wehub/dmlc--dgl
1
0
Fork 0
Code Issues Pull Requests Actions Deployments Models Agent Notes Packages Projects Releases Wiki Activity
Files
main
dmlc--dgl/python/dgl/heterograph.py
T
wehub-resource-sync c36a561cd8 chore: import upstream snapshot with attribution
2026-07-13 13:35:51 +08:00

6759 lines
239 KiB
Python
Raw Permalink Blame History

"""Classes for heterogeneous graphs."""
import copy
import itertools
import numbers
# pylint: disable= too-many-lines
from collections import defaultdict
from collections.abc import Iterable, Mapping
from contextlib import contextmanager
import networkx as nx
import numpy as np
from . import backend as F, core, graph_index, heterograph_index, utils
from ._ffi.function import _init_api
from .base import (
ALL,
dgl_warning,
DGLError,
EID,
ETYPE,
is_all,
NID,
NTYPE,
SLICE_FULL,
)
from .frame import Frame
from .ops import segment
from .view import (
HeteroEdgeDataView,
HeteroEdgeView,
HeteroNodeDataView,
HeteroNodeView,
)
__all__ = ["DGLGraph", "combine_names"]
class DGLGraph(object):
"""Class for storing graph structure and node/edge feature data.
There are a few ways to create a DGLGraph:
* To create a homogeneous graph from Tensor data, use :func:`dgl.graph`.
* To create a heterogeneous graph from Tensor data, use :func:`dgl.heterograph`.
* To create a graph from other data sources, use ``dgl.*`` create ops. See
:ref:`api-graph-create-ops`.
Read the user guide chapter :ref:`guide-graph` for an in-depth explanation about its
usage.
"""
is_block = False
# pylint: disable=unused-argument, dangerous-default-value
def __init__(
self,
gidx=[],
ntypes=["_N"],
etypes=["_E"],
node_frames=None,
edge_frames=None,
**deprecate_kwargs
):
"""Internal constructor for creating a DGLGraph.
Parameters
----------
gidx : HeteroGraphIndex
Graph index object.
ntypes : list of str, pair of list of str
Node type list. ``ntypes[i]`` stores the name of node type i.
If a pair is given, the graph created is a uni-directional bipartite graph,
and its SRC node types and DST node types are given as in the pair.
etypes : list of str
Edge type list. ``etypes[i]`` stores the name of edge type i.
node_frames : list[Frame], optional
Node feature storage. If None, empty frame is created.
Otherwise, ``node_frames[i]`` stores the node features
of node type i. (default: None)
edge_frames : list[Frame], optional
Edge feature storage. If None, empty frame is created.
Otherwise, ``edge_frames[i]`` stores the edge features
of edge type i. (default: None)
"""
if isinstance(gidx, DGLGraph):
raise DGLError(
"The input is already a DGLGraph. No need to create it again."
)
if not isinstance(gidx, heterograph_index.HeteroGraphIndex):
dgl_warning(
"Recommend creating graphs by `dgl.graph(data)`"
" instead of `dgl.DGLGraph(data)`."
)
(sparse_fmt, arrays), num_src, num_dst = utils.graphdata2tensors(
gidx
)
if sparse_fmt == "coo":
gidx = heterograph_index.create_unitgraph_from_coo(
1,
num_src,
num_dst,
arrays[0],
arrays[1],
["coo", "csr", "csc"],
)
else:
gidx = heterograph_index.create_unitgraph_from_csr(
1,
num_src,
num_dst,
arrays[0],
arrays[1],
arrays[2],
["coo", "csr", "csc"],
sparse_fmt == "csc",
)
if len(deprecate_kwargs) != 0:
dgl_warning(
"Keyword arguments {} are deprecated in v0.5, and can be safely"
" removed in all cases.".format(list(deprecate_kwargs.keys()))
)
self._init(gidx, ntypes, etypes, node_frames, edge_frames)
def _init(self, gidx, ntypes, etypes, node_frames, edge_frames):
"""Init internal states."""
self._graph = gidx
self._canonical_etypes = None
self._batch_num_nodes = None
self._batch_num_edges = None
# Handle node types
if isinstance(ntypes, tuple):
if len(ntypes) != 2:
errmsg = "Invalid input. Expect a pair (srctypes, dsttypes) but got {}".format(
ntypes
)
raise TypeError(errmsg)
if not self._graph.is_metagraph_unibipartite():
raise ValueError(
"Invalid input. The metagraph must be a uni-directional"
" bipartite graph."
)
self._ntypes = ntypes[0] + ntypes[1]
self._srctypes_invmap = {t: i for i, t in enumerate(ntypes[0])}
self._dsttypes_invmap = {
t: i + len(ntypes[0]) for i, t in enumerate(ntypes[1])
}
self._is_unibipartite = True
if len(ntypes[0]) == 1 and len(ntypes[1]) == 1 and len(etypes) == 1:
self._canonical_etypes = [
(ntypes[0][0], etypes[0], ntypes[1][0])
]
else:
self._ntypes = ntypes
if len(ntypes) == 1:
src_dst_map = None
else:
src_dst_map = find_src_dst_ntypes(
self._ntypes, self._graph.metagraph
)
self._is_unibipartite = src_dst_map is not None
if self._is_unibipartite:
self._srctypes_invmap, self._dsttypes_invmap = src_dst_map
else:
self._srctypes_invmap = {
t: i for i, t in enumerate(self._ntypes)
}
self._dsttypes_invmap = self._srctypes_invmap
# Handle edge types
self._etypes = etypes
if self._canonical_etypes is None:
if len(etypes) == 1 and len(ntypes) == 1:
self._canonical_etypes = [(ntypes[0], etypes[0], ntypes[0])]
else:
self._canonical_etypes = make_canonical_etypes(
self._etypes, self._ntypes, self._graph.metagraph
)
# An internal map from etype to canonical etype tuple.
# If two etypes have the same name, an empty tuple is stored instead to indicate
# ambiguity.
self._etype2canonical = {}
for i, ety in enumerate(self._etypes):
if ety in self._etype2canonical:
self._etype2canonical[ety] = tuple()
else:
self._etype2canonical[ety] = self._canonical_etypes[i]
self._etypes_invmap = {
t: i for i, t in enumerate(self._canonical_etypes)
}
# node and edge frame
if node_frames is None:
node_frames = [None] * len(self._ntypes)
node_frames = [
Frame(num_rows=self._graph.num_nodes(i)) if frame is None else frame
for i, frame in enumerate(node_frames)
]
self._node_frames = node_frames
if edge_frames is None:
edge_frames = [None] * len(self._etypes)
edge_frames = [
Frame(num_rows=self._graph.num_edges(i)) if frame is None else frame
for i, frame in enumerate(edge_frames)
]
self._edge_frames = edge_frames
def __setstate__(self, state):
# Compatibility check
# TODO: version the storage
if isinstance(state, dict):
# Since 0.5 we use the default __dict__ method
self.__dict__.update(state)
elif isinstance(state, tuple) and len(state) == 5:
# DGL == 0.4.3
dgl_warning(
"The object is pickled with DGL == 0.4.3. "
"Some of the original attributes are ignored."
)
self._init(*state)
elif isinstance(state, dict):
# DGL <= 0.4.2
dgl_warning(
"The object is pickled with DGL <= 0.4.2. "
"Some of the original attributes are ignored."
)
self._init(
state["_graph"],
state["_ntypes"],
state["_etypes"],
state["_node_frames"],
state["_edge_frames"],
)
else:
raise IOError("Unrecognized pickle format.")
def __repr__(self):
if len(self.ntypes) == 1 and len(self.etypes) == 1:
ret = (
"Graph(num_nodes={node}, num_edges={edge},\n"
" ndata_schemes={ndata}\n"
" edata_schemes={edata})"
)
return ret.format(
node=self.num_nodes(),
edge=self.num_edges(),
ndata=str(self.node_attr_schemes()),
edata=str(self.edge_attr_schemes()),
)
else:
ret = (
"Graph(num_nodes={node},\n"
" num_edges={edge},\n"
" metagraph={meta})"
)
nnode_dict = {
self.ntypes[i]: self._graph.num_nodes(i)
for i in range(len(self.ntypes))
}
nedge_dict = {
self.canonical_etypes[i]: self._graph.num_edges(i)
for i in range(len(self.etypes))
}
meta = str(self.metagraph().edges(keys=True))
return ret.format(node=nnode_dict, edge=nedge_dict, meta=meta)
def __copy__(self):
"""Shallow copy implementation."""
# TODO(minjie): too many states in python; should clean up and lower to C
cls = type(self)
obj = cls.__new__(cls)
obj.__dict__.update(self.__dict__)
return obj
#################################################################
# Mutation operations
#################################################################
def add_nodes(self, num, data=None, ntype=None):
r"""Add new nodes of the same node type
Parameters
----------
num : int
Number of nodes to add.
data : dict, optional
Feature data of the added nodes.
ntype : str, optional
The type of the new nodes. Can be omitted if there is
only one node type in the graph.
Notes
-----
* Inplace update is applied to the current graph.
* If the key of ``data`` does not contain some existing feature fields,
those features for the new nodes will be created by initializers
defined with :func:`set_n_initializer` (default initializer fills zeros).
* If the key of ``data`` contains new feature fields, those features for
the old nodes will be created by initializers defined with
:func:`set_n_initializer` (default initializer fills zeros).
* This function discards the batch information. Please use
:func:`dgl.DGLGraph.set_batch_num_nodes`
and :func:`dgl.DGLGraph.set_batch_num_edges` on the transformed graph
to maintain the information.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Homogeneous Graphs or Heterogeneous Graphs with A Single Node Type**
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.num_nodes()
3
>>> g.add_nodes(2)
>>> g.num_nodes()
5
If the graph has some node features and new nodes are added without
features, their features will be created by initializers defined
with :func:`set_n_initializer`.
>>> g.ndata['h'] = torch.ones(5, 1)
>>> g.add_nodes(1)
>>> g.ndata['h']
tensor([[1.], [1.], [1.], [1.], [1.], [0.]])
We can also assign features for the new nodes in adding new nodes.
>>> g.add_nodes(1, {'h': torch.ones(1, 1), 'w': torch.ones(1, 1)})
>>> g.ndata['h']
tensor([[1.], [1.], [1.], [1.], [1.], [0.], [1.]])
Since ``data`` contains new feature fields, the features for old nodes
will be created by initializers defined with :func:`set_n_initializer`.
>>> g.ndata['w']
tensor([[0.], [0.], [0.], [0.], [0.], [0.], [1.]])
**Heterogeneous Graphs with Multiple Node Types**
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.add_nodes(2)
DGLError: Node type name must be specified
if there are more than one node types.
>>> g.num_nodes('user')
3
>>> g.add_nodes(2, ntype='user')
>>> g.num_nodes('user')
5
See Also
--------
remove_nodes
add_edges
remove_edges
"""
# TODO(xiangsx): block do not support add_nodes
if ntype is None:
if self._graph.number_of_ntypes() != 1:
raise DGLError(
"Node type name must be specified if there are more than one "
"node types."
)
# nothing happen
if num == 0:
return
assert num > 0, "Number of new nodes should be larger than one."
ntid = self.get_ntype_id(ntype)
# update graph idx
metagraph = self._graph.metagraph
num_nodes_per_type = []
for c_ntype in self.ntypes:
if self.get_ntype_id(c_ntype) == ntid:
num_nodes_per_type.append(self.num_nodes(c_ntype) + num)
else:
num_nodes_per_type.append(self.num_nodes(c_ntype))
relation_graphs = []
for c_etype in self.canonical_etypes:
# src or dst == ntype, update the relation graph
if (
self.get_ntype_id(c_etype[0]) == ntid
or self.get_ntype_id(c_etype[2]) == ntid
):
u, v = self.edges(form="uv", order="eid", etype=c_etype)
hgidx = heterograph_index.create_unitgraph_from_coo(
1 if c_etype[0] == c_etype[2] else 2,
self.num_nodes(c_etype[0])
+ (num if self.get_ntype_id(c_etype[0]) == ntid else 0),
self.num_nodes(c_etype[2])
+ (num if self.get_ntype_id(c_etype[2]) == ntid else 0),
u,
v,
["coo", "csr", "csc"],
)
relation_graphs.append(hgidx)
else:
# do nothing
relation_graphs.append(
self._graph.get_relation_graph(self.get_etype_id(c_etype))
)
hgidx = heterograph_index.create_heterograph_from_relations(
metagraph,
relation_graphs,
utils.toindex(num_nodes_per_type, "int64"),
)
self._graph = hgidx
# update data frames
if data is None:
# Initialize feature with :func:`set_n_initializer`
self._node_frames[ntid].add_rows(num)
else:
self._node_frames[ntid].append(data)
self._reset_cached_info()
def add_edges(self, u, v, data=None, etype=None):
r"""Add multiple new edges for the specified edge type
The i-th new edge will be from ``u[i]`` to ``v[i]``.
Parameters
----------
u : int, tensor, numpy.ndarray, list
Source node IDs, ``u[i]`` gives the source node for the i-th new edge.
v : int, tensor, numpy.ndarray, list
Destination node IDs, ``v[i]`` gives the destination node for the i-th new edge.
data : dict, optional
Feature data of the added edges. The i-th row of the feature data
corresponds to the i-th new edge.
etype : str or tuple of str, optional
The type of the new edges. Can be omitted if there is
only one edge type in the graph.
Notes
-----
* Inplace update is applied to the current graph.
* If end nodes of adding edges does not exists, add_nodes is invoked
to add new nodes. The node features of the new nodes will be created
by initializers defined with :func:`set_n_initializer` (default
initializer fills zeros). In certain cases, it is recommanded to
add_nodes first and then add_edges.
* If the key of ``data`` does not contain some existing feature fields,
those features for the new edges will be created by initializers
defined with :func:`set_n_initializer` (default initializer fills zeros).
* If the key of ``data`` contains new feature fields, those features for
the old edges will be created by initializers defined with
:func:`set_n_initializer` (default initializer fills zeros).
* This function discards the batch information. Please use
:func:`dgl.DGLGraph.set_batch_num_nodes`
and :func:`dgl.DGLGraph.set_batch_num_edges` on the transformed graph
to maintain the information.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Homogeneous Graphs or Heterogeneous Graphs with A Single Edge Type**
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.num_edges()
2
>>> g.add_edges(torch.tensor([1, 3]), torch.tensor([0, 1]))
>>> g.num_edges()
4
Since ``u`` or ``v`` contains a non-existing node ID, the nodes are
added implicitly.
>>> g.num_nodes()
4
If the graph has some edge features and new edges are added without
features, their features will be created by initializers defined
with :func:`set_n_initializer`.
>>> g.edata['h'] = torch.ones(4, 1)
>>> g.add_edges(torch.tensor([1]), torch.tensor([1]))
>>> g.edata['h']
tensor([[1.], [1.], [1.], [1.], [0.]])
We can also assign features for the new edges in adding new edges.
>>> g.add_edges(torch.tensor([0, 0]), torch.tensor([2, 2]),
... {'h': torch.tensor([[1.], [2.]]), 'w': torch.ones(2, 1)})
>>> g.edata['h']
tensor([[1.], [1.], [1.], [1.], [0.], [1.], [2.]])
Since ``data`` contains new feature fields, the features for old edges
will be created by initializers defined with :func:`set_n_initializer`.
>>> g.edata['w']
tensor([[0.], [0.], [0.], [0.], [0.], [1.], [1.]])
**Heterogeneous Graphs with Multiple Edge Types**
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.add_edges(torch.tensor([3]), torch.tensor([3]))
DGLError: Edge type name must be specified
if there are more than one edge types.
>>> g.num_edges('plays')
4
>>> g.add_edges(torch.tensor([3]), torch.tensor([3]), etype='plays')
>>> g.num_edges('plays')
5
See Also
--------
add_nodes
remove_nodes
remove_edges
"""
# TODO(xiangsx): block do not support add_edges
u = utils.prepare_tensor(self, u, "u")
v = utils.prepare_tensor(self, v, "v")
if etype is None:
if self._graph.number_of_etypes() != 1:
raise DGLError(
"Edge type name must be specified if there are more than one "
"edge types."
)
# nothing changed
if len(u) == 0 or len(v) == 0:
return
assert len(u) == len(v) or len(u) == 1 or len(v) == 1, (
"The number of source nodes and the number of destination nodes should be same, "
"or either the number of source nodes or the number of destination nodes is 1."
)
if len(u) == 1 and len(v) > 1:
u = F.full_1d(
len(v), F.as_scalar(u), dtype=F.dtype(u), ctx=F.context(u)
)
if len(v) == 1 and len(u) > 1:
v = F.full_1d(
len(u), F.as_scalar(v), dtype=F.dtype(v), ctx=F.context(v)
)
u_type, e_type, v_type = self.to_canonical_etype(etype)
# if end nodes of adding edges does not exists
# use add_nodes to add new nodes first.
num_of_u = self.num_nodes(u_type)
num_of_v = self.num_nodes(v_type)
u_max = F.as_scalar(F.max(u, dim=0)) + 1
v_max = F.as_scalar(F.max(v, dim=0)) + 1
if u_type == v_type:
num_nodes = max(u_max, v_max)
if num_nodes > num_of_u:
self.add_nodes(num_nodes - num_of_u, ntype=u_type)
else:
if u_max > num_of_u:
self.add_nodes(u_max - num_of_u, ntype=u_type)
if v_max > num_of_v:
self.add_nodes(v_max - num_of_v, ntype=v_type)
# metagraph is not changed
metagraph = self._graph.metagraph
num_nodes_per_type = []
for ntype in self.ntypes:
num_nodes_per_type.append(self.num_nodes(ntype))
# update graph idx
relation_graphs = []
for c_etype in self.canonical_etypes:
# the target edge type
if c_etype == (u_type, e_type, v_type):
old_u, old_v = self.edges(form="uv", order="eid", etype=c_etype)
hgidx = heterograph_index.create_unitgraph_from_coo(
1 if u_type == v_type else 2,
self.num_nodes(u_type),
self.num_nodes(v_type),
F.cat([old_u, u], dim=0),
F.cat([old_v, v], dim=0),
["coo", "csr", "csc"],
)
relation_graphs.append(hgidx)
else:
# do nothing
# Note: node range change has been handled in add_nodes()
relation_graphs.append(
self._graph.get_relation_graph(self.get_etype_id(c_etype))
)
hgidx = heterograph_index.create_heterograph_from_relations(
metagraph,
relation_graphs,
utils.toindex(num_nodes_per_type, "int64"),
)
self._graph = hgidx
# handle data
etid = self.get_etype_id(etype)
if data is None:
self._edge_frames[etid].add_rows(len(u))
else:
self._edge_frames[etid].append(data)
self._reset_cached_info()
def remove_edges(self, eids, etype=None, store_ids=False):
r"""Remove multiple edges with the specified edge type
Nodes will not be removed. After removing edges, the rest
edges will be re-indexed using consecutive integers from 0,
with their relative order preserved.
The features for the removed edges will be removed accordingly.
Parameters
----------
eids : int, tensor, numpy.ndarray, list
IDs for the edges to remove.
etype : str or tuple of str, optional
The type of the edges to remove. Can be omitted if there is
only one edge type in the graph.
store_ids : bool, optional
If True, it will store the raw IDs of the extracted nodes and edges in the ``ndata``
and ``edata`` of the resulting graph under name ``dgl.NID`` and ``dgl.EID``,
respectively.
Notes
-----
This function preserves the batch information.
Examples
--------
>>> import dgl
>>> import torch
**Homogeneous Graphs or Heterogeneous Graphs with A Single Edge Type**
>>> g = dgl.graph((torch.tensor([0, 0, 2]), torch.tensor([0, 1, 2])))
>>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
>>> g.remove_edges(torch.tensor([0, 1]))
>>> g
Graph(num_nodes=3, num_edges=1,
ndata_schemes={}
edata_schemes={'he': Scheme(shape=(1,), dtype=torch.float32)})
>>> g.edges('all')
(tensor([2]), tensor([2]), tensor([0]))
>>> g.edata['he']
tensor([[2.]])
Removing edges from a batched graph preserves batch information.
>>> g = dgl.graph((torch.tensor([0, 0, 2]), torch.tensor([0, 1, 2])))
>>> g2 = dgl.graph((torch.tensor([1, 2, 3]), torch.tensor([1, 3, 4])))
>>> bg = dgl.batch([g, g2])
>>> bg.batch_num_edges()
tensor([3, 3])
>>> bg.remove_edges([1, 4])
>>> bg.batch_num_edges()
tensor([2, 2])
**Heterogeneous Graphs with Multiple Edge Types**
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.remove_edges(torch.tensor([0, 1]))
DGLError: Edge type name must be specified
if there are more than one edge types.
>>> g.remove_edges(torch.tensor([0, 1]), 'plays')
>>> g.edges('all', etype='plays')
(tensor([0, 1]), tensor([0, 0]), tensor([0, 1]))
See Also
--------
add_nodes
add_edges
remove_nodes
"""
# TODO(xiangsx): block do not support remove_edges
if etype is None:
if self._graph.number_of_etypes() != 1:
raise DGLError(
"Edge type name must be specified if there are more than one "
"edge types."
)
eids = utils.prepare_tensor(self, eids, "u")
if len(eids) == 0:
# no edge to delete
return
assert self.num_edges(etype) > F.as_scalar(
F.max(eids, dim=0)
), "The input eid {} is out of the range [0:{})".format(
F.as_scalar(F.max(eids, dim=0)), self.num_edges(etype)
)
# edge_subgraph
edges = {}
u_type, e_type, v_type = self.to_canonical_etype(etype)
for c_etype in self.canonical_etypes:
# the target edge type
if c_etype == (u_type, e_type, v_type):
origin_eids = self.edges(form="eid", order="eid", etype=c_etype)
edges[c_etype] = utils.compensate(eids, origin_eids)
else:
edges[c_etype] = self.edges(
form="eid", order="eid", etype=c_etype
)
# If the graph is batched, update batch_num_edges
batched = self._batch_num_edges is not None
if batched:
c_etype = (u_type, e_type, v_type)
one_hot_removed_edges = F.zeros(
(self.num_edges(c_etype),), F.float32, self.device
)
one_hot_removed_edges = F.scatter_row(
one_hot_removed_edges,
eids,
F.full_1d(len(eids), 1.0, F.float32, self.device),
)
c_etype_batch_num_edges = self._batch_num_edges[c_etype]
batch_num_removed_edges = segment.segment_reduce(
c_etype_batch_num_edges, one_hot_removed_edges, reducer="sum"
)
self._batch_num_edges[c_etype] = c_etype_batch_num_edges - F.astype(
batch_num_removed_edges, self.idtype
)
sub_g = self.edge_subgraph(
edges, relabel_nodes=False, store_ids=store_ids
)
self._graph = sub_g._graph
self._node_frames = sub_g._node_frames
self._edge_frames = sub_g._edge_frames
def remove_nodes(self, nids, ntype=None, store_ids=False):
r"""Remove multiple nodes with the specified node type
Edges that connect to the nodes will be removed as well. After removing
nodes and edges, the rest nodes and edges will be re-indexed using
consecutive integers from 0, with their relative order preserved.
The features for the removed nodes/edges will be removed accordingly.
Parameters
----------
nids : int, tensor, numpy.ndarray, list
Nodes to remove.
ntype : str, optional
The type of the nodes to remove. Can be omitted if there is
only one node type in the graph.
store_ids : bool, optional
If True, it will store the raw IDs of the extracted nodes and edges in the ``ndata``
and ``edata`` of the resulting graph under name ``dgl.NID`` and ``dgl.EID``,
respectively.
Notes
-----
This function preserves the batch information.
Examples
--------
>>> import dgl
>>> import torch
**Homogeneous Graphs or Heterogeneous Graphs with A Single Node Type**
>>> g = dgl.graph((torch.tensor([0, 0, 2]), torch.tensor([0, 1, 2])))
>>> g.ndata['hv'] = torch.arange(3).float().reshape(-1, 1)
>>> g.edata['he'] = torch.arange(3).float().reshape(-1, 1)
>>> g.remove_nodes(torch.tensor([0, 1]))
>>> g
Graph(num_nodes=1, num_edges=1,
ndata_schemes={'hv': Scheme(shape=(1,), dtype=torch.float32)}
edata_schemes={'he': Scheme(shape=(1,), dtype=torch.float32)})
>>> g.ndata['hv']
tensor([[2.]])
>>> g.edata['he']
tensor([[2.]])
Removing nodes from a batched graph preserves batch information.
>>> g = dgl.graph((torch.tensor([0, 0, 2]), torch.tensor([0, 1, 2])))
>>> g2 = dgl.graph((torch.tensor([1, 2, 3]), torch.tensor([1, 3, 4])))
>>> bg = dgl.batch([g, g2])
>>> bg.batch_num_nodes()
tensor([3, 5])
>>> bg.remove_nodes([1, 4])
>>> bg.batch_num_nodes()
tensor([2, 4])
>>> bg.batch_num_edges()
tensor([2, 2])
**Heterogeneous Graphs with Multiple Node Types**
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.remove_nodes(torch.tensor([0, 1]))
DGLError: Node type name must be specified
if there are more than one node types.
>>> g.remove_nodes(torch.tensor([0, 1]), ntype='game')
>>> g.num_nodes('user')
3
>>> g.num_nodes('game')
0
>>> g.num_edges('plays')
0
See Also
--------
add_nodes
add_edges
remove_edges
"""
# TODO(xiangsx): block do not support remove_nodes
if ntype is None:
if self._graph.number_of_ntypes() != 1:
raise DGLError(
"Node type name must be specified if there are more than one "
"node types."
)
nids = utils.prepare_tensor(self, nids, "u")
if len(nids) == 0:
# no node to delete
return
assert self.num_nodes(ntype) > F.as_scalar(
F.max(nids, dim=0)
), "The input nids {} is out of the range [0:{})".format(
F.as_scalar(F.max(nids, dim=0)), self.num_nodes(ntype)
)
ntid = self.get_ntype_id(ntype)
nodes = {}
for c_ntype in self.ntypes:
if self.get_ntype_id(c_ntype) == ntid:
target_ntype = c_ntype
original_nids = self.nodes(c_ntype)
nodes[c_ntype] = utils.compensate(nids, original_nids)
else:
nodes[c_ntype] = self.nodes(c_ntype)
# If the graph is batched, update batch_num_nodes
batched = self._batch_num_nodes is not None
if batched:
one_hot_removed_nodes = F.zeros(
(self.num_nodes(target_ntype),), F.float32, self.device
)
one_hot_removed_nodes = F.scatter_row(
one_hot_removed_nodes,
nids,
F.full_1d(len(nids), 1.0, F.float32, self.device),
)
c_ntype_batch_num_nodes = self._batch_num_nodes[target_ntype]
batch_num_removed_nodes = segment.segment_reduce(
c_ntype_batch_num_nodes, one_hot_removed_nodes, reducer="sum"
)
self._batch_num_nodes[
target_ntype
] = c_ntype_batch_num_nodes - F.astype(
batch_num_removed_nodes, self.idtype
)
# Record old num_edges to check later whether some edges were removed
old_num_edges = {
c_etype: self._graph.num_edges(self.get_etype_id(c_etype))
for c_etype in self.canonical_etypes
}
# node_subgraph
# If batch_num_edges is to be updated, record the original edge IDs
sub_g = self.subgraph(nodes, store_ids=store_ids or batched)
self._graph = sub_g._graph
self._node_frames = sub_g._node_frames
self._edge_frames = sub_g._edge_frames
# If the graph is batched, update batch_num_edges
if batched:
canonical_etypes = [
c_etype
for c_etype in self.canonical_etypes
if self._graph.num_edges(self.get_etype_id(c_etype))
!= old_num_edges[c_etype]
]
for c_etype in canonical_etypes:
if self._graph.num_edges(self.get_etype_id(c_etype)) == 0:
self._batch_num_edges[c_etype] = F.zeros(
(self.batch_size,), self.idtype, self.device
)
continue
one_hot_left_edges = F.zeros(
(old_num_edges[c_etype],), F.float32, self.device
)
eids = self.edges[c_etype].data[EID]
one_hot_left_edges = F.scatter_row(
one_hot_left_edges,
eids,
F.full_1d(len(eids), 1.0, F.float32, self.device),
)
batch_num_left_edges = segment.segment_reduce(
self._batch_num_edges[c_etype],
one_hot_left_edges,
reducer="sum",
)
self._batch_num_edges[c_etype] = F.astype(
batch_num_left_edges, self.idtype
)
if batched and not store_ids:
for c_ntype in self.ntypes:
self.nodes[c_ntype].data.pop(NID)
for c_etype in self.canonical_etypes:
self.edges[c_etype].data.pop(EID)
def _reset_cached_info(self):
"""Some info like batch_num_nodes may be stale after mutation
Clean these cached info
"""
self._batch_num_nodes = None
self._batch_num_edges = None
#################################################################
# Metagraph query
#################################################################
@property
def is_unibipartite(self):
"""Return whether the graph is a uni-bipartite graph.
A uni-bipartite heterograph can further divide its node types into two sets:
SRC and DST. All edges are from nodes in SRC to nodes in DST. The following APIs
can be used to get the type, data, and nodes that belong to SRC and DST sets:
* :func:`srctype` and :func:`dsttype`
* :func:`srcdata` and :func:`dstdata`
* :func:`srcnodes` and :func:`dstnodes`
Note that we allow two node types to have the same name as long as one
belongs to SRC while the other belongs to DST. To distinguish them, prepend
the name with ``"SRC/"`` or ``"DST/"`` when specifying a node type.
"""
return self._is_unibipartite
@property
def ntypes(self):
"""Return all the node type names in the graph.
Returns
-------
list[str]
All the node type names in a list.
Notes
-----
DGL internally assigns an integer ID for each node type. The returned
node type names are sorted according to their IDs.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
>>> g.ntypes
['game', 'user']
"""
return self._ntypes
@property
def etypes(self):
"""Return all the edge type names in the graph.
Returns
-------
list[str]
All the edge type names in a list.
Notes
-----
DGL internally assigns an integer ID for each edge type. The returned
edge type names are sorted according to their IDs.
The complete format to specify an relation is a string triplet ``(str, str, str)``
for source node type, edge type and destination node type. DGL calls this
format *canonical edge type*. An edge type can appear in multiple canonical edge types.
For example, ``'interacts'`` can appear in two canonical edge types
``('drug', 'interacts', 'drug')`` and ``('protein', 'interacts', 'protein')``.
See Also
--------
canonical_etypes
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
>>> g.etypes
['follows', 'follows', 'plays']
"""
return self._etypes
@property
def canonical_etypes(self):
"""Return all the canonical edge types in the graph.
A canonical edge type is a string triplet ``(str, str, str)``
for source node type, edge type and destination node type.
Returns
-------
list[(str, str, str)]
All the canonical edge type triplets in a list.
Notes
-----
DGL internally assigns an integer ID for each edge type. The returned
edge type names are sorted according to their IDs.
See Also
--------
etypes
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
>>> g.canonical_etypes
[('user', 'follows', 'user'),
('user', 'follows', 'game'),
('user', 'plays', 'game')]
"""
return self._canonical_etypes
@property
def srctypes(self):
"""Return all the source node type names in this graph.
If the graph can further divide its node types into two subsets A and B where
all the edeges are from nodes of types in A to nodes of types in B, we call
this graph a *uni-bipartite* graph and the nodes in A being the *source*
nodes and the ones in B being the *destination* nodes. If the graph is not
uni-bipartite, the source and destination nodes are just the entire set of
nodes in the graph.
Returns
-------
list[str]
All the source node type names in a list.
See Also
--------
dsttypes
is_unibipartite
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for a uni-bipartite graph.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
>>> g.srctypes
['developer', 'user']
Query for a graph that is not uni-bipartite.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
>>> g.srctypes
['developer', 'game', 'user']
"""
if self.is_unibipartite:
return sorted(list(self._srctypes_invmap.keys()))
else:
return self.ntypes
@property
def dsttypes(self):
"""Return all the destination node type names in this graph.
If the graph can further divide its node types into two subsets A and B where
all the edeges are from nodes of types in A to nodes of types in B, we call
this graph a *uni-bipartite* graph and the nodes in A being the *source*
nodes and the ones in B being the *destination* nodes. If the graph is not
uni-bipartite, the source and destination nodes are just the entire set of
nodes in the graph.
Returns
-------
list[str]
All the destination node type names in a list.
See Also
--------
srctypes
is_unibipartite
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for a uni-bipartite graph.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
>>> g.dsttypes
['game']
Query for a graph that is not uni-bipartite.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
>>> g.dsttypes
['developer', 'game', 'user']
"""
if self.is_unibipartite:
return sorted(list(self._dsttypes_invmap.keys()))
else:
return self.ntypes
def metagraph(self):
"""Return the metagraph of the heterograph.
The metagraph (or network schema) of a heterogeneous network specifies type constraints
on the sets of nodes and edges between the nodes. For a formal definition, refer to
`Yizhou et al. <https://www.kdd.org/exploration_files/V14-02-03-Sun.pdf>`_.
Returns
-------
networkx.MultiDiGraph
The metagraph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
>>> meta_g = g.metagraph()
>>> meta_g.nodes()
NodeView(('user', 'game'))
>>> meta_g.edges()
OutMultiEdgeDataView([('user', 'user'), ('user', 'game'), ('user', 'game')])
"""
nx_graph = self._graph.metagraph.to_networkx()
nx_metagraph = nx.MultiDiGraph()
for u_v in nx_graph.edges:
srctype, etype, dsttype = self.canonical_etypes[
nx_graph.edges[u_v]["id"]
]
nx_metagraph.add_edge(srctype, dsttype, etype)
return nx_metagraph
def to_canonical_etype(self, etype):
"""Convert an edge type to the corresponding canonical edge type in the graph.
A canonical edge type is a string triplet ``(str, str, str)``
for source node type, edge type and destination node type.
The function expects the given edge type name can uniquely identify a canonical edge
type. DGL will raise error if this is not the case.
Parameters
----------
etype : str or (str, str, str)
If :attr:`etype` is an edge type (str), it returns the corresponding canonical edge
type in the graph. If :attr:`etype` is already a canonical edge type,
it directly returns the input unchanged.
Returns
-------
(str, str, str)
The canonical edge type corresponding to the edge type.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a heterograph.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [1, 2]),
... ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1]),
... ('developer', 'follows', 'game'): ([0, 1], [0, 1])
... })
Map an edge type to its corresponding canonical edge type.
>>> g.to_canonical_etype('plays')
('user', 'plays', 'game')
>>> g.to_canonical_etype(('user', 'plays', 'game'))
('user', 'plays', 'game')
See Also
--------
canonical_etypes
"""
if etype is None:
if len(self.etypes) != 1:
raise DGLError(
"Edge type name must be specified if there are more than one "
"edge types."
)
etype = self.etypes[0]
if isinstance(etype, tuple):
return etype
else:
ret = self._etype2canonical.get(etype, None)
if ret is None:
raise DGLError('Edge type "{}" does not exist.'.format(etype))
if len(ret) == 0:
raise DGLError(
'Edge type "%s" is ambiguous. Please use canonical edge type '
"in the form of (srctype, etype, dsttype)" % etype
)
return ret
def get_ntype_id(self, ntype):
"""Return the ID of the given node type.
ntype can also be None. If so, there should be only one node type in the
graph.
Parameters
----------
ntype : str
Node type
Returns
-------
int
"""
if self.is_unibipartite and ntype is not None:
# Only check 'SRC/' and 'DST/' prefix when is_unibipartite graph is True.
if ntype.startswith("SRC/"):
return self.get_ntype_id_from_src(ntype[4:])
elif ntype.startswith("DST/"):
return self.get_ntype_id_from_dst(ntype[4:])
# If there is no prefix, fallback to normal lookup.
# Lookup both SRC and DST
if ntype is None:
if self.is_unibipartite or len(self._srctypes_invmap) != 1:
raise DGLError(
"Node type name must be specified if there are more than one "
"node types."
)
return 0
ntid = self._srctypes_invmap.get(
ntype, self._dsttypes_invmap.get(ntype, None)
)
if ntid is None:
raise DGLError('Node type "{}" does not exist.'.format(ntype))
return ntid
def get_ntype_id_from_src(self, ntype):
"""Internal function to return the ID of the given SRC node type.
ntype can also be None. If so, there should be only one node type in the
SRC category. Callable even when the self graph is not uni-bipartite.
Parameters
----------
ntype : str
Node type
Returns
-------
int
"""
if ntype is None:
if len(self._srctypes_invmap) != 1:
raise DGLError(
"SRC node type name must be specified if there are more than one "
"SRC node types."
)
return next(iter(self._srctypes_invmap.values()))
ntid = self._srctypes_invmap.get(ntype, None)
if ntid is None:
raise DGLError('SRC node type "{}" does not exist.'.format(ntype))
return ntid
def get_ntype_id_from_dst(self, ntype):
"""Internal function to return the ID of the given DST node type.
ntype can also be None. If so, there should be only one node type in the
DST category. Callable even when the self graph is not uni-bipartite.
Parameters
----------
ntype : str
Node type
Returns
-------
int
"""
if ntype is None:
if len(self._dsttypes_invmap) != 1:
raise DGLError(
"DST node type name must be specified if there are more than one "
"DST node types."
)
return next(iter(self._dsttypes_invmap.values()))
ntid = self._dsttypes_invmap.get(ntype, None)
if ntid is None:
raise DGLError('DST node type "{}" does not exist.'.format(ntype))
return ntid
def get_etype_id(self, etype):
"""Return the id of the given edge type.
etype can also be None. If so, there should be only one edge type in the
graph.
Parameters
----------
etype : str or tuple of str
Edge type
Returns
-------
int
"""
if etype is None:
if self._graph.number_of_etypes() != 1:
raise DGLError(
"Edge type name must be specified if there are more than one "
"edge types."
)
return 0
etid = self._etypes_invmap.get(self.to_canonical_etype(etype), None)
if etid is None:
raise DGLError('Edge type "{}" does not exist.'.format(etype))
return etid
#################################################################
# Batching
#################################################################
@property
def batch_size(self):
"""Return the number of graphs in the batched graph.
Returns
-------
int
The Number of graphs in the batch. If the graph is not a batched one,
it will return 1.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for homogeneous graphs.
>>> g1 = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g1.batch_size
1
>>> g2 = dgl.graph((torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])))
>>> bg = dgl.batch([g1, g2])
>>> bg.batch_size
2
Query for heterogeneous graphs.
>>> hg1 = dgl.heterograph({
... ('user', 'plays', 'game') : (torch.tensor([0, 1]), torch.tensor([0, 0]))})
>>> hg1.batch_size
1
>>> hg2 = dgl.heterograph({
... ('user', 'plays', 'game') : (torch.tensor([0, 0]), torch.tensor([1, 0]))})
>>> bg = dgl.batch([hg1, hg2])
>>> bg.batch_size
2
"""
return len(self.batch_num_nodes(self.ntypes[0]))
def batch_num_nodes(self, ntype=None):
"""Return the number of nodes for each graph in the batch with the specified node type.
Parameters
----------
ntype : str, optional
The node type for query. If the graph has multiple node types, one must
specify the argument. Otherwise, it can be omitted. If the graph is not a batched
one, it will return a list of length 1 that holds the number of nodes in the graph.
Returns
-------
Tensor
The number of nodes with the specified type for each graph in the batch. The i-th
element of it is the number of nodes with the specified type for the i-th graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for homogeneous graphs.
>>> g1 = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g1.batch_num_nodes()
tensor([4])
>>> g2 = dgl.graph((torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])))
>>> bg = dgl.batch([g1, g2])
>>> bg.batch_num_nodes()
tensor([4, 3])
Query for heterogeneous graphs.
>>> hg1 = dgl.heterograph({
... ('user', 'plays', 'game') : (torch.tensor([0, 1]), torch.tensor([0, 0]))})
>>> hg2 = dgl.heterograph({
... ('user', 'plays', 'game') : (torch.tensor([0, 0]), torch.tensor([1, 0]))})
>>> bg = dgl.batch([hg1, hg2])
>>> bg.batch_num_nodes('user')
tensor([2, 1])
"""
if ntype is not None and ntype not in self.ntypes:
raise DGLError(
"Expect ntype in {}, got {}".format(self.ntypes, ntype)
)
if self._batch_num_nodes is None:
self._batch_num_nodes = {}
for ty in self.ntypes:
bnn = F.copy_to(
F.tensor([self.num_nodes(ty)], self.idtype), self.device
)
self._batch_num_nodes[ty] = bnn
if ntype is None:
if len(self.ntypes) != 1:
raise DGLError(
"Node type name must be specified if there are more than one "
"node types."
)
ntype = self.ntypes[0]
return self._batch_num_nodes[ntype]
def set_batch_num_nodes(self, val):
"""Manually set the number of nodes for each graph in the batch with the specified node
type.
Parameters
----------
val : Tensor or Mapping[str, Tensor]
The dictionary storing number of nodes for each graph in the batch for all node types.
If the graph has only one node type, ``val`` can also be a single array indicating the
number of nodes per graph in the batch.
Notes
-----
This API is always used together with ``set_batch_num_edges`` to specify batching
information of a graph, it also do not check the correspondance between the graph structure
and batching information and user must guarantee there will be no cross-graph edges in the
batch.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph(([0, 1, 2, 3, 4, 5], [1, 2, 0, 4, 5, 3]))
Manually set batch information
>>> g.set_batch_num_nodes(torch.tensor([3, 3]))
>>> g.set_batch_num_edges(torch.tensor([3, 3]))
Unbatch the graph.
>>> dgl.unbatch(g)
[Graph(num_nodes=3, num_edges=3,
ndata_schemes={}
edata_schemes={}), Graph(num_nodes=3, num_edges=3,
ndata_schemes={}
edata_schemes={})]
Create a heterogeneous graph.
>>> hg = dgl.heterograph({
... ('user', 'plays', 'game') : ([0, 1, 2, 3, 4, 5], [0, 1, 1, 3, 3, 2]),
... ('developer', 'develops', 'game') : ([0, 1, 2, 3], [1, 0, 3, 2])})
Manually set batch information.
>>> hg.set_batch_num_nodes({
... 'user': torch.tensor([3, 3]),
... 'game': torch.tensor([2, 2]),
... 'developer': torch.tensor([2, 2])})
>>> hg.set_batch_num_edges({
... ('user', 'plays', 'game'): torch.tensor([3, 3]),
... ('developer', 'develops', 'game'): torch.tensor([2, 2])})
Unbatch the graph.
>>> g1, g2 = dgl.unbatch(hg)
>>> g1
Graph(num_nodes={'developer': 2, 'game': 2, 'user': 3},
num_edges={('developer', 'develops', 'game'): 2, ('user', 'plays', 'game'): 3},
metagraph=[('developer', 'game', 'develops'), ('user', 'game', 'plays')])
>>> g2
Graph(num_nodes={'developer': 2, 'game': 2, 'user': 3},
num_edges={('developer', 'develops', 'game'): 2, ('user', 'plays', 'game'): 3},
metagraph=[('developer', 'game', 'develops'), ('user', 'game', 'plays')])
See Also
--------
set_batch_num_edges
batch
unbatch
"""
val = utils.prepare_tensor_or_dict(self, val, "batch_num_nodes")
if not isinstance(val, Mapping):
if len(self.ntypes) != 1:
raise DGLError(
"Must provide a dictionary when there are multiple node types."
)
val = {self.ntypes[0]: val}
self._batch_num_nodes = val
def batch_num_edges(self, etype=None):
"""Return the number of edges for each graph in the batch with the specified edge type.
Parameters
----------
etype : str or tuple of str, optional
The edge type for query, which can be an edge type (str) or a canonical edge type
(3-tuple of str). When an edge type appears in multiple canonical edge types, one
must use a canonical edge type. If the graph has multiple edge types, one must
specify the argument. Otherwise, it can be omitted.
Returns
-------
Tensor
The number of edges with the specified type for each graph in the batch. The i-th
element of it is the number of edges with the specified type for the i-th graph.
If the graph is not a batched one, it will return a list of length 1 that holds
the number of edges in the graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for homogeneous graphs.
>>> g1 = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g1.batch_num_edges()
tensor([3])
>>> g2 = dgl.graph((torch.tensor([0, 0, 0, 1]), torch.tensor([0, 1, 2, 0])))
>>> bg = dgl.batch([g1, g2])
>>> bg.batch_num_edges()
tensor([3, 4])
Query for heterogeneous graphs.
>>> hg1 = dgl.heterograph({
... ('user', 'plays', 'game') : (torch.tensor([0, 1]), torch.tensor([0, 0]))})
>>> hg2 = dgl.heterograph({
... ('user', 'plays', 'game') : (torch.tensor([0, 0]), torch.tensor([1, 0]))})
>>> bg = dgl.batch([hg1, hg2])
>>> bg.batch_num_edges('plays')
tensor([2, 2])
"""
if self._batch_num_edges is None:
self._batch_num_edges = {}
for ty in self.canonical_etypes:
bne = F.copy_to(
F.tensor([self.num_edges(ty)], self.idtype), self.device
)
self._batch_num_edges[ty] = bne
if etype is None:
if len(self.etypes) != 1:
raise DGLError(
"Edge type name must be specified if there are more than one "
"edge types."
)
etype = self.canonical_etypes[0]
else:
etype = self.to_canonical_etype(etype)
return self._batch_num_edges[etype]
def set_batch_num_edges(self, val):
"""Manually set the number of edges for each graph in the batch with the specified edge
type.
Parameters
----------
val : Tensor or Mapping[str, Tensor]
The dictionary storing number of edges for each graph in the batch for all edge types.
If the graph has only one edge type, ``val`` can also be a single array indicating the
number of edges per graph in the batch.
Notes
-----
This API is always used together with ``set_batch_num_nodes`` to specify batching
information of a graph, it also do not check the correspondance between the graph structure
and batching information and user must guarantee there will be no cross-graph edges in the
batch.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph(([0, 1, 2, 3, 4, 5], [1, 2, 0, 4, 5, 3]))
Manually set batch information
>>> g.set_batch_num_nodes(torch.tensor([3, 3]))
>>> g.set_batch_num_edges(torch.tensor([3, 3]))
Unbatch the graph.
>>> dgl.unbatch(g)
[Graph(num_nodes=3, num_edges=3,
ndata_schemes={}
edata_schemes={}), Graph(num_nodes=3, num_edges=3,
ndata_schemes={}
edata_schemes={})]
Create a heterogeneous graph.
>>> hg = dgl.heterograph({
... ('user', 'plays', 'game') : ([0, 1, 2, 3, 4, 5], [0, 1, 1, 3, 3, 2]),
... ('developer', 'develops', 'game') : ([0, 1, 2, 3], [1, 0, 3, 2])})
Manually set batch information.
>>> hg.set_batch_num_nodes({
... 'user': torch.tensor([3, 3]),
... 'game': torch.tensor([2, 2]),
... 'developer': torch.tensor([2, 2])})
>>> hg.set_batch_num_edges(
... {('user', 'plays', 'game'): torch.tensor([3, 3]),
... ('developer', 'develops', 'game'): torch.tensor([2, 2])})
Unbatch the graph.
>>> g1, g2 = dgl.unbatch(hg)
>>> g1
Graph(num_nodes={'developer': 2, 'game': 2, 'user': 3},
num_edges={('developer', 'develops', 'game'): 2, ('user', 'plays', 'game'): 3},
metagraph=[('developer', 'game', 'develops'), ('user', 'game', 'plays')])
>>> g2
Graph(num_nodes={'developer': 2, 'game': 2, 'user': 3},
num_edges={('developer', 'develops', 'game'): 2, ('user', 'plays', 'game'): 3},
metagraph=[('developer', 'game', 'develops'), ('user', 'game', 'plays')])
See Also
--------
set_batch_num_nodes
batch
unbatch
"""
val = utils.prepare_tensor_or_dict(self, val, "batch_num_edges")
if not isinstance(val, Mapping):
if len(self.etypes) != 1:
raise DGLError(
"Must provide a dictionary when there are multiple edge types."
)
val = {self.canonical_etypes[0]: val}
self._batch_num_edges = val
#################################################################
# View
#################################################################
def get_node_storage(self, key, ntype=None):
"""Get storage object of node feature of type :attr:`ntype` and name :attr:`key`."""
return self._node_frames[self.get_ntype_id(ntype)]._columns[key]
def get_edge_storage(self, key, etype=None):
"""Get storage object of edge feature of type :attr:`etype` and name :attr:`key`."""
return self._edge_frames[self.get_etype_id(etype)]._columns[key]
@property
def nodes(self):
"""Return a node view
One can use it for:
1. Getting the node IDs for a single node type.
2. Setting/getting features for all nodes of a single node type.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph and a heterogeneous graph of two node types.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
Get the node IDs of the homogeneous graph.
>>> g.nodes()
tensor([0, 1, 2])
Get the node IDs of the heterogeneous graph. With multiple node types introduced,
one needs to specify the node type for query.
>>> hg.nodes('user')
tensor([0, 1, 2, 3, 4])
Set and get a feature 'h' for all nodes of a single type in the heterogeneous graph.
>>> hg.nodes['user'].data['h'] = torch.ones(5, 1)
>>> hg.nodes['user'].data['h']
tensor([[1.], [1.], [1.], [1.], [1.]])
To set node features for a graph with a single node type, use :func:`DGLGraph.ndata`.
See Also
--------
ndata
"""
# Todo (Mufei) Replace the syntax g.nodes[...].ndata[...] with g.nodes[...][...]
return HeteroNodeView(self, self.get_ntype_id)
@property
def srcnodes(self):
"""Return a node view for source nodes
If the graph is a uni-bipartite graph (see :func:`is_unibipartite` for reference),
this is :func:`nodes` restricted to source node types. Otherwise, it is an alias
for :func:`nodes`.
One can use it for:
1. Getting the node IDs for a single node type.
2. Setting/getting features for all nodes of a single node type.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a uni-bipartite graph.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
Get the node IDs for source node types.
>>> g.srcnodes('user')
tensor([0])
>>> g.srcnodes('developer')
tensor([0, 1])
Set/get features for source node types.
>>> g.srcnodes['user'].data['h'] = torch.ones(1, 1)
>>> g.srcnodes['user'].data['h']
tensor([[1.]])
Create a graph that is not uni-bipartite.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
:func:`dgl.DGLGraph.srcnodes` falls back to :func:`dgl.DGLGraph.nodes` and one can
get the node IDs for both source and destination node types.
>>> g.srcnodes('game')
tensor([0, 1, 2])
One can also set/get features for destination node types in this case.
>>> g.srcnodes['game'].data['h'] = torch.ones(3, 1)
>>> g.srcnodes['game'].data['h']
tensor([[1.],
[1.],
[1.]])
See Also
--------
srcdata
"""
return HeteroNodeView(self, self.get_ntype_id_from_src)
@property
def dstnodes(self):
"""Return a node view for destination nodes
If the graph is a uni-bipartite graph (see :func:`is_unibipartite` for reference),
this is :func:`nodes` restricted to destination node types. Otherwise, it is an alias
for :func:`nodes`.
One can use it for:
1. Getting the node IDs for a single node type.
2. Setting/getting features for all nodes of a single node type.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a uni-bipartite graph.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
Get the node IDs for destination node types.
>>> g.dstnodes('game')
tensor([0, 1, 2])
Set/get features for destination node types.
>>> g.dstnodes['game'].data['h'] = torch.ones(3, 1)
>>> g.dstnodes['game'].data['h']
tensor([[1.],
[1.],
[1.]])
Create a graph that is not uni-bipartite.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0]), torch.tensor([1])),
... ('developer', 'develops', 'game'): (torch.tensor([1]), torch.tensor([2]))
... })
:func:`dgl.DGLGraph.dstnodes` falls back to :func:`dgl.DGLGraph.nodes` and one can
get the node IDs for both source and destination node types.
>>> g.dstnodes('developer')
tensor([0, 1])
One can also set/get features for source node types in this case.
>>> g.dstnodes['developer'].data['h'] = torch.ones(2, 1)
>>> g.dstnodes['developer'].data['h']
tensor([[1.],
[1.]])
See Also
--------
dstdata
"""
return HeteroNodeView(self, self.get_ntype_id_from_dst)
@property
def ndata(self):
"""Return a node data view for setting/getting node features
Let ``g`` be a DGLGraph. If ``g`` is a graph of a single node type, ``g.ndata[feat]``
returns the node feature associated with the name ``feat``. One can also set a node
feature associated with the name ``feat`` by setting ``g.ndata[feat]`` to a tensor.
If ``g`` is a graph of multiple node types, ``g.ndata[feat]`` returns a
dict[str, Tensor] mapping node types to the node features associated with the name
``feat`` for the corresponding type. One can also set a node feature associated
with the name ``feat`` for some node type(s) by setting ``g.ndata[feat]`` to a
dictionary as described.
Notes
-----
For setting features, the device of the features must be the same as the device
of the graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Set and get feature 'h' for a graph of a single node type.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.ndata['h'] = torch.ones(3, 1)
>>> g.ndata['h']
tensor([[1.],
[1.],
[1.]])
Set and get feature 'h' for a graph of multiple node types.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
... ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
... })
>>> g.ndata['h'] = {'game': torch.zeros(2, 1), 'player': torch.ones(3, 1)}
>>> g.ndata['h']
{'game': tensor([[0.], [0.]]),
'player': tensor([[1.], [1.], [1.]])}
>>> g.ndata['h'] = {'game': torch.ones(2, 1)}
>>> g.ndata['h']
{'game': tensor([[1.], [1.]]),
'player': tensor([[1.], [1.], [1.]])}
See Also
--------
nodes
"""
if len(self.ntypes) == 1:
ntid = self.get_ntype_id(None)
ntype = self.ntypes[0]
return HeteroNodeDataView(self, ntype, ntid, ALL)
else:
ntids = [self.get_ntype_id(ntype) for ntype in self.ntypes]
ntypes = self.ntypes
return HeteroNodeDataView(self, ntypes, ntids, ALL)
@property
def srcdata(self):
"""Return a node data view for setting/getting source node features.
Let ``g`` be a DGLGraph. If ``g`` is a graph of a single source node type,
``g.srcdata[feat]`` returns the source node feature associated with the name ``feat``.
One can also set a source node feature associated with the name ``feat`` by
setting ``g.srcdata[feat]`` to a tensor.
If ``g`` is a graph of multiple source node types, ``g.srcdata[feat]`` returns a
dict[str, Tensor] mapping source node types to the node features associated with
the name ``feat`` for the corresponding type. One can also set a node feature
associated with the name ``feat`` for some source node type(s) by setting
``g.srcdata[feat]`` to a dictionary as described.
Notes
-----
For setting features, the device of the features must be the same as the device
of the graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Set and get feature 'h' for a graph of a single source node type.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1]), torch.tensor([1, 2]))})
>>> g.srcdata['h'] = torch.ones(2, 1)
>>> g.srcdata['h']
tensor([[1.],
[1.]])
Set and get feature 'h' for a graph of multiple source node types.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
... ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
... })
>>> g.srcdata['h'] = {'user': torch.zeros(3, 1), 'player': torch.ones(3, 1)}
>>> g.srcdata['h']
{'player': tensor([[1.], [1.], [1.]]),
'user': tensor([[0.], [0.], [0.]])}
>>> g.srcdata['h'] = {'user': torch.ones(3, 1)}
>>> g.srcdata['h']
{'player': tensor([[1.], [1.], [1.]]),
'user': tensor([[1.], [1.], [1.]])}
See Also
--------
nodes
ndata
srcnodes
"""
if len(self.srctypes) == 1:
ntype = self.srctypes[0]
ntid = self.get_ntype_id_from_src(ntype)
return HeteroNodeDataView(self, ntype, ntid, ALL)
else:
ntypes = self.srctypes
ntids = [self.get_ntype_id_from_src(ntype) for ntype in ntypes]
return HeteroNodeDataView(self, ntypes, ntids, ALL)
@property
def dstdata(self):
"""Return a node data view for setting/getting destination node features.
Let ``g`` be a DGLGraph. If ``g`` is a graph of a single destination node type,
``g.dstdata[feat]`` returns the destination node feature associated with the name
``feat``. One can also set a destination node feature associated with the name
``feat`` by setting ``g.dstdata[feat]`` to a tensor.
If ``g`` is a graph of multiple destination node types, ``g.dstdata[feat]`` returns a
dict[str, Tensor] mapping destination node types to the node features associated with
the name ``feat`` for the corresponding type. One can also set a node feature
associated with the name ``feat`` for some destination node type(s) by setting
``g.dstdata[feat]`` to a dictionary as described.
Notes
-----
For setting features, the device of the features must be the same as the device
of the graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Set and get feature 'h' for a graph of a single destination node type.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1]), torch.tensor([1, 2]))})
>>> g.dstdata['h'] = torch.ones(3, 1)
>>> g.dstdata['h']
tensor([[1.],
[1.],
[1.]])
Set and get feature 'h' for a graph of multiple destination node types.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([1, 2]), torch.tensor([1, 2])),
... ('user', 'watches', 'movie'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
... })
>>> g.dstdata['h'] = {'game': torch.zeros(3, 1), 'movie': torch.ones(2, 1)}
>>> g.dstdata['h']
{'game': tensor([[0.], [0.], [0.]]),
'movie': tensor([[1.], [1.]])}
>>> g.dstdata['h'] = {'game': torch.ones(3, 1)}
>>> g.dstdata['h']
{'game': tensor([[1.], [1.], [1.]]),
'movie': tensor([[1.], [1.]])}
See Also
--------
nodes
ndata
dstnodes
"""
if len(self.dsttypes) == 1:
ntype = self.dsttypes[0]
ntid = self.get_ntype_id_from_dst(ntype)
return HeteroNodeDataView(self, ntype, ntid, ALL)
else:
ntypes = self.dsttypes
ntids = [self.get_ntype_id_from_dst(ntype) for ntype in ntypes]
return HeteroNodeDataView(self, ntypes, ntids, ALL)
@property
def edges(self):
"""Return an edge view
One can use it for:
1. Getting the edges for a single edge type. In this case, it can take the
following optional arguments:
- form : str, optional
The return form, which can be one of the following:
- ``'uv'`` (default): The returned result is a 2-tuple of 1D tensors
:math:`(U, V)`, representing the source and destination nodes of all edges.
For each :math:`i`, :math:`(U[i], V[i])` forms an edge.
- ``'eid'``: The returned result is a 1D tensor :math:`EID`, representing
the IDs of all edges.
- ``'all'``: The returned result is a 3-tuple of 1D tensors :math:`(U, V, EID)`,
representing the source nodes, destination nodes and IDs of all edges.
For each :math:`i`, :math:`(U[i], V[i])` forms an edge with ID :math:`EID[i]`.
- order : str, optional
The order of the returned edges, which can be one of the following:
- ``'eid'`` (default): The edges are sorted by their IDs.
- ``'srcdst'``: The edges are sorted first by their source node IDs and then
by their destination node IDs to break ties.
- etype : str or tuple of str, optional
The edge type for query, which can be an edge type (str) or a canonical edge
type (3-tuple of str). When an edge type appears in multiple canonical edge
types, one must use a canonical edge type. If the graph has multiple edge
types, one must specify the argument. Otherwise, it can be omitted.
2. Setting/getting features for all edges of a single edge type. To set/get a feature
``feat`` for edges of type ``etype`` in a graph ``g``, one can use
``g.edges[etype].data[feat]``.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Get the Edges for a Single Edge Type**
Create a graph with a single edge type.
>>> g = dgl.graph((torch.tensor([1, 0, 0]), torch.tensor([1, 1, 0])))
>>> g.edges()
(tensor([1, 0, 0]), tensor([1, 1, 0]))
Specify a different value for :attr:`form` and :attr:`order`.
>>> g.edges(form='all', order='srcdst')
(tensor([0, 0, 1]), tensor([0, 1, 1]), tensor([2, 1, 0]))
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.edges(etype='plays')
(tensor([3, 4]), tensor([5, 6]))
**Set/get Features for All Edges of a Single Edge Type**
Create a heterogeneous graph of two edge types.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
Set and get a feature 'h' for all edges of a single type in the heterogeneous graph.
>>> hg.edges['follows'].data['h'] = torch.ones(2, 1)
>>> hg.edges['follows'].data['h']
tensor([[1.], [1.]])
To set edge features for a graph with a single edge type, use :func:`DGLGraph.edata`.
See Also
--------
edata
"""
# TODO(Mufei): Replace the syntax g.edges[...].edata[...] with g.edges[...][...]
return HeteroEdgeView(self)
@property
def edata(self):
"""Return an edge data view for setting/getting edge features.
Let ``g`` be a DGLGraph. If ``g`` is a graph of a single edge type, ``g.edata[feat]``
returns the edge feature associated with the name ``feat``. One can also set an
edge feature associated with the name ``feat`` by setting ``g.edata[feat]`` to a tensor.
If ``g`` is a graph of multiple edge types, ``g.edata[feat]`` returns a
dict[str, Tensor] mapping canonical edge types to the edge features associated with
the name ``feat`` for the corresponding type. One can also set an edge feature
associated with the name ``feat`` for some edge type(s) by setting
``g.edata[feat]`` to a dictionary as described.
Notes
-----
For setting features, the device of the features must be the same as the device
of the graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Set and get feature 'h' for a graph of a single edge type.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.edata['h'] = torch.ones(2, 1)
>>> g.edata['h']
tensor([[1.],
[1.]])
Set and get feature 'h' for a graph of multiple edge types.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([1, 2]), torch.tensor([3, 4])),
... ('user', 'plays', 'user'): (torch.tensor([2, 2]), torch.tensor([1, 1])),
... ('player', 'plays', 'game'): (torch.tensor([2, 2]), torch.tensor([1, 1]))
... })
>>> g.edata['h'] = {('user', 'follows', 'user'): torch.zeros(2, 1),
... ('user', 'plays', 'user'): torch.ones(2, 1)}
>>> g.edata['h']
{('user', 'follows', 'user'): tensor([[0.], [0.]]),
('user', 'plays', 'user'): tensor([[1.], [1.]])}
>>> g.edata['h'] = {('user', 'follows', 'user'): torch.ones(2, 1)}
>>> g.edata['h']
{('user', 'follows', 'user'): tensor([[1.], [1.]]),
('user', 'plays', 'user'): tensor([[1.], [1.]])}
See Also
--------
edges
"""
if len(self.canonical_etypes) == 1:
return HeteroEdgeDataView(self, None, ALL)
else:
return HeteroEdgeDataView(self, self.canonical_etypes, ALL)
def _find_etypes(self, key):
etypes = [
i
for i, (srctype, etype, dsttype) in enumerate(
self._canonical_etypes
)
if (key[0] == SLICE_FULL or key[0] == srctype)
and (key[1] == SLICE_FULL or key[1] == etype)
and (key[2] == SLICE_FULL or key[2] == dsttype)
]
return etypes
def __getitem__(self, key):
"""Return the relation slice of this graph.
You can get a relation slice with ``self[srctype, etype, dsttype]``, where
``srctype``, ``etype``, and ``dsttype`` can be either a string or a full
slice (``:``) representing wildcard (i.e. any source/edge/destination type).
A relation slice is a homogeneous (with one node type and one edge type) or
bipartite (with two node types and one edge type) graph, transformed from
the original heterogeneous graph.
If there is only one canonical edge type found, then the returned relation
slice would be a subgraph induced from the original graph. That is, it is
equivalent to ``self.edge_type_subgraph(etype)``. The node and edge features
of the returned graph would be shared with thew original graph.
If there are multiple canonical edge types found, then the source/edge/destination
node types would be a *concatenation* of original node/edge types. The
new source/destination node type would have the concatenation determined by
:func:`dgl.combine_names() <dgl.combine_names>` called on original source/destination
types as its name. The source/destination node would be formed by concatenating the
common features of the original source/destination types. Therefore they are not
shared with the original graph. Edge type is similar.
Parameters
----------
key : str or tuple
Either a string representing the edge type name, or a tuple in the form of
``(srctype, etype, dsttype)`` where ``srctype``, ``etype``, ``dsttype`` can be either
strings representing type names or a full slice object (`:`).
Returns
-------
DGLGraph
The relation slice.
Notes
-----
This function returns a new graph. Changing the content of this graph does not reflect
onto the original graph.
If the graph combines multiple node types or edge types together, it will have the
mapping of node/edge types and IDs from the new graph to the original graph.
The mappings have the name ``dgl.NTYPE``, ``dgl.NID``, ``dgl.ETYPE`` and ``dgl.EID``,
similar to the function :func:`dgl.to_homogenenous`.
Examples
--------
>>> g = dgl.heterograph({
... ('A1', 'AB1', 'B'): ([0, 1, 2], [1, 2, 3]),
... ('A1', 'AB2', 'B'): ([1, 2, 3], [3, 4, 5]),
... ('A2', 'AB2', 'B'): ([1, 3, 5], [2, 4, 6])})
>>> new_g = g['A1', :, 'B'] # combines all edge types between A1 and B
>>> new_g
Graph(num_nodes={'A1': 4, 'B': 7},
num_edges={('A1', 'AB1+AB2', 'B'): 6},
metagraph=[('A1', 'B', 'AB1+AB2')])
>>> new_g.edges()
(tensor([0, 1, 2, 1, 2, 3]), tensor([1, 2, 3, 3, 4, 5]))
>>> new_g2 = g[:, 'AB2', 'B'] # combines all node types that are source of AB2
>>> new_g2
Graph(num_nodes={'A1+A2': 10, 'B': 7},
num_edges={('A1+A2', 'AB2+AB2', 'B'): 6},
metagraph=[('A1+A2', 'B', 'AB2+AB2')])
>>> new_g2.edges()
(tensor([1, 2, 3, 5, 7, 9]), tensor([3, 4, 5, 2, 4, 6]))
If a combination of multiple node types and edge types occur, one can find
the mapping to the original node type and IDs like the following:
>>> new_g1.edges['AB1+AB2'].data[dgl.EID]
tensor([0, 1, 2, 0, 1, 2])
>>> new_g1.edges['AB1+AB2'].data[dgl.ETYPE]
tensor([0, 0, 0, 1, 1, 1])
>>> new_g2.nodes['A1+A2'].data[dgl.NID]
tensor([0, 1, 2, 3, 0, 1, 2, 3, 4, 5])
>>> new_g2.nodes['A1+A2'].data[dgl.NTYPE]
tensor([0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
"""
err_msg = (
"Invalid slice syntax. Use G['etype'] or G['srctype', 'etype', 'dsttype'] "
+ "to get view of one relation type. Use : to slice multiple types (e.g. "
+ "G['srctype', :, 'dsttype'])."
)
orig_key = key
if not isinstance(key, tuple):
key = (SLICE_FULL, key, SLICE_FULL)
if len(key) != 3:
raise DGLError(err_msg)
etypes = self._find_etypes(key)
if len(etypes) == 0:
raise DGLError(
'Invalid key "{}". Must be one of the edge types.'.format(
orig_key
)
)
if len(etypes) == 1:
# no ambiguity: return the unitgraph itself
srctype, etype, dsttype = self._canonical_etypes[etypes[0]]
stid = self.get_ntype_id_from_src(srctype)
etid = self.get_etype_id((srctype, etype, dsttype))
dtid = self.get_ntype_id_from_dst(dsttype)
new_g = self._graph.get_relation_graph(etid)
if stid == dtid:
new_ntypes = [srctype]
new_nframes = [self._node_frames[stid]]
else:
new_ntypes = ([srctype], [dsttype])
new_nframes = [self._node_frames[stid], self._node_frames[dtid]]
new_etypes = [etype]
new_eframes = [self._edge_frames[etid]]
return self.__class__(
new_g, new_ntypes, new_etypes, new_nframes, new_eframes
)
else:
flat = self._graph.flatten_relations(etypes)
new_g = flat.graph
# merge frames
stids = flat.induced_srctype_set.asnumpy()
dtids = flat.induced_dsttype_set.asnumpy()
etids = flat.induced_etype_set.asnumpy()
new_ntypes = [combine_names(self.ntypes, stids)]
if new_g.number_of_ntypes() == 2:
new_ntypes.append(combine_names(self.ntypes, dtids))
new_nframes = [
combine_frames(self._node_frames, stids),
combine_frames(self._node_frames, dtids),
]
else:
assert np.array_equal(stids, dtids)
new_nframes = [combine_frames(self._node_frames, stids)]
new_etypes = [combine_names(self.etypes, etids)]
new_eframes = [combine_frames(self._edge_frames, etids)]
# create new heterograph
new_hg = self.__class__(
new_g, new_ntypes, new_etypes, new_nframes, new_eframes
)
src = new_ntypes[0]
dst = new_ntypes[1] if new_g.number_of_ntypes() == 2 else src
# put the parent node/edge type and IDs
new_hg.nodes[src].data[NTYPE] = F.zerocopy_from_dgl_ndarray(
flat.induced_srctype
)
new_hg.nodes[src].data[NID] = F.zerocopy_from_dgl_ndarray(
flat.induced_srcid
)
new_hg.nodes[dst].data[NTYPE] = F.zerocopy_from_dgl_ndarray(
flat.induced_dsttype
)
new_hg.nodes[dst].data[NID] = F.zerocopy_from_dgl_ndarray(
flat.induced_dstid
)
new_hg.edata[ETYPE] = F.zerocopy_from_dgl_ndarray(
flat.induced_etype
)
new_hg.edata[EID] = F.zerocopy_from_dgl_ndarray(flat.induced_eid)
return new_hg
#################################################################
# Graph query
#################################################################
def number_of_nodes(self, ntype=None):
"""Alias of :meth:`num_nodes`"""
return self.num_nodes(ntype)
def num_nodes(self, ntype=None):
"""Return the number of nodes in the graph.
Parameters
----------
ntype : str, optional
The node type name. If given, it returns the number of nodes of the
type. If not given (default), it returns the total number of nodes of all types.
Returns
-------
int
The number of nodes.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a graph with two node types -- 'user' and 'game'.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
Query for the number of nodes.
>>> g.num_nodes('user')
5
>>> g.num_nodes('game')
7
>>> g.num_nodes()
12
"""
if ntype is None:
return sum(
[
self._graph.num_nodes(ntid)
for ntid in range(len(self.ntypes))
]
)
else:
return self._graph.num_nodes(self.get_ntype_id(ntype))
def number_of_src_nodes(self, ntype=None):
"""Alias of :meth:`num_src_nodes`"""
return self.num_src_nodes(ntype)
def num_src_nodes(self, ntype=None):
"""Return the number of source nodes in the graph.
If the graph can further divide its node types into two subsets A and B where
all the edeges are from nodes of types in A to nodes of types in B, we call
this graph a *uni-bipartite* graph and the nodes in A being the *source*
nodes and the ones in B being the *destination* nodes. If the graph is not
uni-bipartite, the source and destination nodes are just the entire set of
nodes in the graph.
Parameters
----------
ntype : str, optional
The source node type name. If given, it returns the number of nodes for
the source node type. If not given (default), it returns the number of
nodes summed over all source node types.
Returns
-------
int
The number of nodes
See Also
--------
num_dst_nodes
is_unibipartite
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph for query.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.num_src_nodes()
3
Create a heterogeneous graph with two source node types -- 'developer' and 'user'.
>>> g = dgl.heterograph({
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
Query for the number of nodes.
>>> g.num_src_nodes('developer')
2
>>> g.num_src_nodes('user')
5
>>> g.num_src_nodes()
7
"""
if ntype is None:
return sum(
[
self._graph.num_nodes(self.get_ntype_id_from_src(nty))
for nty in self.srctypes
]
)
else:
return self._graph.num_nodes(self.get_ntype_id_from_src(ntype))
def number_of_dst_nodes(self, ntype=None):
"""Alias of :func:`num_dst_nodes`"""
return self.num_dst_nodes(ntype)
def num_dst_nodes(self, ntype=None):
"""Return the number of destination nodes in the graph.
If the graph can further divide its node types into two subsets A and B where
all the edeges are from nodes of types in A to nodes of types in B, we call
this graph a *uni-bipartite* graph and the nodes in A being the *source*
nodes and the ones in B being the *destination* nodes. If the graph is not
uni-bipartite, the source and destination nodes are just the entire set of
nodes in the graph.
Parameters
----------
ntype : str, optional
The destination node type name. If given, it returns the number of nodes of
the destination node type. If not given (default), it returns the number of
nodes summed over all the destination node types.
Returns
-------
int
The number of nodes
See Also
--------
num_src_nodes
is_unibipartite
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph for query.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.num_dst_nodes()
3
Create a heterogeneous graph with two destination node types -- 'user' and 'game'.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
Query for the number of nodes.
>>> g.num_dst_nodes('user')
5
>>> g.num_dst_nodes('game')
7
>>> g.num_dst_nodes()
12
"""
if ntype is None:
return sum(
[
self._graph.num_nodes(self.get_ntype_id_from_dst(nty))
for nty in self.dsttypes
]
)
else:
return self._graph.num_nodes(self.get_ntype_id_from_dst(ntype))
def number_of_edges(self, etype=None):
"""Alias of :func:`num_edges`"""
return self.num_edges(etype)
def num_edges(self, etype=None):
"""Return the number of edges in the graph.
Parameters
----------
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
If not provided, return the total number of edges regardless of the types
in the graph.
Returns
-------
int
The number of edges.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a graph with three canonical edge types.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
Query for the number of edges.
>>> g.num_edges('plays')
2
>>> g.num_edges()
7
Use a canonical edge type instead when there is ambiguity for an edge type.
>>> g.num_edges(('user', 'follows', 'user'))
2
>>> g.num_edges(('user', 'follows', 'game'))
3
"""
if etype is None:
return sum(
[
self._graph.num_edges(etid)
for etid in range(len(self.canonical_etypes))
]
)
else:
return self._graph.num_edges(self.get_etype_id(etype))
@property
def is_multigraph(self):
"""Return whether the graph is a multigraph with parallel edges.
A multigraph has more than one edges between the same pair of nodes, called
*parallel edges*. For heterogeneous graphs, parallel edge further requires
the canonical edge type to be the same (see :meth:`canonical_etypes` for the
definition).
Returns
-------
bool
True if the graph is a multigraph.
Notes
-----
Checking whether the graph is a multigraph could be expensive for a large one.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Check for homogeneous graphs.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 3])))
>>> g.is_multigraph
False
>>> g = dgl.graph((torch.tensor([0, 1, 1]), torch.tensor([1, 3, 3])))
>>> g.is_multigraph
True
Check for heterogeneous graphs.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3]))
... })
>>> g.is_multigraph
False
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1, 1]), torch.tensor([1, 2, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3]))
... })
>>> g.is_multigraph
True
"""
return self._graph.is_multigraph()
@property
def is_homogeneous(self):
"""Return whether the graph is a homogeneous graph.
A homogeneous graph only has one node type and one edge type.
Returns
-------
bool
True if the graph is a homogeneous graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph for check.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
>>> g.is_homogeneous
True
Create a heterogeneous graph for check.
If the graph has multiple edge types, one need to specify the edge type.
>>> g = dgl.heterograph({
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3]))})
>>> g.is_homogeneous
False
"""
return len(self.ntypes) == 1 and len(self.etypes) == 1
@property
def idtype(self):
"""The data type for storing the structure-related graph information
such as node and edge IDs.
Returns
-------
Framework-specific device object
For example, this can be ``torch.int32`` or ``torch.int64`` for PyTorch.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> src_ids = torch.tensor([0, 0, 1])
>>> dst_ids = torch.tensor([1, 2, 2])
>>> g = dgl.graph((src_ids, dst_ids))
>>> g.idtype
torch.int64
>>> g = dgl.graph((src_ids, dst_ids), idtype=torch.int32)
>>> g.idtype
torch.int32
See Also
--------
long
int
"""
return getattr(F, self._graph.dtype)
@property
def _idtype_str(self):
"""The dtype of graph index
Returns
-------
backend dtype object
th.int32/th.int64 or tf.int32/tf.int64 etc.
"""
return self._graph.dtype
def has_nodes(self, vid, ntype=None):
"""Return whether the graph contains the given nodes.
Parameters
----------
vid : node ID(s)
The nodes IDs. The allowed nodes ID formats are:
* ``int``: The ID of a single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
ntype : str, optional
The node type name. Can be omitted if there is
only one type of nodes in the graph.
Returns
-------
bool or bool Tensor
A tensor of bool flags where each element is True if the node is in the graph.
If the input is a single node, return one bool value.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a graph with two node types -- 'user' and 'game'.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([0, 1]))
... })
Query for the nodes.
>>> g.has_nodes(0, 'user')
True
>>> g.has_nodes(3, 'game')
False
>>> g.has_nodes(torch.tensor([3, 0, 1]), 'game')
tensor([False, True, True])
"""
vid_tensor = utils.prepare_tensor(self, vid, "vid")
if len(vid_tensor) > 0 and F.as_scalar(F.min(vid_tensor, 0)) < 0 < len(
vid_tensor
):
raise DGLError("All IDs must be non-negative integers.")
ret = self._graph.has_nodes(self.get_ntype_id(ntype), vid_tensor)
if isinstance(vid, numbers.Integral):
return bool(F.as_scalar(ret))
else:
return F.astype(ret, F.bool)
def has_edges_between(self, u, v, etype=None):
"""Return whether the graph contains the given edges.
Parameters
----------
u : node IDs
The source node IDs of the edges. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
v : node IDs
The destination node IDs of the edges. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
bool or bool Tensor
A tensor of bool flags where each element is True if the node is in the graph.
If the input is a single node, return one bool value.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
Query for the edges.
>>> g.has_edges_between(1, 2)
True
>>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]))
tensor([ True, False])
If the graph has multiple edge types, one need to specify the edge type.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
>>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]), 'plays')
tensor([ True, False])
Use a canonical edge type instead when there is ambiguity for an edge type.
>>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]),
... ('user', 'follows', 'user'))
tensor([ True, False])
>>> g.has_edges_between(torch.tensor([1, 2]), torch.tensor([2, 3]),
... ('user', 'follows', 'game'))
tensor([True, True])
"""
srctype, _, dsttype = self.to_canonical_etype(etype)
u_tensor = utils.prepare_tensor(self, u, "u")
if F.as_scalar(
F.sum(self.has_nodes(u_tensor, ntype=srctype), dim=0)
) != len(u_tensor):
raise DGLError("u contains invalid node IDs")
v_tensor = utils.prepare_tensor(self, v, "v")
if F.as_scalar(
F.sum(self.has_nodes(v_tensor, ntype=dsttype), dim=0)
) != len(v_tensor):
raise DGLError("v contains invalid node IDs")
ret = self._graph.has_edges_between(
self.get_etype_id(etype), u_tensor, v_tensor
)
if isinstance(u, numbers.Integral) and isinstance(v, numbers.Integral):
return bool(F.as_scalar(ret))
else:
return F.astype(ret, F.bool)
def predecessors(self, v, etype=None):
"""Return the predecessor(s) of a particular node with the specified edge type.
Node ``u`` is a predecessor of node ``v`` if there is an edge ``(u, v)`` with type
``etype`` in the graph.
Parameters
----------
v : int
The node ID. If the graph has multiple edge types, the ID is for the destination
type corresponding to the edge type.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor
The predecessors of :attr:`v` with the specified edge type.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 2, 3])))
Query for node 1.
>>> g.predecessors(1)
tensor([0, 0])
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.predecessors(1, etype='follows')
tensor([0])
See Also
--------
successors
"""
if not self.has_nodes(v, self.to_canonical_etype(etype)[-1]):
raise DGLError("Non-existing node ID {}".format(v))
return self._graph.predecessors(self.get_etype_id(etype), v)
def successors(self, v, etype=None):
"""Return the successor(s) of a particular node with the specified edge type.
Node ``u`` is a successor of node ``v`` if there is an edge ``(v, u)`` with type
``etype`` in the graph.
Parameters
----------
v : int
The node ID. If the graph has multiple edge types, the ID is for the source
type corresponding to the edge type.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor
The successors of :attr:`v` with the specified edge type.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 2, 3])))
Query for node 1.
>>> g.successors(1)
tensor([2, 3])
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.successors(1, etype='follows')
tensor([2])
See Also
--------
predecessors
"""
if not self.has_nodes(v, self.to_canonical_etype(etype)[0]):
raise DGLError("Non-existing node ID {}".format(v))
return self._graph.successors(self.get_etype_id(etype), v)
def edge_ids(self, u, v, return_uv=False, etype=None):
"""Return the edge ID(s) given the two endpoints of the edge(s).
Parameters
----------
u : node IDs
The source node IDs of the edges. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
v : node IDs
The destination node IDs of the edges. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
return_uv : bool, optional
Whether to return the source and destination node IDs along with the edges. If
False (default), it assumes that the graph is a simple graph and there is only
one edge from one node to another. If True, there can be multiple edges found
from one node to another.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor, or (Tensor, Tensor, Tensor)
* If ``return_uv=False``, it returns the edge IDs in a tensor, where the i-th
element is the ID of the edge ``(u[i], v[i])``.
* If ``return_uv=True``, it returns a tuple of three 1D tensors ``(eu, ev, e)``.
``e[i]`` is the ID of an edge from ``eu[i]`` to ``ev[i]``. It returns all edges
(including parallel edges) from ``eu[i]`` to ``ev[i]`` in this case.
Notes
-----
If the graph is a simple graph, ``return_uv=False``, and there are no edges
between some pairs of node(s), it will raise an error.
If the graph is a multigraph, ``return_uv=False``, and there are multiple edges
between some pairs of node(s), it returns an arbitrary one from them.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1, 1]), torch.tensor([1, 0, 2, 3, 2])))
Query for the edges.
>>> g.edge_ids(0, 0)
1
>>> g.edge_ids(torch.tensor([1, 0]), torch.tensor([3, 1]))
tensor([3, 0])
Get all edges for pairs of nodes.
>>> g.edge_ids(torch.tensor([1, 0]), torch.tensor([3, 1]), return_uv=True)
(tensor([1, 0]), tensor([3, 1]), tensor([3, 0]))
If the graph has multiple edge types, one need to specify the edge type.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'follows', 'game'): (torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])),
... ('user', 'plays', 'game'): (torch.tensor([1, 3]), torch.tensor([2, 3]))
... })
>>> g.edge_ids(torch.tensor([1]), torch.tensor([2]), etype='plays')
tensor([0])
Use a canonical edge type instead when there is ambiguity for an edge type.
>>> g.edge_ids(torch.tensor([0, 1]), torch.tensor([1, 2]),
... etype=('user', 'follows', 'user'))
tensor([0, 1])
>>> g.edge_ids(torch.tensor([1, 2]), torch.tensor([2, 3]),
... etype=('user', 'follows', 'game'))
tensor([1, 2])
"""
is_int = isinstance(u, numbers.Integral) and isinstance(
v, numbers.Integral
)
srctype, _, dsttype = self.to_canonical_etype(etype)
u = utils.prepare_tensor(self, u, "u")
if F.as_scalar(F.sum(self.has_nodes(u, ntype=srctype), dim=0)) != len(
u
):
raise DGLError("u contains invalid node IDs")
v = utils.prepare_tensor(self, v, "v")
if F.as_scalar(F.sum(self.has_nodes(v, ntype=dsttype), dim=0)) != len(
v
):
raise DGLError("v contains invalid node IDs")
if return_uv:
return self._graph.edge_ids_all(self.get_etype_id(etype), u, v)
else:
eid = self._graph.edge_ids_one(self.get_etype_id(etype), u, v)
is_neg_one = F.equal(eid, -1)
if F.as_scalar(F.sum(is_neg_one, 0)):
# Raise error since some (u, v) pair is not a valid edge.
idx = F.nonzero_1d(is_neg_one)
raise DGLError(
"Error: (%d, %d) does not form a valid edge."
% (
F.as_scalar(F.gather_row(u, idx)),
F.as_scalar(F.gather_row(v, idx)),
)
)
return F.as_scalar(eid) if is_int else eid
def find_edges(self, eid, etype=None):
"""Return the source and destination node ID(s) given the edge ID(s).
Parameters
----------
eid : edge ID(s)
The edge IDs. The allowed formats are:
* ``int``: A single ID.
* Int Tensor: Each element is an ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is an ID.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor
The source node IDs of the edges. The i-th element is the source node ID of
the i-th edge.
Tensor
The destination node IDs of the edges. The i-th element is the destination node
ID of the i-th edge.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
Find edges of IDs 0 and 2.
>>> g.find_edges(torch.tensor([0, 2]))
(tensor([0, 1]), tensor([1, 2]))
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.find_edges(torch.tensor([1, 0]), 'plays')
(tensor([4, 3]), tensor([6, 5]))
"""
eid = utils.prepare_tensor(self, eid, "eid")
if len(eid) > 0:
min_eid = F.as_scalar(F.min(eid, 0))
if min_eid < 0:
raise DGLError("Invalid edge ID {:d}".format(min_eid))
max_eid = F.as_scalar(F.max(eid, 0))
if max_eid >= self.num_edges(etype):
raise DGLError("Invalid edge ID {:d}".format(max_eid))
if len(eid) == 0:
empty = F.copy_to(F.tensor([], self.idtype), self.device)
return empty, empty
src, dst, _ = self._graph.find_edges(self.get_etype_id(etype), eid)
return src, dst
def in_edges(self, v, form="uv", etype=None):
"""Return the incoming edges of the given nodes.
Parameters
----------
v : node ID(s)
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
form : str, optional
The result format, which can be one of the following:
- ``'eid'``: The returned result is a 1D tensor :math:`EID`, representing
the IDs of all edges.
- ``'uv'`` (default): The returned result is a 2-tuple of 1D tensors :math:`(U, V)`,
representing the source and destination nodes of all edges. For each :math:`i`,
:math:`(U[i], V[i])` forms an edge.
- ``'all'``: The returned result is a 3-tuple of 1D tensors :math:`(U, V, EID)`,
representing the source nodes, destination nodes and IDs of all edges.
For each :math:`i`, :math:`(U[i], V[i])` forms an edge with ID :math:`EID[i]`.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor or (Tensor, Tensor) or (Tensor, Tensor, Tensor)
All incoming edges of the nodes with the specified type. For a description of the
returned result, see the description of :attr:`form`.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
Query for the nodes 1 and 0.
>>> g.in_edges(torch.tensor([1, 0]))
(tensor([0, 0]), tensor([1, 0]))
Specify a different value for :attr:`form`.
>>> g.in_edges(torch.tensor([1, 0]), form='all')
(tensor([0, 0]), tensor([1, 0]), tensor([0, 1]))
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.in_edges(torch.tensor([1, 0]), etype='follows')
(tensor([0]), tensor([1]))
See Also
--------
edges
out_edges
"""
v = utils.prepare_tensor(self, v, "v")
src, dst, eid = self._graph.in_edges(self.get_etype_id(etype), v)
if form == "all":
return src, dst, eid
elif form == "uv":
return src, dst
elif form == "eid":
return eid
else:
raise DGLError(
'Invalid form: {}. Must be "all", "uv" or "eid".'.format(form)
)
def out_edges(self, u, form="uv", etype=None):
"""Return the outgoing edges of the given nodes.
Parameters
----------
u : node ID(s)
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
form : str, optional
The return form, which can be one of the following:
- ``'eid'``: The returned result is a 1D tensor :math:`EID`, representing
the IDs of all edges.
- ``'uv'`` (default): The returned result is a 2-tuple of 1D tensors :math:`(U, V)`,
representing the source and destination nodes of all edges. For each :math:`i`,
:math:`(U[i], V[i])` forms an edge.
- ``'all'``: The returned result is a 3-tuple of 1D tensors :math:`(U, V, EID)`,
representing the source nodes, destination nodes and IDs of all edges.
For each :math:`i`, :math:`(U[i], V[i])` forms an edge with ID :math:`EID[i]`.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor or (Tensor, Tensor) or (Tensor, Tensor, Tensor)
All outgoing edges of the nodes with the specified type. For a description of the
returned result, see the description of :attr:`form`.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
Query for the nodes 1 and 2.
>>> g.out_edges(torch.tensor([1, 2]))
(tensor([1, 1]), tensor([2, 3]))
Specify a different value for :attr:`form`.
>>> g.out_edges(torch.tensor([1, 2]), form='all')
(tensor([1, 1]), tensor([2, 3]), tensor([2, 3]))
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.out_edges(torch.tensor([1, 2]), etype='follows')
(tensor([1]), tensor([2]))
See Also
--------
edges
in_edges
"""
u = utils.prepare_tensor(self, u, "u")
srctype, _, _ = self.to_canonical_etype(etype)
if F.as_scalar(F.sum(self.has_nodes(u, ntype=srctype), dim=0)) != len(
u
):
raise DGLError("u contains invalid node IDs")
src, dst, eid = self._graph.out_edges(self.get_etype_id(etype), u)
if form == "all":
return src, dst, eid
elif form == "uv":
return src, dst
elif form == "eid":
return eid
else:
raise DGLError(
'Invalid form: {}. Must be "all", "uv" or "eid".'.format(form)
)
def all_edges(self, form="uv", order="eid", etype=None):
"""Return all edges with the specified edge type.
Parameters
----------
form : str, optional
The return form, which can be one of the following:
- ``'eid'``: The returned result is a 1D tensor :math:`EID`, representing
the IDs of all edges.
- ``'uv'`` (default): The returned result is a 2-tuple of 1D tensors :math:`(U, V)`,
representing the source and destination nodes of all edges. For each :math:`i`,
:math:`(U[i], V[i])` forms an edge.
- ``'all'``: The returned result is a 3-tuple of 1D tensors :math:`(U, V, EID)`,
representing the source nodes, destination nodes and IDs of all edges.
For each :math:`i`, :math:`(U[i], V[i])` forms an edge with ID :math:`EID[i]`.
order : str, optional
The order of the returned edges, which can be one of the following:
- ``'srcdst'``: The edges are sorted first by their source node IDs and then
by their destination node IDs to break ties.
- ``'eid'`` (default): The edges are sorted by their IDs.
etype : str or tuple of str, optional
The edge type for query, which can be an edge type (str) or a canonical edge type
(3-tuple of str). When an edge type appears in multiple canonical edge types, one
must use a canonical edge type. If the graph has multiple edge types, one must
specify the argument. Otherwise, it can be omitted.
Returns
-------
Tensor or (Tensor, Tensor) or (Tensor, Tensor, Tensor)
All edges of the specified edge type. For a description of the returned result,
see the description of :attr:`form`.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 0, 2, 3])))
Query for edges.
>>> g.all_edges()
(tensor([0, 0, 1, 1]), tensor([1, 0, 2, 3]))
Specify a different value for :attr:`form` and :attr:`order`.
>>> g.all_edges(form='all', order='srcdst')
(tensor([0, 0, 1, 1]), tensor([0, 1, 2, 3]), tensor([1, 0, 2, 3]))
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.all_edges(etype='plays')
(tensor([3, 4]), tensor([5, 6]))
See Also
--------
edges
in_edges
out_edges
"""
src, dst, eid = self._graph.edges(self.get_etype_id(etype), order)
if form == "all":
return src, dst, eid
elif form == "uv":
return src, dst
elif form == "eid":
return eid
else:
raise DGLError(
'Invalid form: {}. Must be "all", "uv" or "eid".'.format(form)
)
def in_degrees(self, v=ALL, etype=None):
"""Return the in-degree(s) of the given nodes.
It computes the in-degree(s) w.r.t. to the edges of the given edge type.
Parameters
----------
v : node IDs
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
If not given, return the in-degrees of all the nodes.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
int or Tensor
The in-degree(s) of the node(s) in a Tensor. The i-th element is the in-degree
of the i-th input node. If :attr:`v` is an ``int``, return an ``int`` too.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 2, 3])))
Query for all nodes.
>>> g.in_degrees()
tensor([0, 2, 1, 1])
Query for nodes 1 and 2.
>>> g.in_degrees(torch.tensor([1, 2]))
tensor([2, 1])
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.in_degrees(torch.tensor([1, 0]), etype='follows')
tensor([1, 0])
See Also
--------
out_degrees
"""
dsttype = self.to_canonical_etype(etype)[2]
etid = self.get_etype_id(etype)
if is_all(v):
v = self.dstnodes(dsttype)
v_tensor = utils.prepare_tensor(self, v, "v")
deg = self._graph.in_degrees(etid, v_tensor)
if isinstance(v, numbers.Integral):
return F.as_scalar(deg)
else:
return deg
def out_degrees(self, u=ALL, etype=None):
"""Return the out-degree(s) of the given nodes.
It computes the out-degree(s) w.r.t. to the edges of the given edge type.
Parameters
----------
u : node IDs
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
If not given, return the in-degrees of all the nodes.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
int or Tensor
The out-degree(s) of the node(s) in a Tensor. The i-th element is the out-degree
of the i-th input node. If :attr:`v` is an ``int``, return an ``int`` too.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 0, 1, 1]), torch.tensor([1, 1, 2, 3])))
Query for all nodes.
>>> g.out_degrees()
tensor([2, 2, 0, 0])
Query for nodes 1 and 2.
>>> g.out_degrees(torch.tensor([1, 2]))
tensor([2, 0])
For a graph of multiple edge types, it is required to specify the edge type in query.
>>> hg = dgl.heterograph({
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2])),
... ('user', 'plays', 'game'): (torch.tensor([3, 4]), torch.tensor([5, 6]))
... })
>>> hg.out_degrees(torch.tensor([1, 0]), etype='follows')
tensor([1, 1])
See Also
--------
in_degrees
"""
srctype = self.to_canonical_etype(etype)[0]
etid = self.get_etype_id(etype)
if is_all(u):
u = self.srcnodes(srctype)
u_tensor = utils.prepare_tensor(self, u, "u")
if F.as_scalar(
F.sum(self.has_nodes(u_tensor, ntype=srctype), dim=0)
) != len(u_tensor):
raise DGLError("u contains invalid node IDs")
deg = self._graph.out_degrees(etid, utils.prepare_tensor(self, u, "u"))
if isinstance(u, numbers.Integral):
return F.as_scalar(deg)
else:
return deg
def adjacency_matrix(self, etype=None):
"""Alias of :meth:`adj`"""
return self.adj(etype)
def adj(self, etype=None, eweight_name=None):
"""Get the adjacency matrix of the graph.
Parameters
----------
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and
destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
eweight_name : str, optional
The name of edge feature used as the non-zero values. If not given,
the non-zero values are all 1.
Returns
-------
SparseMatrix
The adjacency matrix.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.graph(([0, 1, 2], [1, 2, 3]))
>>> g.adj()
SparseMatrix(indices=tensor([[0, 1, 2],
[1, 2, 3]]),
values=tensor([1., 1., 1.]),
shape=(4, 4), nnz=3)
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [0, 1]),
... ('developer', 'develops', 'game'): ([0, 1], [0, 2])
... })
>>> g.adj(etype='develops')
SparseMatrix(indices=tensor([[0, 1],
[0, 2]]),
values=tensor([1., 1.]),
shape=(2, 3), nnz=2)
>>> g.edata['h'] = {('user', 'follows', 'user'): torch.tensor([3, 2])}
>>> g.adj(etype='follows', eweight_name='h')
SparseMatrix(indices=tensor([[0, 1],
[0, 1]]),
values=tensor([3, 2]),
shape=(2, 2), nnz=2)
"""
assert F.backend_name == "pytorch", "Only PyTorch backend supports adj."
# Temporal fix to introduce a dependency on torch
import torch
from .sparse import spmatrix
etype = self.to_canonical_etype(etype)
indices = torch.stack(self.all_edges(etype=etype))
shape = (self.num_nodes(etype[0]), self.number_of_nodes(etype[2]))
if eweight_name is not None:
val = self.edata[eweight_name][etype]
else:
val = None
return spmatrix(
indices,
val=val,
shape=shape,
)
def adj_external(
self, transpose=False, ctx=F.cpu(), scipy_fmt=None, etype=None
):
"""Return the adjacency matrix in an external format, such as Scipy or
backend dependent sparse tensor.
By default, a row of returned adjacency matrix represents the
source of an edge and the column represents the destination.
When transpose is True, a row represents the destination and a column
represents the source.
Parameters
----------
transpose : bool, optional
A flag to transpose the returned adjacency matrix. (Default: False)
ctx : context, optional
The context of returned adjacency matrix. (Default: cpu)
scipy_fmt : str, optional
If specified, return a scipy sparse matrix in the given format.
Otherwise, return a backend dependent sparse tensor. (Default: None)
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
SparseTensor or scipy.sparse.spmatrix
Adjacency matrix.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Instantiate a heterogeneous graph.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [0, 1]),
... ('developer', 'develops', 'game'): ([0, 1], [0, 2])
... })
Get a backend dependent sparse tensor. Here we use PyTorch for example.
>>> g.adj_external(etype='develops')
tensor(indices=tensor([[0, 1],
[0, 2]]),
values=tensor([1., 1.]),
size=(2, 3), nnz=2, layout=torch.sparse_coo)
Get a scipy coo sparse matrix.
>>> g.adj_external(scipy_fmt='coo', etype='develops')
<2x3 sparse matrix of type '<class 'numpy.int64'>'
with 2 stored elements in COOrdinate format>
"""
etid = self.get_etype_id(etype)
if scipy_fmt is None:
return self._graph.adjacency_matrix(etid, transpose, ctx)[0]
else:
return self._graph.adjacency_matrix_scipy(
etid, transpose, scipy_fmt, False
)
def adj_tensors(self, fmt, etype=None):
"""Return the adjacency matrix of edges of the given edge type as tensors of
a sparse matrix representation.
By default, a row of returned adjacency matrix represents the
source of an edge and the column represents the destination.
Parameters
----------
fmt : str
Either ``coo``, ``csr`` or ``csc``.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
tuple[Tensor]
If :attr:`fmt` is ``coo``, returns a pair of source and destination node ID
tensors.
If :attr:`fmt` is ``csr`` or ``csc``, return the CSR or CSC representation
of the adjacency matrix as a triplet of tensors
``(indptr, indices, edge_ids)``. Namely ``edge_ids`` could be an empty
tensor with 0 elements, in which case the edge IDs are consecutive
integers starting from 0.
Examples
--------
>>> g = dgl.graph(([0, 1, 2], [1, 2, 3]))
>>> g.adj_tensors('coo')
(tensor([0, 1, 2]), tensor([1, 2, 3]))
>>> g.adj_tensors('csr')
(tensor([0, 1, 2, 3, 3]), tensor([1, 2, 3]), tensor([0, 1, 2]))
"""
etid = self.get_etype_id(etype)
if fmt == "csc":
# The first two elements are number of rows and columns
return self._graph.adjacency_matrix_tensors(etid, True, "csr")[2:]
else:
return self._graph.adjacency_matrix_tensors(etid, False, fmt)[2:]
def inc(self, typestr, ctx=F.cpu(), etype=None):
"""Return the incidence matrix representation of edges with the given
edge type.
An incidence matrix is an n-by-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 incidence matrices :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`` (only if source and destination node type are the same):
- :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
----------
typestr : str
Can be either ``in``, ``out`` or ``both``
ctx : context, optional
The context of returned incidence matrix. (Default: cpu)
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Framework SparseTensor
The incidence matrix.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> g = dgl.graph(([0, 1], [0, 2]))
>>> g.inc('in')
tensor(indices=tensor([[0, 2],
[0, 1]]),
values=tensor([1., 1.]),
size=(3, 2), nnz=2, layout=torch.sparse_coo)
>>> g.inc('out')
tensor(indices=tensor([[0, 1],
[0, 1]]),
values=tensor([1., 1.]),
size=(3, 2), nnz=2, layout=torch.sparse_coo)
>>> g.inc('both')
tensor(indices=tensor([[1, 2],
[1, 1]]),
values=tensor([-1., 1.]),
size=(3, 2), nnz=2, layout=torch.sparse_coo)
"""
etid = self.get_etype_id(etype)
return self._graph.incidence_matrix(etid, typestr, ctx)[0]
incidence_matrix = inc
#################################################################
# Features
#################################################################
def node_attr_schemes(self, ntype=None):
"""Return the node feature schemes for the specified type.
The scheme of a feature describes the shape and data type of it.
Parameters
----------
ntype : str, optional
The node type name. Can be omitted if there is only one type of nodes
in the graph.
Returns
-------
dict[str, Scheme]
A dictionary mapping a feature name to its associated feature scheme.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.ndata['h1'] = torch.randn(3, 1)
>>> g.ndata['h2'] = torch.randn(3, 2)
>>> g.node_attr_schemes()
{'h1': Scheme(shape=(1,), dtype=torch.float32),
'h2': Scheme(shape=(2,), dtype=torch.float32)}
Query for a heterogeneous graph of multiple node types.
>>> g = dgl.heterograph({('user', 'plays', 'game'):
... (torch.tensor([1, 2]), torch.tensor([3, 4]))})
>>> g.nodes['user'].data['h1'] = torch.randn(3, 1)
>>> g.nodes['user'].data['h2'] = torch.randn(3, 2)
>>> g.node_attr_schemes('user')
{'h1': Scheme(shape=(1,), dtype=torch.float32),
'h2': Scheme(shape=(2,), dtype=torch.float32)}
See Also
--------
edge_attr_schemes
"""
return self._node_frames[self.get_ntype_id(ntype)].schemes
def edge_attr_schemes(self, etype=None):
"""Return the edge feature schemes for the specified type.
The scheme of a feature describes the shape and data type of it.
Parameters
----------
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
dict[str, Scheme]
A dictionary mapping a feature name to its associated feature scheme.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Query for a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> g.edata['h1'] = torch.randn(2, 1)
>>> g.edata['h2'] = torch.randn(2, 2)
>>> g.edge_attr_schemes()
{'h1': Scheme(shape=(1,), dtype=torch.float32),
'h2': Scheme(shape=(2,), dtype=torch.float32)}
Query for a heterogeneous graph of multiple edge types.
>>> g = dgl.heterograph({('user', 'plays', 'game'):
... (torch.tensor([1, 2]), torch.tensor([3, 4])),
... ('user', 'follows', 'user'):
... (torch.tensor([3, 4]), torch.tensor([5, 6]))})
>>> g.edges['plays'].data['h1'] = torch.randn(2, 1)
>>> g.edges['plays'].data['h2'] = torch.randn(2, 2)
>>> g.edge_attr_schemes('plays')
{'h1': Scheme(shape=(1,), dtype=torch.float32),
'h2': Scheme(shape=(2,), dtype=torch.float32)}
See Also
--------
node_attr_schemes
"""
return self._edge_frames[self.get_etype_id(etype)].schemes
def set_n_initializer(self, initializer, field=None, ntype=None):
"""Set the initializer for node features.
When only part of the nodes have a feature (e.g. new nodes are added,
features are set for a subset of nodes), the initializer initializes
features for the rest nodes.
Parameters
----------
initializer : callable
A function of signature ``func(shape, dtype, ctx, id_range) -> Tensor``.
The tensor will be the initialized features. The arguments are:
- ``shape``: The shape of the tensor to return, which is a tuple of int.
The first dimension is the number of nodes for feature initialization.
- ``dtype``: The data type of the tensor to return, which is a
framework-specific data type object.
- ``ctx``: The device of the tensor to return, which is a framework-specific
device object.
- ``id_range``: The start and end ID of the nodes for feature initialization,
which is a slice.
field : str, optional
The name of the feature that the initializer applies. If not given, the
initializer applies to all features.
ntype : str, optional
The type name of the nodes. Can be omitted if the graph has only one type of nodes.
Notes
-----
Without setting a node feature initializer, zero tensors are generated
for nodes without a feature.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Define a function for initializer.
>>> def init_feats(shape, dtype, device, id_range):
... return torch.ones(shape, dtype=dtype, device=device)
An example for a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0]), torch.tensor([1])))
>>> g.ndata['h1'] = torch.zeros(2, 2)
>>> g.ndata['h2'] = torch.ones(2, 1)
>>> # Apply the initializer to feature 'h2' only.
>>> g.set_n_initializer(init_feats, field='h2')
>>> g.add_nodes(1)
>>> print(g.ndata['h1'])
tensor([[0., 0.],
[0., 0.],
[0., 0.]])
>>> print(g.ndata['h2'])
tensor([[1.], [1.], [1.]])
An example for a heterogeneous graph of multiple node types.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.nodes['user'].data['h'] = torch.zeros(3, 2)
>>> g.nodes['game'].data['w'] = torch.ones(2, 2)
>>> g.set_n_initializer(init_feats, ntype='game')
>>> g.add_nodes(1, ntype='user')
>>> # Initializer not set for 'user', use zero tensors by default
>>> g.nodes['user'].data['h']
tensor([[0., 0.],
[0., 0.],
[0., 0.],
[0., 0.]])
>>> # Initializer set for 'game'
>>> g.add_nodes(1, ntype='game')
>>> g.nodes['game'].data['w']
tensor([[1., 1.],
[1., 1.],
[1., 1.]])
"""
ntid = self.get_ntype_id(ntype)
self._node_frames[ntid].set_initializer(initializer, field)
def set_e_initializer(self, initializer, field=None, etype=None):
"""Set the initializer for edge features.
When only part of the edges have a feature (e.g. new edges are added,
features are set for a subset of edges), the initializer initializes
features for the rest edges.
Parameters
----------
initializer : callable
A function of signature ``func(shape, dtype, ctx, id_range) -> Tensor``.
The tensor will be the initialized features. The arguments are:
- ``shape``: The shape of the tensor to return, which is a tuple of int.
The first dimension is the number of edges for feature initialization.
- ``dtype``: The data type of the tensor to return, which is a
framework-specific data type object.
- ``ctx``: The device of the tensor to return, which is a framework-specific
device object.
- ``id_range``: The start and end ID of the edges for feature initialization,
which is a slice.
field : str, optional
The name of the feature that the initializer applies. If not given, the
initializer applies to all features.
etype : str or (str, str, str), optional
The type names of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Notes
-----
Without setting an edge feature initializer, zero tensors are generated
for edges without a feature.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Define a function for initializer.
>>> def init_feats(shape, dtype, device, id_range):
... return torch.ones(shape, dtype=dtype, device=device)
An example for a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0]), torch.tensor([1])))
>>> g.edata['h1'] = torch.zeros(1, 2)
>>> g.edata['h2'] = torch.ones(1, 1)
>>> # Apply the initializer to feature 'h2' only.
>>> g.set_e_initializer(init_feats, field='h2')
>>> g.add_edges(torch.tensor([1]), torch.tensor([1]))
>>> print(g.edata['h1'])
tensor([[0., 0.],
[0., 0.]])
>>> print(g.edata['h2'])
tensor([[1.], [1.]])
An example for a heterogeneous graph of multiple edge types.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 0])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.edges['plays'].data['h'] = torch.zeros(2, 2)
>>> g.edges['develops'].data['w'] = torch.ones(2, 2)
>>> g.set_e_initializer(init_feats, etype='plays')
>>> # Initializer not set for 'develops', use zero tensors by default
>>> g.add_edges(torch.tensor([1]), torch.tensor([1]), etype='develops')
>>> g.edges['develops'].data['w']
tensor([[1., 1.],
[1., 1.],
[0., 0.]])
>>> # Initializer set for 'plays'
>>> g.add_edges(torch.tensor([1]), torch.tensor([1]), etype='plays')
>>> g.edges['plays'].data['h']
tensor([[0., 0.],
[0., 0.],
[1., 1.]])
"""
etid = self.get_etype_id(etype)
self._edge_frames[etid].set_initializer(initializer, field)
def _set_n_repr(self, ntid, u, data):
"""Internal API to set node features.
`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 updates will be done out of place to work with autograd.
Parameters
----------
ntid : int
Node type id.
u : node, container or tensor
The node(s).
data : dict of tensor
Node representation.
"""
if is_all(u):
num_nodes = self._graph.num_nodes(ntid)
else:
u = utils.prepare_tensor(self, u, "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 F.context(val) != self.device:
raise DGLError(
'Cannot assign node feature "{}" on device {} to a graph on'
" device {}. Call DGLGraph.to() to copy the graph to the"
" same device.".format(key, F.context(val), self.device)
)
# To prevent users from doing things like:
#
# g.pin_memory_()
# g.ndata['x'] = torch.randn(...)
# sg = g.sample_neighbors(torch.LongTensor([...]).cuda())
# sg.ndata['x'] # Becomes a CPU tensor even if sg is on GPU due to lazy slicing
if (
self.is_pinned()
and F.context(val) == "cpu"
and not F.is_pinned(val)
):
raise DGLError(
"Pinned graph requires the node data to be pinned as well. "
"Please pin the node data before assignment."
)
if is_all(u):
self._node_frames[ntid].update(data)
else:
self._node_frames[ntid].update_row(u, data)
def _get_n_repr(self, ntid, u):
"""Get node(s) representation of a single node type.
The returned feature tensor batches multiple node features on the first dimension.
Parameters
----------
ntid : int
Node type id.
u : node, container or tensor
The node(s).
Returns
-------
dict
Representation dict from feature name to feature tensor.
"""
if is_all(u):
return self._node_frames[ntid]
else:
u = utils.prepare_tensor(self, u, "u")
return self._node_frames[ntid].subframe(u)
def _pop_n_repr(self, ntid, key):
"""Internal API to get and remove the specified node feature.
Parameters
----------
ntid : int
Node type id.
key : str
The attribute name.
Returns
-------
Tensor
The popped representation
"""
return self._node_frames[ntid].pop(key)
def _set_e_repr(self, etid, edges, data):
"""Internal API to set edge(s) features.
`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.
Parameters
----------
etid : int
Edge type id.
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.
data : tensor or dict of tensor
Edge representation.
"""
# parse argument
if not is_all(edges):
eid = utils.parse_edges_arg_to_eid(self, edges, etid, "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(edges):
num_edges = self._graph.num_edges(etid)
else:
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)
)
if F.context(val) != self.device:
raise DGLError(
'Cannot assign edge feature "{}" on device {} to a graph on'
" device {}. Call DGLGraph.to() to copy the graph to the"
" same device.".format(key, F.context(val), self.device)
)
# To prevent users from doing things like:
#
# g.pin_memory_()
# g.edata['x'] = torch.randn(...)
# sg = g.sample_neighbors(torch.LongTensor([...]).cuda())
# sg.edata['x'] # Becomes a CPU tensor even if sg is on GPU due to lazy slicing
if (
self.is_pinned()
and F.context(val) == "cpu"
and not F.is_pinned(val)
):
raise DGLError(
"Pinned graph requires the edge data to be pinned as well. "
"Please pin the edge data before assignment."
)
# set
if is_all(edges):
self._edge_frames[etid].update(data)
else:
self._edge_frames[etid].update_row(eid, data)
def _get_e_repr(self, etid, edges):
"""Internal API to get edge features.
Parameters
----------
etid : int
Edge type id.
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
"""
# parse argument
if is_all(edges):
return self._edge_frames[etid]
else:
eid = utils.parse_edges_arg_to_eid(self, edges, etid, "edges")
return self._edge_frames[etid].subframe(eid)
def _pop_e_repr(self, etid, key):
"""Get and remove the specified edge repr of a single edge type.
Parameters
----------
etid : int
Edge type id.
key : str
The attribute name.
Returns
-------
Tensor
The popped representation
"""
self._edge_frames[etid].pop(key)
#################################################################
# Message passing
#################################################################
def apply_nodes(self, func, v=ALL, ntype=None):
"""Update the features of the specified nodes by the provided function.
Parameters
----------
func : callable
The function to update node features. It must be
a :ref:`apiudf`.
v : node IDs
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
If not given (default), use all the nodes in the graph.
ntype : str, optional
The node type name. Can be omitted if there is
only one type of nodes in the graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Homogeneous graph**
>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['h'] = torch.ones(5, 2)
>>> g.apply_nodes(lambda nodes: {'x' : nodes.data['h'] * 2})
>>> g.ndata['x']
tensor([[2., 2.],
[2., 2.],
[2., 2.],
[2., 2.],
[2., 2.]])
**Heterogeneous graph**
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1], [1, 2])})
>>> g.nodes['user'].data['h'] = torch.ones(3, 5)
>>> g.apply_nodes(lambda nodes: {'h': nodes.data['h'] * 2}, ntype='user')
>>> g.nodes['user'].data['h']
tensor([[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.]])
See Also
--------
apply_edges
"""
ntid = self.get_ntype_id(ntype)
ntype = self.ntypes[ntid]
if is_all(v):
v_id = self.nodes(ntype)
else:
v_id = utils.prepare_tensor(self, v, "v")
ndata = core.invoke_node_udf(self, v_id, ntype, func, orig_nid=v_id)
self._set_n_repr(ntid, v, ndata)
def apply_edges(self, func, edges=ALL, etype=None):
"""Update the features of the specified edges by the provided function.
Parameters
----------
func : dgl.function.BuiltinFunction or callable
The function to generate new edge features. It must be either
a :ref:`api-built-in` or a :ref:`apiudf`.
edges : edges
The edges to update features on. The allowed input formats are:
* ``int``: A single edge ID.
* Int Tensor: Each element is an edge ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is an edge ID.
* (Tensor, Tensor): The node-tensors format where the i-th elements
of the two tensors specify an edge.
* (iterable[int], iterable[int]): Similar to the node-tensors format but
stores edge endpoints in python iterables.
Default value specifies all the edges in the graph.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Notes
-----
DGL recommends using DGL's bulit-in function for the :attr:`func` argument,
because DGL will invoke efficient kernels that avoids copying node features to
edge features in this case.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Homogeneous graph**
>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['h'] = torch.ones(5, 2)
>>> g.apply_edges(lambda edges: {'x' : edges.src['h'] + edges.dst['h']})
>>> g.edata['x']
tensor([[2., 2.],
[2., 2.],
[2., 2.],
[2., 2.]])
Use built-in function
>>> import dgl.function as fn
>>> g.apply_edges(fn.u_add_v('h', 'h', 'x'))
>>> g.edata['x']
tensor([[2., 2.],
[2., 2.],
[2., 2.],
[2., 2.]])
**Heterogeneous graph**
>>> g = dgl.heterograph({('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 2, 1])})
>>> g.edges[('user', 'plays', 'game')].data['h'] = torch.ones(4, 5)
>>> g.apply_edges(lambda edges: {'h': edges.data['h'] * 2})
>>> g.edges[('user', 'plays', 'game')].data['h']
tensor([[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2.]])
See Also
--------
apply_nodes
"""
# Graph with one relation type
if self._graph.number_of_etypes() == 1 or etype is not None:
etid = self.get_etype_id(etype)
etype = self.canonical_etypes[etid]
g = self if etype is None else self[etype]
else: # heterogeneous graph with number of relation types > 1
if not core.is_builtin(func):
raise DGLError(
"User defined functions are not yet "
"supported in apply_edges for heterogeneous graphs. "
"Please use (apply_edges(func), etype = rel) instead."
)
g = self
if is_all(edges):
eid = ALL
else:
eid = utils.parse_edges_arg_to_eid(self, edges, etid, "edges")
if core.is_builtin(func):
if not is_all(eid):
g = g.edge_subgraph(eid, relabel_nodes=False)
edata = core.invoke_gsddmm(g, func)
else:
edata = core.invoke_edge_udf(g, eid, etype, func)
if self._graph.number_of_etypes() == 1 or etype is not None:
self._set_e_repr(etid, eid, edata)
else:
edata_tensor = {}
key = list(edata.keys())[0]
out_tensor_tuples = edata[key]
for etid in range(self._graph.number_of_etypes()):
# TODO (Israt): Check the logic why some output tensor is None
if out_tensor_tuples[etid] is not None:
edata_tensor[key] = out_tensor_tuples[etid]
self._set_e_repr(etid, eid, edata_tensor)
def send_and_recv(
self, edges, message_func, reduce_func, apply_node_func=None, etype=None
):
"""Send messages along the specified edges and reduce them on
the destination nodes to update their features.
Parameters
----------
edges : edges
The edges to send and receive messages on. The allowed input formats are:
* ``int``: A single edge ID.
* Int Tensor: Each element is an edge ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is an edge ID.
* (Tensor, Tensor): The node-tensors format where the i-th elements
of the two tensors specify an edge.
* (iterable[int], iterable[int]): Similar to the node-tensors format but
stores edge endpoints in python iterables.
message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Notes
-----
DGL recommends using DGL's bulit-in function for the :attr:`message_func`
and the :attr:`reduce_func` arguments,
because DGL will invoke efficient kernels that avoids copying node features to
edge features in this case.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch
**Homogeneous graph**
>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['x'] = torch.ones(5, 2)
>>> # Specify edges using (Tensor, Tensor).
>>> g.send_and_recv(([1, 2], [2, 3]), fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
[0., 0.],
[1., 1.],
[1., 1.],
[0., 0.]])
>>> # Specify edges using IDs.
>>> g.send_and_recv([0, 2, 3], fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
[1., 1.],
[0., 0.],
[1., 1.],
[1., 1.]])
**Heterogeneous graph**
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [1, 2]),
... ('user', 'plays', 'game'): ([0, 1, 1, 2], [0, 0, 1, 1])
... })
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
>>> g.send_and_recv(g['follows'].edges(), fn.copy_u('h', 'm'),
... fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[0.],
[1.]])
**``send_and_recv`` using user-defined functions**
>>> import torch as th
>>> g = dgl.graph(([0, 1], [1, 2]))
>>> g.ndata['x'] = th.tensor([[1.], [2.], [3.]])
>>> # Define the function for sending node features as messages.
>>> def send_source(edges):
... return {'m': edges.src['x']}
>>> # Sum the messages received and use this to replace the original node feature.
>>> def simple_reduce(nodes):
... return {'x': nodes.mailbox['m'].sum(1)}
Send and receive messages.
>>> g.send_and_recv(g.edges())
>>> g.ndata['x']
tensor([[1.],
[1.],
[2.]])
Note that the feature of node 0 remains the same as it has no incoming edges.
"""
# edge type
etid = self.get_etype_id(etype)
_, dtid = self._graph.metagraph.find_edge(etid)
etype = self.canonical_etypes[etid]
# edge IDs
eid = utils.parse_edges_arg_to_eid(self, edges, etid, "edges")
if len(eid) == 0:
# no computation
return
u, v = self.find_edges(eid, etype=etype)
# call message passing onsubgraph
g = self if etype is None else self[etype]
compute_graph, _, dstnodes, _ = _create_compute_graph(g, u, v, eid)
ndata = core.message_passing(
compute_graph, message_func, reduce_func, apply_node_func
)
self._set_n_repr(dtid, dstnodes, ndata)
def pull(
self, v, message_func, reduce_func, apply_node_func=None, etype=None
):
"""Pull messages from the specified node(s)' predecessors along the
specified edge type, aggregate them to update the node features.
Parameters
----------
v : node IDs
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Notes
-----
* If some of the given nodes :attr:`v` has no in-edges, DGL does not invoke
message and reduce functions for these nodes and fill their aggregated messages
with zero. Users can control the filled values via :meth:`set_n_initializer`.
DGL still invokes :attr:`apply_node_func` if provided.
* DGL recommends using DGL's bulit-in function for the :attr:`message_func`
and the :attr:`reduce_func` arguments,
because DGL will invoke efficient kernels that avoids copying node features to
edge features in this case.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch
**Homogeneous graph**
>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['x'] = torch.ones(5, 2)
>>> g.pull([0, 3, 4], fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
[0., 0.],
[0., 0.],
[1., 1.],
[1., 1.]])
**Heterogeneous graph**
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [1, 2]),
... ('user', 'plays', 'game'): ([0, 2], [0, 1])
... })
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
Pull.
>>> g['follows'].pull(2, fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[1.],
[1.]])
"""
v = utils.prepare_tensor(self, v, "v")
if len(v) == 0:
# no computation
return
etid = self.get_etype_id(etype)
_, dtid = self._graph.metagraph.find_edge(etid)
etype = self.canonical_etypes[etid]
g = self if etype is None else self[etype]
# call message passing on subgraph
src, dst, eid = g.in_edges(v, form="all")
compute_graph, _, dstnodes, _ = _create_compute_graph(
g, src, dst, eid, v
)
ndata = core.message_passing(
compute_graph, message_func, reduce_func, apply_node_func
)
self._set_n_repr(dtid, dstnodes, ndata)
def push(
self, u, message_func, reduce_func, apply_node_func=None, etype=None
):
"""Send message from the specified node(s) to their successors
along the specified edge type and update their node features.
Parameters
----------
v : node IDs
The node IDs. The allowed formats are:
* ``int``: A single node.
* Int Tensor: Each element is a node ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is a node ID.
message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Notes
-----
DGL recommends using DGL's bulit-in function for the :attr:`message_func`
and the :attr:`reduce_func` arguments,
because DGL will invoke efficient kernels that avoids copying node features to
edge features in this case.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch
**Homogeneous graph**
>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['x'] = torch.ones(5, 2)
>>> g.push([0, 1], fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
[1., 1.],
[1., 1.],
[0., 0.],
[0., 0.]])
**Heterogeneous graph**
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 0], [1, 2])})
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
Push.
>>> g['follows'].push(0, fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[0.],
[0.]])
"""
edges = self.out_edges(u, form="eid", etype=etype)
self.send_and_recv(
edges, message_func, reduce_func, apply_node_func, etype=etype
)
def update_all(
self, message_func, reduce_func, apply_node_func=None, etype=None
):
"""Send messages along all the edges of the specified type
and update all the nodes of the corresponding destination type.
For heterogeneous graphs with number of relation types > 1, send messages
along all the edges, reduce them by type-wisely and across different types
at the same time. Then, update the node features of all the nodes.
Parameters
----------
message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Notes
-----
* If some of the nodes in the graph has no in-edges, DGL does not invoke
message and reduce functions for these nodes and fill their aggregated messages
with zero. Users can control the filled values via :meth:`set_n_initializer`.
DGL still invokes :attr:`apply_node_func` if provided.
* DGL recommends using DGL's bulit-in function for the :attr:`message_func`
and the :attr:`reduce_func` arguments,
because DGL will invoke efficient kernels that avoids copying node features to
edge features in this case.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch
**Homogeneous graph**
>>> g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]))
>>> g.ndata['x'] = torch.ones(5, 2)
>>> g.update_all(fn.copy_u('x', 'm'), fn.sum('m', 'h'))
>>> g.ndata['h']
tensor([[0., 0.],
[1., 1.],
[1., 1.],
[1., 1.],
[1., 1.]])
**Heterogeneous graph**
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2], [1, 2, 2])})
Update all.
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [2.]])
>>> g['follows'].update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[0.],
[0.],
[3.]])
**Heterogenenous graph (number relation types > 1)**
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [1, 1]),
... ('game', 'attracts', 'user'): ([0], [1])
... })
Update all.
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.]])
>>> g.nodes['game'].data['h'] = torch.tensor([[1.]])
>>> g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
>>> g.nodes['user'].data['h']
tensor([[0.],
[4.]])
"""
# Graph with one relation type
if self._graph.number_of_etypes() == 1 or etype is not None:
etid = self.get_etype_id(etype)
etype = self.canonical_etypes[etid]
_, dtid = self._graph.metagraph.find_edge(etid)
g = self if etype is None else self[etype]
ndata = core.message_passing(
g, message_func, reduce_func, apply_node_func
)
if (
core.is_builtin(reduce_func)
and reduce_func.name in ["min", "max"]
and ndata
):
# Replace infinity with zero for isolated nodes
key = list(ndata.keys())[0]
ndata[key] = F.replace_inf_with_zero(ndata[key])
self._set_n_repr(dtid, ALL, ndata)
else: # heterogeneous graph with number of relation types > 1
if not core.is_builtin(message_func) or not core.is_builtin(
reduce_func
):
raise DGLError(
"User defined functions are not yet "
"supported in update_all for heterogeneous graphs. "
"Please use multi_update_all instead."
)
if reduce_func.name in ["mean"]:
raise NotImplementedError(
"Cannot set both intra-type and inter-type reduce "
"operators as 'mean' using update_all. Please use "
"multi_update_all instead."
)
g = self
all_out = core.message_passing(
g, message_func, reduce_func, apply_node_func
)
key = list(all_out.keys())[0]
out_tensor_tuples = all_out[key]
dst_tensor = {}
for _, _, dsttype in g.canonical_etypes:
dtid = g.get_ntype_id(dsttype)
dst_tensor[key] = out_tensor_tuples[dtid]
if core.is_builtin(reduce_func) and reduce_func.name in [
"min",
"max",
]:
dst_tensor[key] = F.replace_inf_with_zero(dst_tensor[key])
self._node_frames[dtid].update(dst_tensor)
#################################################################
# Message passing on heterograph
#################################################################
def multi_update_all(self, etype_dict, cross_reducer, apply_node_func=None):
r"""Send messages along all the edges, reduce them by first type-wisely
then across different types, and then update the node features of all
the nodes.
Parameters
----------
etype_dict : dict
Arguments for edge-type-wise message passing. The keys are edge types
while the values are message passing arguments.
The allowed key formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
The value must be a tuple ``(message_func, reduce_func, [apply_node_func])``, where
* message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
* reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
* apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
cross_reducer : str or callable function
Cross type reducer. One of ``"sum"``, ``"min"``, ``"max"``, ``"mean"``, ``"stack"``
or a callable function. If a callable function is provided, the input argument must be
a single list of tensors containing aggregation results from each edge type, and the
output of function must be a single tensor.
apply_node_func : callable, optional
An optional apply function after the messages are reduced both
type-wisely and across different types.
It must be a :ref:`apiudf`.
Notes
-----
DGL recommends using DGL's bulit-in function for the message_func
and the reduce_func in the type-wise message passing arguments,
because DGL will invoke efficient kernels that avoids copying node features to
edge features in this case.
Examples
--------
>>> import dgl
>>> import dgl.function as fn
>>> import torch
Instantiate a heterograph.
>>> g = dgl.heterograph({
... ('user', 'follows', 'user'): ([0, 1], [1, 1]),
... ('game', 'attracts', 'user'): ([0], [1])
... })
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.]])
>>> g.nodes['game'].data['h'] = torch.tensor([[1.]])
Update all.
>>> g.multi_update_all(
... {'follows': (fn.copy_u('h', 'm'), fn.sum('m', 'h')),
... 'attracts': (fn.copy_u('h', 'm'), fn.sum('m', 'h'))},
... "sum")
>>> g.nodes['user'].data['h']
tensor([[0.],
[4.]])
User-defined cross reducer equivalent to "sum".
>>> def cross_sum(flist):
... return torch.sum(torch.stack(flist, dim=0), dim=0) if len(flist) > 1 else flist[0]
Use the user-defined cross reducer.
>>> g.multi_update_all(
... {'follows': (fn.copy_u('h', 'm'), fn.sum('m', 'h')),
... 'attracts': (fn.copy_u('h', 'm'), fn.sum('m', 'h'))},
... cross_sum)
"""
all_out = defaultdict(list)
merge_order = defaultdict(list)
for etype, args in etype_dict.items():
etid = self.get_etype_id(etype)
_, dtid = self._graph.metagraph.find_edge(etid)
args = pad_tuple(args, 3)
if args is None:
raise DGLError(
'Invalid arguments for edge type "{}". Should be '
"(msg_func, reduce_func, [apply_node_func])".format(etype)
)
mfunc, rfunc, afunc = args
g = self if etype is None else self[etype]
all_out[dtid].append(core.message_passing(g, mfunc, rfunc, afunc))
merge_order[dtid].append(
etid
) # use edge type id as merge order hint
for dtid, frames in all_out.items():
# merge by cross_reducer
out = reduce_dict_data(frames, cross_reducer, merge_order[dtid])
# Replace infinity with zero for isolated nodes when reducer is min/max
if core.is_builtin(rfunc) and rfunc.name in ["min", "max"]:
for key in out.keys():
out[key] = (
F.replace_inf_with_zero(out[key])
if out[key] is not None
else None
)
self._node_frames[dtid].update(out)
# apply
if apply_node_func is not None:
self.apply_nodes(apply_node_func, ALL, self.ntypes[dtid])
#################################################################
# Message propagation
#################################################################
def prop_nodes(
self,
nodes_generator,
message_func,
reduce_func,
apply_node_func=None,
etype=None,
):
"""Propagate messages using graph traversal by sequentially triggering
:func:`pull()` on nodes.
The traversal order is specified by the ``nodes_generator``. It generates
node frontiers, which is a list or a tensor of nodes. The nodes in the
same frontier will be triggered together, while nodes in different frontiers
will be triggered according to the generating order.
Parameters
----------
nodes_generator : iterable[node IDs]
The generator of node frontiers. Each frontier is a set of node IDs
stored in Tensor or python iterables.
It specifies which nodes perform :func:`pull` at each step.
message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Examples
--------
>>> import torch
>>> import dgl
>>> import dgl.function as fn
Instantiate a heterogrph and perform multiple rounds of message passing.
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2, 3], [2, 3, 4, 4])})
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.], [3.], [4.], [5.]])
>>> g['follows'].prop_nodes([[2, 3], [4]], fn.copy_u('h', 'm'),
... fn.sum('m', 'h'), etype='follows')
tensor([[1.],
[2.],
[1.],
[2.],
[3.]])
See Also
--------
prop_edges
"""
for node_frontier in nodes_generator:
self.pull(
node_frontier,
message_func,
reduce_func,
apply_node_func,
etype=etype,
)
def prop_edges(
self,
edges_generator,
message_func,
reduce_func,
apply_node_func=None,
etype=None,
):
"""Propagate messages using graph traversal by sequentially triggering
:func:`send_and_recv()` on edges.
The traversal order is specified by the ``edges_generator``. It generates
edge frontiers. The edge frontiers should be of *valid edges type*.
See :func:`send` for more details.
Edges in the same frontier will be triggered together, and edges in
different frontiers will be triggered according to the generating order.
Parameters
----------
edges_generator : generator
The generator of edge frontiers.
message_func : dgl.function.BuiltinFunction or callable
The message function to generate messages along the edges.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
reduce_func : dgl.function.BuiltinFunction or callable
The reduce function to aggregate the messages.
It must be either a :ref:`api-built-in` or a :ref:`apiudf`.
apply_node_func : callable, optional
An optional apply function to further update the node features
after the message reduction. It must be a :ref:`apiudf`.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Examples
--------
>>> import torch
>>> import dgl
>>> import dgl.function as fn
Instantiate a heterogrph and perform multiple rounds of message passing.
>>> g = dgl.heterograph({('user', 'follows', 'user'): ([0, 1, 2, 3], [2, 3, 4, 4])})
>>> g.nodes['user'].data['h'] = torch.tensor([[1.], [2.], [3.], [4.], [5.]])
>>> g['follows'].prop_edges([[0, 1], [2, 3]], fn.copy_u('h', 'm'),
... fn.sum('m', 'h'), etype='follows')
>>> g.nodes['user'].data['h']
tensor([[1.],
[2.],
[1.],
[2.],
[3.]])
See Also
--------
prop_nodes
"""
for edge_frontier in edges_generator:
self.send_and_recv(
edge_frontier,
message_func,
reduce_func,
apply_node_func,
etype=etype,
)
#################################################################
# Misc
#################################################################
def filter_nodes(self, predicate, nodes=ALL, ntype=None):
"""Return the IDs of the nodes with the given node type that satisfy
the given predicate.
Parameters
----------
predicate : callable
A function of signature ``func(nodes) -> Tensor``.
``nodes`` are :class:`dgl.NodeBatch` objects.
Its output tensor should be a 1D boolean tensor with
each element indicating whether the corresponding node in
the batch satisfies the predicate.
nodes : node ID(s), optional
The node(s) for query. The allowed formats are:
- Tensor: A 1D tensor that contains the node(s) for query, whose data type
and device should be the same as the :py:attr:`idtype` and device of the graph.
- iterable[int] : Similar to the tensor, but stores node IDs in a sequence
(e.g. list, tuple, numpy.ndarray).
By default, it considers all nodes.
ntype : str, optional
The node type for query. If the graph has multiple node types, one must
specify the argument. Otherwise, it can be omitted.
Returns
-------
Tensor
A 1D tensor that contains the ID(s) of the node(s) that satisfy the predicate.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Define a predicate function.
>>> def nodes_with_feature_one(nodes):
... # Whether a node has feature 1
... return (nodes.data['h'] == 1.).squeeze(1)
Filter nodes for a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g.ndata['h'] = torch.tensor([[0.], [1.], [1.], [0.]])
>>> print(g.filter_nodes(nodes_with_feature_one))
tensor([1, 2])
Filter on nodes with IDs 0 and 1
>>> print(g.filter_nodes(nodes_with_feature_one, nodes=torch.tensor([0, 1])))
tensor([1])
Filter nodes for a heterogeneous graph.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1]))})
>>> g.nodes['user'].data['h'] = torch.tensor([[0.], [1.], [1.]])
>>> g.nodes['game'].data['h'] = torch.tensor([[0.], [1.]])
>>> # Filter for 'user' nodes
>>> print(g.filter_nodes(nodes_with_feature_one, ntype='user'))
tensor([1, 2])
"""
if is_all(nodes):
nodes = self.nodes(ntype)
v = utils.prepare_tensor(self, nodes, "nodes")
if F.as_scalar(F.sum(self.has_nodes(v, ntype=ntype), dim=0)) != len(v):
raise DGLError("v contains invalid node IDs")
with self.local_scope():
self.apply_nodes(
lambda nbatch: {"_mask": predicate(nbatch)}, nodes, ntype
)
ntype = self.ntypes[0] if ntype is None else ntype
mask = self.nodes[ntype].data["_mask"]
if is_all(nodes):
return F.nonzero_1d(mask)
else:
return F.boolean_mask(v, F.gather_row(mask, v))
def filter_edges(self, predicate, edges=ALL, etype=None):
"""Return the IDs of the edges with the given edge type that satisfy
the given predicate.
Parameters
----------
predicate : callable
A function of signature ``func(edges) -> Tensor``.
``edges`` are :class:`dgl.EdgeBatch` objects.
Its output tensor should be a 1D boolean tensor with
each element indicating whether the corresponding edge in
the batch satisfies the predicate.
edges : edges
The edges to send and receive messages on. The allowed input formats are:
* ``int``: A single edge ID.
* Int Tensor: Each element is an edge ID. The tensor must have the same device type
and ID data type as the graph's.
* iterable[int]: Each element is an edge ID.
* (Tensor, Tensor): The node-tensors format where the i-th elements
of the two tensors specify an edge.
* (iterable[int], iterable[int]): Similar to the node-tensors format but
stores edge endpoints in python iterables.
By default, it considers all the edges.
etype : str or (str, str, str), optional
The type name of the edges. The allowed type name formats are:
* ``(str, str, str)`` for source node type, edge type and destination node type.
* or one ``str`` edge type name if the name can uniquely identify a
triplet format in the graph.
Can be omitted if the graph has only one type of edges.
Returns
-------
Tensor
A 1D tensor that contains the ID(s) of the edge(s) that satisfy the predicate.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Define a predicate function.
>>> def edges_with_feature_one(edges):
... # Whether an edge has feature 1
... return (edges.data['h'] == 1.).squeeze(1)
Filter edges for a homogeneous graph.
>>> g = dgl.graph((torch.tensor([0, 1, 2]), torch.tensor([1, 2, 3])))
>>> g.edata['h'] = torch.tensor([[0.], [1.], [1.]])
>>> print(g.filter_edges(edges_with_feature_one))
tensor([1, 2])
Filter on edges with IDs 0 and 1
>>> print(g.filter_edges(edges_with_feature_one, edges=torch.tensor([0, 1])))
tensor([1])
Filter edges for a heterogeneous graph.
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('user', 'follows', 'user'): (torch.tensor([0, 1]), torch.tensor([1, 2]))})
>>> g.edges['plays'].data['h'] = torch.tensor([[0.], [1.], [1.], [0.]])
>>> # Filter for 'plays' nodes
>>> print(g.filter_edges(edges_with_feature_one, etype='plays'))
tensor([1, 2])
"""
if is_all(edges):
pass
elif isinstance(edges, tuple):
u, v = edges
srctype, _, dsttype = self.to_canonical_etype(etype)
u = utils.prepare_tensor(self, u, "u")
if F.as_scalar(
F.sum(self.has_nodes(u, ntype=srctype), dim=0)
) != len(u):
raise DGLError("edges[0] contains invalid node IDs")
v = utils.prepare_tensor(self, v, "v")
if F.as_scalar(
F.sum(self.has_nodes(v, ntype=dsttype), dim=0)
) != len(v):
raise DGLError("edges[1] contains invalid node IDs")
elif isinstance(edges, Iterable) or F.is_tensor(edges):
edges = utils.prepare_tensor(self, edges, "edges")
min_eid = F.as_scalar(F.min(edges, 0))
if len(edges) > 0 > min_eid:
raise DGLError("Invalid edge ID {:d}".format(min_eid))
max_eid = F.as_scalar(F.max(edges, 0))
if len(edges) > 0 and max_eid >= self.num_edges(etype):
raise DGLError("Invalid edge ID {:d}".format(max_eid))
else:
raise ValueError("Unsupported type of edges:", type(edges))
with self.local_scope():
self.apply_edges(
lambda ebatch: {"_mask": predicate(ebatch)}, edges, etype
)
etype = self.canonical_etypes[0] if etype is None else etype
mask = self.edges[etype].data["_mask"]
if is_all(edges):
return F.nonzero_1d(mask)
else:
if isinstance(edges, tuple):
e = self.edge_ids(edges[0], edges[1], etype=etype)
else:
e = utils.prepare_tensor(self, edges, "edges")
return F.boolean_mask(e, F.gather_row(mask, e))
@property
def device(self):
"""Get the device of the graph.
Returns
-------
device context
The device of the graph, which should be a framework-specific device object
(e.g., ``torch.device``).
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a homogeneous graph for demonstration.
>>> g = dgl.graph((torch.tensor([0, 1]), torch.tensor([1, 2])))
>>> print(g.device)
device(type='cpu')
The case of heterogeneous graphs is the same.
"""
return F.to_backend_ctx(self._graph.ctx)
def to(self, device, **kwargs): # pylint: disable=invalid-name
"""Move ndata, edata and graph structure to the targeted device (cpu/gpu).
If the graph is already on the specified device, the function directly returns it.
Otherwise, it returns a cloned graph on the specified device.
Note that data of node and edge features are not moved to the specified
device before being accessed or `materialize_data()` is called.
Parameters
----------
device : Framework-specific device context object
The context to move data to (e.g., ``torch.device``).
kwargs : Key-word arguments.
Key-word arguments fed to the framework copy function.
Returns
-------
DGLGraph
The graph on the specified device.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.graph((torch.tensor([1, 0]), torch.tensor([1, 2])))
>>> g.ndata['h'] = torch.ones(3, 1)
>>> g.edata['h'] = torch.zeros(2, 2)
>>> g1 = g.to(torch.device('cuda:0'))
>>> print(g1.device)
device(type='cuda', index=0)
>>> print(g1.ndata['h'].device)
device(type='cuda', index=0)
>>> print(g1.nodes().device)
device(type='cuda', index=0)
The original graph is still on CPU.
>>> print(g.device)
device(type='cpu')
>>> print(g.ndata['h'].device)
device(type='cpu')
>>> print(g.nodes().device)
device(type='cpu')
The case of heterogeneous graphs is the same.
"""
if device is None or self.device == device:
return self
ret = copy.copy(self)
# 1. Copy graph structure
ret._graph = self._graph.copy_to(utils.to_dgl_context(device))
# 2. Copy features
# TODO(minjie): handle initializer
new_nframes = []
for nframe in self._node_frames:
new_nframes.append(nframe.to(device, **kwargs))
ret._node_frames = new_nframes
new_eframes = []
for eframe in self._edge_frames:
new_eframes.append(eframe.to(device, **kwargs))
ret._edge_frames = new_eframes
# 2. Copy misc info
if self._batch_num_nodes is not None:
new_bnn = {
k: F.copy_to(num, device, **kwargs)
for k, num in self._batch_num_nodes.items()
}
ret._batch_num_nodes = new_bnn
if self._batch_num_edges is not None:
new_bne = {
k: F.copy_to(num, device, **kwargs)
for k, num in self._batch_num_edges.items()
}
ret._batch_num_edges = new_bne
return ret
def cpu(self):
"""Return a new copy of this graph on CPU.
Returns
-------
DGLGraph
Graph on CPU.
See Also
--------
to
"""
return self.to(F.cpu())
def materialize_data(self):
"""Materialize the graph data on the current device.
This method is a no-op if the graph data is already materialized.
Returns
-------
DGLGraph
The graph on the current device.
"""
for frame in itertools.chain(self._node_frames, self._edge_frames):
for col in frame._columns.values():
col.data # pylint: disable=pointless-statement
return self
def pin_memory_(self):
"""Pin the graph structure and node/edge data to the page-locked memory for
GPU zero-copy access.
This is an **inplace** method. The graph structure must be on CPU to be pinned.
If the graph struture is already pinned, the function directly returns it.
Materialization of new sparse formats for pinned graphs is not allowed.
To avoid implicit formats materialization during training,
you should create all the needed formats before pinning.
But cloning and materialization is fine. See the examples below.
Returns
-------
DGLGraph
The pinned graph.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
>>> g = dgl.graph((torch.tensor([1, 0]), torch.tensor([1, 2])))
>>> g.pin_memory_()
Materialization of new sparse formats is not allowed for pinned graphs.
>>> g.create_formats_() # This would raise an error! You should do this before pinning.
Cloning and materializing new formats is allowed. The returned graph is **not** pinned.
>>> g1 = g.formats(['csc'])
>>> assert not g1.is_pinned()
The pinned graph can be access from both CPU and GPU. The concrete device depends
on the context of ``query``. For example, ``eid`` in ``find_edges()`` is a query.
When ``eid`` is on CPU, ``find_edges()`` is executed on CPU, and the returned
values are CPU tensors
>>> g.unpin_memory_()
>>> g.create_formats_()
>>> g.pin_memory_()
>>> eid = torch.tensor([1])
>>> g.find_edges(eids)
(tensor([0]), tensor([2]))
Moving ``eid`` to GPU, ``find_edges()`` will be executed on GPU, and the returned
values are GPU tensors.
>>> eid = eid.to('cuda:0')
>>> g.find_edges(eids)
(tensor([0], device='cuda:0'), tensor([2], device='cuda:0'))
If you don't provide a ``query``, methods will be executed on CPU by default.
>>> g.in_degrees()
tensor([0, 1, 1])
"""
if not self._graph.is_pinned():
if F.device_type(self.device) != "cpu":
raise DGLError(
"The graph structure must be on CPU to be pinned."
)
self._graph.pin_memory_()
for frame in itertools.chain(self._node_frames, self._edge_frames):
for col in frame._columns.values():
col.pin_memory_()
return self
def unpin_memory_(self):
"""Unpin the graph structure and node/edge data from the page-locked memory.
This is an **inplace** method. If the graph struture is not pinned,
e.g., on CPU or GPU, the function directly returns it.
Returns
-------
DGLGraph
The unpinned graph.
"""
if self._graph.is_pinned():
self._graph.unpin_memory_()
for frame in itertools.chain(self._node_frames, self._edge_frames):
for col in frame._columns.values():
col.unpin_memory_()
return self
def is_pinned(self):
"""Check if the graph structure is pinned to the page-locked memory.
Returns
-------
bool
True if the graph structure is pinned.
"""
return self._graph.is_pinned()
def record_stream(self, stream):
"""Record the stream that is using this graph.
This method only supports the PyTorch backend and requires graphs on the GPU.
Parameters
----------
stream : torch.cuda.Stream
The stream that is using this graph.
Returns
-------
DGLGraph
self.
"""
if F.get_preferred_backend() != "pytorch":
raise DGLError("record_stream only support the PyTorch backend.")
if F.device_type(self.device) != "cuda":
raise DGLError("The graph must be on GPU to be recorded.")
self._graph.record_stream(stream)
for frame in itertools.chain(self._node_frames, self._edge_frames):
for col in frame._columns.values():
col.record_stream(stream)
return self
def clone(self):
"""Return a heterograph object that is a clone of current graph.
Returns
-------
DGLGraph
The graph object that is a clone of current graph.
"""
# XXX(minjie): Do a shallow copy first to clone some internal metagraph information.
# Not a beautiful solution though.
ret = copy.copy(self)
# Clone the graph structure
meta_edges = []
for s_ntype, _, d_ntype in self.canonical_etypes:
meta_edges.append(
(self.get_ntype_id(s_ntype), self.get_ntype_id(d_ntype))
)
metagraph = graph_index.from_edge_list(meta_edges, True)
# rebuild graph idx
num_nodes_per_type = [
self.num_nodes(c_ntype) for c_ntype in self.ntypes
]
relation_graphs = [
self._graph.get_relation_graph(self.get_etype_id(c_etype))
for c_etype in self.canonical_etypes
]
ret._graph = heterograph_index.create_heterograph_from_relations(
metagraph,
relation_graphs,
utils.toindex(num_nodes_per_type, "int64"),
)
# Clone the frames
ret._node_frames = [fr.clone() for fr in self._node_frames]
ret._edge_frames = [fr.clone() for fr in self._edge_frames]
# Copy the batch information
ret._batch_num_nodes = copy.copy(self._batch_num_nodes)
ret._batch_num_edges = copy.copy(self._batch_num_edges)
return ret
def local_var(self):
"""Return a graph object for usage in a local function scope.
The returned graph object shares the feature data and graph structure of this graph.
However, any out-place mutation to the feature data will not reflect to this graph,
thus making it easier to use in a function scope (e.g. forward computation of a model).
If set, the local graph object will use same initializers for node features and
edge features.
Returns
-------
DGLGraph
The graph object for a local variable.
Notes
-----
Inplace operations do reflect to the original graph. This function also has little
overhead when the number of feature tensors in this graph is small.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a function for computation on graphs.
>>> def foo(g):
... g = g.local_var()
... g.edata['h'] = torch.ones((g.num_edges(), 3))
... g.edata['h2'] = torch.ones((g.num_edges(), 3))
... return g.edata['h']
``local_var`` avoids changing the graph features when exiting the function.
>>> g = dgl.graph((torch.tensor([0, 1, 1]), torch.tensor([0, 0, 2])))
>>> g.edata['h'] = torch.zeros((g.num_edges(), 3))
>>> newh = foo(g)
>>> print(g.edata['h']) # still get tensor of all zeros
tensor([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
>>> 'h2' in g.edata # new feature set in the function scope is not found
False
In-place operations will still reflect to the original graph.
>>> def foo(g):
... g = g.local_var()
... # in-place operation
... g.edata['h'] += 1
... return g.edata['h']
>>> g = dgl.graph((torch.tensor([0, 1, 1]), torch.tensor([0, 0, 2])))
>>> g.edata['h'] = torch.zeros((g.num_edges(), 1))
>>> newh = foo(g)
>>> print(g.edata['h']) # the result changes
tensor([[1.],
[1.],
[1.]])
See Also
--------
local_scope
"""
ret = copy.copy(self)
ret._node_frames = [fr.clone() for fr in self._node_frames]
ret._edge_frames = [fr.clone() for fr in self._edge_frames]
return ret
@contextmanager
def local_scope(self):
"""Enter a local scope context for the graph.
By entering a local scope, any out-place mutation to the feature data will
not reflect to the original graph, thus making it easier to use in a function scope
(e.g. forward computation of a model).
If set, the local scope will use same initializers for node features and
edge features.
Notes
-----
Inplace operations do reflect to the original graph. This function also has little
overhead when the number of feature tensors in this graph is small.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a function for computation on graphs.
>>> def foo(g):
... with g.local_scope():
... g.edata['h'] = torch.ones((g.num_edges(), 3))
... g.edata['h2'] = torch.ones((g.num_edges(), 3))
... return g.edata['h']
``local_scope`` avoids changing the graph features when exiting the function.
>>> g = dgl.graph((torch.tensor([0, 1, 1]), torch.tensor([0, 0, 2])))
>>> g.edata['h'] = torch.zeros((g.num_edges(), 3))
>>> newh = foo(g)
>>> print(g.edata['h']) # still get tensor of all zeros
tensor([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
>>> 'h2' in g.edata # new feature set in the function scope is not found
False
In-place operations will still reflect to the original graph.
>>> def foo(g):
... with g.local_scope():
... # in-place operation
... g.edata['h'] += 1
... return g.edata['h']
>>> g = dgl.graph((torch.tensor([0, 1, 1]), torch.tensor([0, 0, 2])))
>>> g.edata['h'] = torch.zeros((g.num_edges(), 1))
>>> newh = foo(g)
>>> print(g.edata['h']) # the result changes
tensor([[1.],
[1.],
[1.]])
See Also
--------
local_var
"""
old_nframes = self._node_frames
old_eframes = self._edge_frames
self._node_frames = [fr.clone() for fr in self._node_frames]
self._edge_frames = [fr.clone() for fr in self._edge_frames]
try:
yield
finally:
self._node_frames = old_nframes
self._edge_frames = old_eframes
def formats(self, formats=None):
r"""Get a cloned graph with the specified allowed sparse format(s) or
query for the usage status of sparse formats.
The API copies both the graph structure and the features.
If the input graph has multiple edge types, they will have the same
sparse format.
When ``formats`` is not None, if the intersection between `formats` and
the current graph's created sparse format(s) is not empty, the returned
cloned graph only retains all sparse format(s) in the intersection. If
the intersection is empty, a sparse format will be selected to be
created following the order of ``'coo' -> 'csr' -> 'csc'``.
Parameters
----------
formats : str or list of str or None
* If formats is None, return the usage status of sparse formats
* Otherwise, it can be ``'coo'``/``'csr'``/``'csc'`` or a sublist of
them, specifying the sparse formats to use.
Returns
-------
dict or DGLGraph
* If formats is None, the result will be a dict recording the usage
status of sparse formats.
* Otherwise, a DGLGraph will be returned, which is a clone of the
original graph with the specified allowed sparse format(s)
``formats``.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Homographs or Heterographs with A Single Edge Type**
>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g.ndata['h'] = torch.ones(4, 1)
>>> # Check status of format usage.
>>> g.formats()
{'created': ['coo'], 'not created': ['csr', 'csc']}
>>> # Get a clone of the graph with 'csr' format.
>>> csr_g = g.formats('csr')
>>> # Only allowed formats will be displayed in the status query.
>>> csr_g.formats()
{'created': ['csr'], 'not created': []}
>>> # Features are copied as well.
>>> csr_g.ndata['h']
tensor([[1.],
[1.],
[1.],
[1.]])
**Heterographs with Multiple Edge Types**
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.formats()
{'created': ['coo'], 'not created': ['csr', 'csc']}
>>> # Get a clone of the graph with 'csr' format.
>>> csr_g = g.formats('csr')
>>> # Only allowed formats will be displayed in the status query.
>>> csr_g.formats()
{'created': ['csr'], 'not created': []}
**When formats intersects with created formats**
>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g = g.formats(['coo', 'csr'])
>>> g.create_formats_()
>>> g.formats()
{'created': ['coo', 'csr'], 'not created': []}
>>> # Get a clone of the graph allowed formats 'csr' and 'csc'.
>>> csr_csc_g = g.formats(['csr', 'csc'])
>>> # Only the intersection 'csr' will be retained.
>>> csr_csc_g.formats()
{'created': ['csr'], 'not created': ['csc']}
**When formats doesn't intersect with created formats**
>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g = g.formats('coo')
>>> g.formats()
{'created': ['coo'], 'not created': []}
>>> # Get a clone of the graph allowed formats 'csr' and 'csc'.
>>> csr_csc_g = g.formats(['csr', 'csc'])
>>> # Since the intersection is empty, 'csr' will be created as it is
>>> # first in the order of 'coo' -> 'csr' -> 'csc'.
>>> csr_csc_g.formats()
{'created': ['csr'], 'not created': ['csc']}
"""
if formats is None:
# Return the format information.
return self._graph.formats()
else:
# Convert the graph to use another allowed format.
ret = copy.copy(self)
ret._graph = self._graph.formats(formats)
return ret
def create_formats_(self):
r"""Create all sparse matrices allowed for the graph.
By default, we create sparse matrices for a graph only when necessary.
In some cases we may want to create them immediately (e.g. in a
multi-process data loader), which can be achieved via this API.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
**Homographs or Heterographs with A Single Edge Type**
>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g.format()
{'created': ['coo'], 'not created': ['csr', 'csc']}
>>> g.create_formats_()
>>> g.format()
{'created': ['coo', 'csr', 'csc'], 'not created': []}
**Heterographs with Multiple Edge Types**
>>> g = dgl.heterograph({
... ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
... torch.tensor([0, 0, 1, 1])),
... ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
... torch.tensor([0, 1]))
... })
>>> g.format()
{'created': ['coo'], 'not created': ['csr', 'csc']}
>>> g.create_formats_()
>>> g.format()
{'created': ['coo', 'csr', 'csc'], 'not created': []}
"""
return self._graph.create_formats_()
def astype(self, idtype):
"""Cast this graph to use another ID type.
Features are copied (shallow copy) to the new graph.
Parameters
----------
idtype : Data type object.
New ID type. Can only be int32 or int64.
Returns
-------
DGLGraph
Graph in the new ID type.
"""
if idtype is None:
return self
utils.check_valid_idtype(idtype)
if self.idtype == idtype:
return self
bits = 32 if idtype == F.int32 else 64
ret = copy.copy(self)
ret._graph = self._graph.asbits(bits)
return ret
# TODO: Formats should not be specified, just saving all the materialized formats
def shared_memory(self, name, formats=("coo", "csr", "csc")):
"""Return a copy of this graph in shared memory, without node data or edge data.
It moves the graph index to shared memory and returns a DGLGraph object which
has the same graph structure, node types and edge types but does not contain node data
or edge data.
Parameters
----------
name : str
The name of the shared memory.
formats : str or a list of str (optional)
Desired formats to be materialized.
Returns
-------
DGLGraph
The graph in shared memory
"""
assert len(name) > 0, "The name of shared memory cannot be empty"
assert len(formats) > 0
if isinstance(formats, str):
formats = [formats]
for fmt in formats:
assert fmt in (
"coo",
"csr",
"csc",
), "{} is not coo, csr or csc".format(fmt)
gidx = self._graph.shared_memory(
name, self.ntypes, self.etypes, formats
)
return DGLGraph(gidx, self.ntypes, self.etypes)
def long(self):
"""Cast the graph to one with idtype int64
If the graph already has idtype int64, the function directly returns it. Otherwise,
it returns a cloned graph of idtype int64 with features copied (shallow copy).
Returns
-------
DGLGraph
The graph of idtype int64.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a graph of idtype int32.
>>> # (0, 1), (0, 2), (1, 2)
>>> g = dgl.graph((torch.tensor([0, 0, 1]).int(), torch.tensor([1, 2, 2]).int()))
>>> g.ndata['feat'] = torch.ones(3, 1)
>>> g.idtype
torch.int32
Cast the graph to one of idtype int64.
>>> # A cloned graph with an idtype of int64
>>> g_long = g.long()
>>> g_long.idtype
torch.int64
>>> # The idtype of the original graph does not change.
>>> g.idtype
torch.int32
>>> g_long.edges()
(tensor([0, 0, 1]), tensor([1, 2, 2]))
>>> g_long.ndata
{'feat': tensor([[1.],
[1.],
[1.]])}
See Also
--------
int
idtype
"""
return self.astype(F.int64)
def int(self):
"""Cast the graph to one with idtype int32
If the graph already has idtype int32, the function directly returns it. Otherwise,
it returns a cloned graph of idtype int32 with features copied (shallow copy).
Returns
-------
DGLGraph
The graph of idtype int32.
Examples
--------
The following example uses PyTorch backend.
>>> import dgl
>>> import torch
Create a graph of idtype int64.
>>> # (0, 1), (0, 2), (1, 2)
>>> g = dgl.graph((torch.tensor([0, 0, 1]), torch.tensor([1, 2, 2])))
>>> g.ndata['feat'] = torch.ones(3, 1)
>>> g.idtype
torch.int64
Cast the graph to one of idtype int32.
>>> # A cloned graph with an idtype of int32
>>> g_int = g.int()
>>> g_int.idtype
torch.int32
>>> # The idtype of the original graph does not change.
>>> g.idtype
torch.int64
>>> g_int.edges()
(tensor([0, 0, 1], dtype=torch.int32), tensor([1, 2, 2], dtype=torch.int32))
>>> g_int.ndata
{'feat': tensor([[1.],
[1.],
[1.]])}
See Also
--------
long
idtype
"""
return self.astype(F.int32)
############################################################
# Internal APIs
############################################################
def make_canonical_etypes(etypes, ntypes, metagraph):
"""Internal function to convert etype name to (srctype, etype, dsttype)
Parameters
----------
etypes : list of str
Edge type list
ntypes : list of str
Node type list
metagraph : GraphIndex
Meta graph.
Returns
-------
list of tuples (srctype, etype, dsttype)
"""
# sanity check
if len(etypes) != metagraph.num_edges():
raise DGLError(
"Length of edge type list must match the number of "
"edges in the metagraph. {} vs {}".format(
len(etypes), metagraph.num_edges()
)
)
if len(ntypes) != metagraph.num_nodes():
raise DGLError(
"Length of nodes type list must match the number of "
"nodes in the metagraph. {} vs {}".format(
len(ntypes), metagraph.num_nodes()
)
)
if len(etypes) == 1 and len(ntypes) == 1:
return [(ntypes[0], etypes[0], ntypes[0])]
src, dst, eid = metagraph.edges(order="eid")
rst = [
(ntypes[sid], etypes[eid], ntypes[did])
for sid, did, eid in zip(src, dst, eid)
]
return rst
def find_src_dst_ntypes(ntypes, metagraph):
"""Internal function to split ntypes into SRC and DST categories.
If the metagraph is not a uni-bipartite graph (so that the SRC and DST categories
are not well-defined), return None.
For node types that are isolated (i.e, no relation is associated with it), they
are assigned to the SRC category.
Parameters
----------
ntypes : list of str
Node type list
metagraph : GraphIndex
Meta graph.
Returns
-------
(dict[int, str], dict[int, str]) or None
Node types belonging to SRC and DST categories. Types are stored in
a dictionary from type name to type id. Return None if the graph is
not uni-bipartite.
"""
ret = _CAPI_DGLFindSrcDstNtypes(metagraph)
if ret is None:
return None
else:
src, dst = ret
srctypes = {ntypes[tid]: tid for tid in src}
dsttypes = {ntypes[tid]: tid for tid in dst}
return srctypes, dsttypes
def pad_tuple(tup, length, pad_val=None):
"""Pad the given tuple to the given length.
If the input is not a tuple, convert it to a tuple of length one.
Return None if pad fails.
"""
if not isinstance(tup, tuple):
tup = (tup,)
if len(tup) > length:
return None
elif len(tup) == length:
return tup
else:
return tup + (pad_val,) * (length - len(tup))
def reduce_dict_data(frames, reducer, order=None):
"""Merge tensor dictionaries into one. Resolve conflict fields using reducer.
Parameters
----------
frames : list[dict[str, Tensor]]
Input tensor dictionaries
reducer : str or callable function
One of "sum", "max", "min", "mean", "stack" or a callable function.
If a callable function is provided, the input arguments must be a single list
of tensors containing aggregation results from each edge type, and the
output of function must be a single tensor.
order : list[Int], optional
Merge order hint. Useful for "stack" reducer.
If provided, each integer indicates the relative order
of the ``frames`` list. Frames are sorted according to this list
in ascending order. Tie is not handled so make sure the order values
are distinct.
Returns
-------
dict[str, Tensor]
Merged frame
"""
if len(frames) == 1 and reducer != "stack":
# Directly return the only one input. Stack reducer requires
# modifying tensor shape.
return frames[0]
if callable(reducer):
merger = reducer
elif reducer == "stack":
# Stack order does not matter. However, it must be consistent!
if order:
assert len(order) == len(frames)
sorted_with_key = sorted(zip(frames, order), key=lambda x: x[1])
frames = list(zip(*sorted_with_key))[0]
def merger(flist):
return F.stack(flist, 1)
else:
redfn = getattr(F, reducer, None)
if redfn is None:
raise DGLError(
"Invalid cross type reducer. Must be one of "
'"sum", "max", "min", "mean" or "stack".'
)
def merger(flist):
return redfn(F.stack(flist, 0), 0) if len(flist) > 1 else flist[0]
keys = set()
for frm in frames:
keys.update(frm.keys())
ret = {}
for k in keys:
flist = []
for frm in frames:
if k in frm:
flist.append(frm[k])
ret[k] = merger(flist)
return ret
def combine_frames(frames, ids, col_names=None):
"""Merge the frames into one frame, taking the common columns.
Return None if there is no common columns.
Parameters
----------
frames : List[Frame]
List of frames
ids : List[int]
List of frame IDs
col_names : List[str], optional
Column names to consider. If not given, it considers all columns.
Returns
-------
Frame
The resulting frame
"""
# find common columns and check if their schemes match
schemes = None
for frame_id in ids:
frame = frames[frame_id]
if frame.num_rows == 0:
continue
if schemes is None:
schemes = frame.schemes
if col_names is not None:
schemes = {key: frame.schemes[key] for key in col_names}
continue
for key, scheme in list(schemes.items()):
if key in frame.schemes:
if frame.schemes[key] != scheme:
raise DGLError(
"Cannot concatenate column %s with shape %s and shape %s"
% (key, frame.schemes[key], scheme)
)
else:
del schemes[key]
if len(schemes) == 0:
return None
# concatenate the columns
to_cat = lambda key: [frames[i][key] for i in ids if frames[i].num_rows > 0]
cols = {key: F.cat(to_cat(key), dim=0) for key in schemes}
return Frame(cols)
def combine_names(names, ids=None):
"""Combine the selected names into one new name.
Parameters
----------
names : list of str
String names
ids : numpy.ndarray, optional
Selected index
Returns
-------
str
"""
if ids is None:
return "+".join(sorted(names))
else:
selected = sorted([names[i] for i in ids])
return "+".join(selected)
class DGLBlock(DGLGraph):
"""Subclass that signifies the graph is a block created from
:func:`dgl.to_block`.
"""
# (BarclayII) I'm making a subclass because I don't want to make another version of
# serialization that contains the is_block flag.
is_block = True
def __repr__(self):
if (
len(self.srctypes) == 1
and len(self.dsttypes) == 1
and len(self.etypes) == 1
):
ret = "Block(num_src_nodes={srcnode}, num_dst_nodes={dstnode}, num_edges={edge})"
return ret.format(
srcnode=self.number_of_src_nodes(),
dstnode=self.number_of_dst_nodes(),
edge=self.num_edges(),
)
else:
ret = (
"Block(num_src_nodes={srcnode},\n"
" num_dst_nodes={dstnode},\n"
" num_edges={edge},\n"
" metagraph={meta})"
)
nsrcnode_dict = {
ntype: self.number_of_src_nodes(ntype)
for ntype in self.srctypes
}
ndstnode_dict = {
ntype: self.number_of_dst_nodes(ntype)
for ntype in self.dsttypes
}
nedge_dict = {
etype: self.num_edges(etype) for etype in self.canonical_etypes
}
meta = str(self.metagraph().edges(keys=True))
return ret.format(
srcnode=nsrcnode_dict,
dstnode=ndstnode_dict,
edge=nedge_dict,
meta=meta,
)
def _create_compute_graph(graph, u, v, eid, recv_nodes=None):
"""Create a computation graph from the given edges.
The compute graph is a uni-directional bipartite graph with only
one edge type. Similar to subgraph extraction, it stores the original node IDs
in the srcdata[NID] and dstdata[NID] and extracts features accordingly.
Edges are not relabeled.
This function is typically used during message passing to generate
a graph that contains only the active set of edges.
Parameters
----------
graph : DGLGraph
The input graph.
u : Tensor
Src nodes.
v : Tensor
Dst nodes.
eid : Tensor
Edge IDs.
recv_nodes : Tensor
Nodes that receive messages. If None, it is equal to unique(v).
Otherwise, it must be a superset of v and can contain nodes
that have no incoming edges.
Returns
-------
DGLGraph
A computation graph.
"""
if len(u) == 0:
# The computation graph has no edge and will not trigger message
# passing. However, because of the apply node phase, we still construct
# an empty graph to continue.
unique_src = new_u = new_v = u
assert recv_nodes is not None
unique_dst, _ = utils.relabel(recv_nodes)
else:
# relabel u and v to starting from 0
unique_src, src_map = utils.relabel(u)
if recv_nodes is None:
unique_dst, dst_map = utils.relabel(v)
else:
unique_dst, dst_map = utils.relabel(recv_nodes)
new_u = F.gather_row(src_map, u)
new_v = F.gather_row(dst_map, v)
srctype, etype, dsttype = graph.canonical_etypes[0]
# create graph
hgidx = heterograph_index.create_unitgraph_from_coo(
2, len(unique_src), len(unique_dst), new_u, new_v, ["coo", "csr", "csc"]
)
# create frame
srcframe = graph._node_frames[graph.get_ntype_id(srctype)].subframe(
unique_src
)
srcframe[NID] = unique_src
dstframe = graph._node_frames[graph.get_ntype_id(dsttype)].subframe(
unique_dst
)
dstframe[NID] = unique_dst
eframe = graph._edge_frames[0].subframe(eid)
eframe[EID] = eid
return (
DGLGraph(
hgidx,
([srctype], [dsttype]),
[etype],
node_frames=[srcframe, dstframe],
edge_frames=[eframe],
),
unique_src,
unique_dst,
eid,
)
_init_api("dgl.heterograph")
Reference in New Issue View Git Blame Copy Permalink