, str]
The edge type, characterized by a triplet of source type name,
destination type name, and edge type name.
transpose : bool, optional (default=False)
A flag to transpose the returned adjacency matrix.
fmt : str, optional (default='csr')
Indicates the format of returned adjacency matrix.
Returns
-------
scipy.sparse.spmatrix
The scipy representation of adjacency matrix.
"""
pass
def adjacency_matrix(self, etype, transpose=False, ctx=F.cpu()):
"""Return the adjacency matrix representation of edges with the
given edge type.
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.
Parameters
----------
etype : tuple[str, str, str]
The edge type, characterized by a triplet of source type name,
destination type name, and edge type name.
transpose : bool, optional (default=False)
A flag to transpose the returned adjacency matrix.
ctx : context, optional (default=cpu)
The context of returned adjacency matrix.
Returns
-------
SparseTensor
The adjacency matrix.
"""
pass
def incidence_matrix(self, etype, typestr, ctx=F.cpu()):
"""Return the incidence matrix representation of edges with the given
edge type.
An incidence matrix is an n x m sparse matrix, where n is
the number of nodes and m is the number of edges. Each nnz
value indicating whether the edge is incident to the node
or not.
There are three types of an incidence matrix :math:`I`:
* ``in``:
- :math:`I[v, e] = 1` if :math:`e` is the in-edge of :math:`v`
(or :math:`v` is the dst node of :math:`e`);
- :math:`I[v, e] = 0` otherwise.
* ``out``:
- :math:`I[v, e] = 1` if :math:`e` is the out-edge of :math:`v`
(or :math:`v` is the src node of :math:`e`);
- :math:`I[v, e] = 0` otherwise.
* ``both``:
- :math:`I[v, e] = 1` if :math:`e` is the in-edge of :math:`v`;
- :math:`I[v, e] = -1` if :math:`e` is the out-edge of :math:`v`;
- :math:`I[v, e] = 0` otherwise (including self-loop).
Parameters
----------
etype : tuple[str, str, str]
The edge type, characterized by a triplet of source type name,
destination type name, and edge type name.
typestr : str
Can be either ``in``, ``out`` or ``both``
ctx : context, optional (default=cpu)
The context of returned incidence matrix.
Returns
-------
SparseTensor
The incidence matrix.
"""
pass
def filter_nodes(self, ntype, predicate, nodes=ALL):
"""Return a tensor of node IDs with the given node type that satisfy
the given predicate.
Parameters
----------
ntype : str
The node type.
predicate : callable
A function of signature ``func(nodes) -> tensor``.
``nodes`` are :class:`NodeBatch` objects as in :mod:`~dgl.udf`.
The ``tensor`` returned should be a 1-D boolean tensor with
each element indicating whether the corresponding node in
the batch satisfies the predicate.
nodes : int, iterable or tensor of ints
The nodes to filter on. Default value is all the nodes.
Returns
-------
tensor
The filtered nodes.
"""
pass
def filter_edges(self, etype, predicate, edges=ALL):
"""Return a tensor of edge IDs with the given edge type that satisfy
the given predicate.
Parameters
----------
etype : tuple[str, str, str]
The edge type, characterized by a triplet of source type name,
destination type name, and edge type name.
predicate : callable
A function of signature ``func(edges) -> tensor``.
``edges`` are :class:`EdgeBatch` objects as in :mod:`~dgl.udf`.
The ``tensor`` returned should be a 1-D boolean tensor with
each element indicating whether the corresponding edge in
the batch satisfies the predicate.
edges : valid edges type
Edges on which to apply ``func``. See :func:`send` for valid
edges type. Default value is all the edges.
Returns
-------
tensor
The filtered edges represented by their ids.
"""
pass
def readonly(self, readonly_state=True):
"""Set this graph's readonly state in-place.
Parameters
----------
readonly_state : bool, optional
New readonly state of the graph, defaults to True.
"""
pass
# TODO: replace this after implementing frame
# pylint: disable=useless-super-delegation
def __repr__(self):
return super(DGLHeteroGraph, self).__repr__()
# pylint: disable=abstract-method
class DGLHeteroSubGraph(DGLHeteroGraph):
"""
Parameters
----------
parent : DGLHeteroGraph
The parent graph.
parent_nid : dict[str, utils.Index]
The type-specific parent node IDs for each type.
parent_eid : dict[etype, utils.Index]
The type-specific parent edge IDs for each type.
graph_idx : GraphIndex
The graph index
shared : bool, optional
Whether the subgraph shares node/edge features with the parent graph
"""
# pylint: disable=unused-argument, super-init-not-called
def __init__(
self,
parent,
parent_nid,
parent_eid,
graph_idx,
shared=False):
pass
@property
def parent_nid(self):
"""Get the parent node ids.
The returned tensor dictionary can be used as a map from the node id
in this subgraph to the node id in the parent graph.
Returns
-------
dict[str, Tensor]
The parent node id array for each type.
"""
pass
@property
def parent_eid(self):
"""Get the parent edge ids.
The returned tensor dictionary can be used as a map from the edge id
in this subgraph to the edge id in the parent graph.
Returns
-------
dict[etype, Tensor]
The parent edge id array for each type.
The edge types are characterized by a triplet of source type
name, destination type name, and edge type name.
"""
pass
def copy_to_parent(self, inplace=False):
"""Write node/edge features to the parent graph.
Parameters
----------
inplace : bool
If true, use inplace write (no gradient but faster)
"""
pass
def copy_from_parent(self):
"""Copy node/edge features from the parent graph.
All old features will be removed.
"""
pass
def map_to_subgraph_nid(self, parent_vids):
"""Map the node IDs in the parent graph to the node IDs in the
subgraph.
Parameters
----------
parent_vids : dict[str, list or tensor]
The dictionary of node types to parent node ID array.
Returns
-------
dict[str, tensor]
The node ID array in the subgraph of each node type.
"""
pass
在新工单中引用 查看 Git Blame 复制永久链接