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Commit e2b7f745 authored by Mufei Li's avatar Mufei Li Committed by Quan (Andy) Gan
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[Hetero] Doc (#892) 

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* style fixes & undefined name fix

* transpose=False in test
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docs/source/api/python/graph.rst
View file @ e2b7f745
.. _apigraph:
DGLGraph -- Graph with node/edge features
DGLGraph -- Untyped graph with node/edge features
=========================================
.. currentmodule:: dgl
......@@ -79,7 +79,6 @@ Converting from/to other format
DGLGraph.adjacency_matrix
DGLGraph.adjacency_matrix_scipy
DGLGraph.incidence_matrix
DGLGraph.to
Using Node/edge features
------------------------
......@@ -121,3 +120,4 @@ Computing with DGLGraph
DGLGraph.prop_edges
DGLGraph.filter_nodes
DGLGraph.filter_edges
DGLGraph.to
docs/source/api/python/heterograph.rst 0 → 100644
View file @ e2b7f745
.. _apiheterograph:
DGLHeteroGraph -- Typed graph with node/edge features
=====================================================
.. currentmodule:: dgl
.. autoclass:: DGLHeteroGraph
Conversion to and from heterogeneous graphs
-----------------------------------------
.. automodule:: dgl.convert
.. currentmodule:: dgl
.. autosummary::
:toctree: ../../generated/
graph
bipartite
hetero_from_relations
to_hetero
to_homo
to_networkx
DGLHeteroGraph.adjacency_matrix
DGLHeteroGraph.incidence_matrix
Querying metagraph structure
----------------------------
.. autosummary::
:toctree: ../../generated/
DGLHeteroGraph.ntypes
DGLHeteroGraph.etypes
DGLHeteroGraph.canonical_etypes
DGLHeteroGraph.metagraph
DGLHeteroGraph.to_canonical_etype
DGLHeteroGraph.get_ntype_id
DGLHeteroGraph.get_etype_id
Querying graph structure
------------------------
.. autosummary::
:toctree: ../../generated/
DGLHeteroGraph.number_of_nodes
DGLHeteroGraph.number_of_edges
DGLHeteroGraph.is_multigraph
DGLHeteroGraph.is_readonly
DGLHeteroGraph.has_node
DGLHeteroGraph.has_nodes
DGLHeteroGraph.has_edge_between
DGLHeteroGraph.has_edges_between
DGLHeteroGraph.predecessors
DGLHeteroGraph.successors
DGLHeteroGraph.edge_id
DGLHeteroGraph.edge_ids
DGLHeteroGraph.find_edges
DGLHeteroGraph.in_edges
DGLHeteroGraph.out_edges
DGLHeteroGraph.all_edges
DGLHeteroGraph.in_degree
DGLHeteroGraph.in_degrees
DGLHeteroGraph.out_degree
DGLHeteroGraph.out_degrees
Using Node/edge features
------------------------
.. autosummary::
:toctree: ../../generated/
DGLHeteroGraph.nodes
DGLHeteroGraph.ndata
DGLHeteroGraph.edges
DGLHeteroGraph.edata
DGLHeteroGraph.node_attr_schemes
DGLHeteroGraph.edge_attr_schemes
DGLHeteroGraph.set_n_initializer
DGLHeteroGraph.set_e_initializer
DGLHeteroGraph.local_var
DGLHeteroGraph.local_scope
Transforming graph
------------------
.. autosummary::
:toctree: ../../generated/
DGLHeteroGraph.subgraph
DGLHeteroGraph.edge_subgraph
DGLHeteroGraph.node_type_subgraph
DGLHeteroGraph.edge_type_subgraph
Computing with DGLHeteroGraph
-----------------------------
.. autosummary::
:toctree: ../../generated/
DGLHeteroGraph.apply_nodes
DGLHeteroGraph.apply_edges
DGLHeteroGraph.group_apply_edges
DGLHeteroGraph.send
DGLHeteroGraph.recv
DGLHeteroGraph.multi_recv
DGLHeteroGraph.send_and_recv
DGLHeteroGraph.multi_send_and_recv
DGLHeteroGraph.pull
DGLHeteroGraph.multi_pull
DGLHeteroGraph.push
DGLHeteroGraph.update_all
DGLHeteroGraph.multi_update_all
DGLHeteroGraph.prop_nodes
DGLHeteroGraph.prop_edges
DGLHeteroGraph.filter_nodes
DGLHeteroGraph.filter_edges
DGLHeteroGraph.to
docs/source/api/python/index.rst
View file @ e2b7f745
......@@ -5,6 +5,7 @@ API Reference
:maxdepth: 2
graph
heterograph
init
batch
function
......
docs/source/api/python/subgraph.rst
View file @ e2b7f745
.. _apigraph:
.. _apisubgraph:
DGLSubGraph -- Class for subgraph data structure
================================================
......
python/dgl/convert.py
View file @ e2b7f745
......@@ -9,7 +9,7 @@ from . import heterograph_index
from .heterograph import DGLHeteroGraph, combine_frames
from . import graph_index
from . import utils
from .base import NTYPE, ETYPE, NID, EID
from .base import NTYPE, ETYPE, NID, EID, DGLError
__all__ = [
'graph',
......@@ -22,21 +22,52 @@ __all__ = [
]
def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
"""Create a graph.
The graph has only one type of nodes and edges.
"""Create a graph with one type of nodes and edges.
In the sparse matrix perspective, :func:`dgl.graph` creates a graph
whose adjacency matrix must be square while :func:`dgl.bipartite`
creates a graph that does not necessarily have square adjacency matrix.
Parameters
----------
data : graph data
Data to initialize graph structure. Supported data formats are
(1) list of edge pairs (e.g. [(0, 2), (3, 1), ...])
(2) pair of vertex IDs representing end nodes (e.g. ([0, 3, ...], [2, 1, ...]))
(3) scipy sparse matrix
(4) networkx graph
ntype : str, optional
Node type name. (Default: _N)
etype : str, optional
Edge type name. (Default: _E)
card : int, optional
Cardinality (number of nodes in the graph). If None, infer from input data, i.e.
the largest node ID plus 1. (Default: None)
kwargs : key-word arguments, optional
Other key word arguments. Only comes into effect when we are using a NetworkX
graph. It can consist of:
* edge_id_attr_name
``Str``, key name for edge ids in the NetworkX graph. If not found, we
will consider the graph not to have pre-specified edge ids.
* node_attrs
``List of str``, names for node features to retrieve from the NetworkX graph
* edge_attrs
``List of str``, names for edge features to retrieve from the NetworkX graph
Returns
-------
DGLHeteroGraph
Examples
--------
Create from edges pairs:
Create from pairs of edges with form (src, dst)
>>> g = dgl.graph([(0, 2), (0, 3), (1, 2)])
Creat from pair of vertex IDs lists
Create from source and destination vertex ID lists
>>> u = [0, 0, 1]
>>> v = [2, 3, 2]
......@@ -70,28 +101,6 @@ def graph(data, ntype='_N', etype='_E', card=None, **kwargs):
['follows']
>>> g.canonical_etypes
[('user', 'follows', 'user')]
Parameters
----------
data : graph data
Data to initialize graph structure. Supported data formats are
(1) list of edge pairs (e.g. [(0, 2), (3, 1), ...])
(2) pair of vertex IDs representing end nodes (e.g. ([0, 3, ...], [2, 1, ...]))
(3) scipy sparse matrix
(4) networkx graph
ntype : str, optional
Node type name. (Default: _N)
etype : str, optional
Edge type name. (Default: _E)
card : int, optional
Cardinality (number of nodes in the graph). If None, infer from input data.
(Default: None)
kwargs : key-word arguments, optional
Other key word arguments.
Returns
-------
DGLHeteroGraph
"""
if card is not None:
urange, vrange = card, card
......@@ -119,9 +128,40 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
whose adjacency matrix must be square while :func:`dgl.bipartite`
creates a graph that does not necessarily have square adjacency matrix.
Parameters
----------
data : graph data
Data to initialize graph structure. Supported data formats are
(1) list of edge pairs (e.g. [(0, 2), (3, 1), ...])
(2) pair of vertex IDs representing end nodes (e.g. ([0, 3, ...], [2, 1, ...]))
(3) scipy sparse matrix
(4) networkx graph
utype : str, optional
Source node type name. (Default: _U)
etype : str, optional
Edge type name. (Default: _E)
vtype : str, optional
Destination node type name. (Default: _V)
card : pair of int, optional
Cardinality (number of nodes in the source and destination group). If None,
infer from input data, i.e. the largest node ID plus 1 for each type. (Default: None)
kwargs : key-word arguments, optional
Other key word arguments. Only comes into effect when we are using a NetworkX
graph. It can consist of:
* edge_id_attr_name
``Str``, key name for edge ids in the NetworkX graph. If not found, we
will consider the graph not to have pre-specified edge ids.
Returns
-------
DGLHeteroGraph
Examples
--------
Create from edges pairs:
Create from pairs of edges
>>> g = dgl.bipartite([(0, 2), (0, 3), (1, 2)], 'user', 'plays', 'game')
>>> g.ntypes
......@@ -137,7 +177,7 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
>>> g.number_of_edges('plays') # 'plays' could be omitted here
3
Creat from pair of vertex IDs lists
Create from source and destination vertex ID lists
>>> u = [0, 0, 1]
>>> v = [2, 3, 2]
......@@ -168,37 +208,13 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
>>> import networkx as nx
>>> nxg = nx.complete_bipartite_graph(3, 4)
>>> g = dgl.graph(nxg, 'user', 'plays', 'game')
>>> g = dgl.bipartite(nxg, 'user', 'plays', 'game')
>>> g.number_of_nodes('user')
3
>>> g.number_of_nodes('game')
4
>>> g.edges()
(tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2]), tensor([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]))
Parameters
----------
data : graph data
Data to initialize graph structure. Supported data formats are
(1) list of edge pairs (e.g. [(0, 2), (3, 1), ...])
(2) pair of vertex IDs representing end nodes (e.g. ([0, 3, ...], [2, 1, ...]))
(3) scipy sparse matrix
(4) networkx graph
utype : str, optional
Source node type name. (Default: _U)
etype : str, optional
Edge type name. (Default: _E)
vtype : str, optional
Destination node type name. (Default: _V)
card : pair of int, optional
Cardinality (number of nodes in the source and destination group). If None,
infer from input data. (Default: None)
kwargs : key-word arguments, optional
Other key word arguments.
Returns
-------
DGLHeteroGraph
"""
if utype == vtype:
raise DGLError('utype should not be equal to vtype. Use ``dgl.graph`` instead.')
......@@ -221,21 +237,18 @@ def bipartite(data, utype='_U', etype='_E', vtype='_V', card=None, **kwargs):
def hetero_from_relations(rel_graphs):
"""Create a heterograph from per-relation graphs.
TODO(minjie): this API can be generalized as a union operation of
the input graphs
TODO(minjie): handle node/edge data
Parameters
----------
rel_graphs : list of DGLHeteroGraph
Graph for each relation.
Each element corresponds to a heterograph for one (src, edge, dst) relation.
Returns
-------
DGLHeteroGraph
A heterograph.
A heterograph consisting of all relations.
"""
# TODO(minjie): this API can be generalized as a union operation of the input graphs
# TODO(minjie): handle node/edge data
# infer meta graph
ntype_dict = {} # ntype -> ntid
meta_edges = []
......@@ -244,11 +257,11 @@ def hetero_from_relations(rel_graphs):
for rgrh in rel_graphs:
assert len(rgrh.etypes) == 1
stype, etype, dtype = rgrh.canonical_etypes[0]
if not stype in ntype_dict:
if stype not in ntype_dict:
ntype_dict[stype] = len(ntypes)
ntypes.append(stype)
stid = ntype_dict[stype]
if not dtype in ntype_dict:
if dtype not in ntype_dict:
ntype_dict[dtype] = len(ntypes)
ntypes.append(dtype)
dtid = ntype_dict[dtype]
......@@ -343,36 +356,32 @@ def heterograph(data_dict, num_nodes_dict=None):
return hetero_from_relations(rel_graphs)
def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE, metagraph=None):
"""Convert the given graph to a heterogeneous graph.
"""Convert the given homogeneous graph to a heterogeneous graph.
The input graph should have only one type of nodes and edges. Each node and edge
stores an integer feature (under ``ntype_field`` and ``etype_field``), representing
the type id, which which can be used to retrieve the type names stored
the type id, which can be used to retrieve the type names stored
in the given ``ntypes`` and ``etypes`` arguments.
The function will automatically distinguish edge types that have the same given
type IDs but different src and dst type IDs. For example, we allow both edges A and B
to have the same type ID 3, but one has (0, 1) and the other as (2, 3) as the
(src, dst) type IDs. In this case, the function will "split" edge type 3 into two types:
to have the same type ID 0, but one has (0, 1) and the other as (2, 3) as the
(src, dst) type IDs. In this case, the function will "split" edge type 0 into two types:
(0, ty_A, 1) and (2, ty_B, 3). In another word, these two edges share the same edge
type name, but can be distinguished by a canonical edge type tuple.
Examples
--------
TBD
Parameters
----------
G : DGLHeteroGraph
Input homogenous graph.
Input homogeneous graph.
ntypes : list of str
The node type names.
etypes : list of str
The edge type names.
ntype_field : str, optional
The feature field used to store node type. (Default: dgl.NTYPE)
The feature field used to store node type. (Default: ``dgl.NTYPE``)
etype_field : str, optional
The feature field used to store edge type. (Default: dgl.ETYPE)
The feature field used to store edge type. (Default: ``dgl.ETYPE``)
metagraph : networkx MultiDiGraph, optional
Metagraph of the returned heterograph.
If provided, DGL assumes that G can indeed be described with the given metagraph.
......@@ -382,9 +391,8 @@ def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE, metagraph
Returns
-------
DGLHeteroGraph
A heterograph.
The parent node and edge ID are stored in the column dgl.NID and dgl.EID
respectively for all node/edge types.
A heterograph. The parent node and edge ID are stored in the column
``dgl.NID`` and ``dgl.EID`` respectively for all node/edge types.
Notes
-----
......@@ -393,8 +401,47 @@ def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE, metagraph
and destination types differ.
The node IDs of a single type in the returned heterogeneous graph is ordered
the same as the nodes with the same ``ntype_field`` feature. Edge IDs of
the same as the nodes with the same ``ntype_field`` feature. Edge IDs of
a single type is similar.
Examples
--------
>>> g1 = dgl.bipartite([(0, 1), (1, 2)], 'user', 'develops', 'activity')
>>> g2 = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
>>> hetero_g = dgl.hetero_from_relations([g1, g2])
>>> print(hetero_g)
Graph(num_nodes={'user': 2, 'activity': 3, 'developer': 2, 'game': 2},
num_edges={'develops': 2},
metagraph=[('user', 'activity'), ('developer', 'game')])
We first convert the heterogeneous graph to a homogeneous graph.
>>> homo_g = dgl.to_homo(hetero_g)
>>> print(homo_g)
Graph(num_nodes=9, num_edges=4,
ndata_schemes={'_TYPE': Scheme(shape=(), dtype=torch.int64),
'_ID': Scheme(shape=(), dtype=torch.int64)}
edata_schemes={'_TYPE': Scheme(shape=(), dtype=torch.int64),
'_ID': Scheme(shape=(), dtype=torch.int64)})
>>> homo_g.ndata
{'_TYPE': tensor([0, 0, 1, 1, 1, 2, 2, 3, 3]), '_ID': tensor([0, 1, 0, 1, 2, 0, 1, 0, 1])}
Nodes 0, 1 for 'user', 2, 3, 4 for 'activity', 5, 6 for 'developer', 7, 8 for 'game'
>>> homo_g.edata
{'_TYPE': tensor([0, 0, 1, 1]), '_ID': tensor([0, 1, 0, 1])}
Edges 0, 1 for ('user', 'develops', 'activity'), 2, 3 for ('developer', 'develops', 'game')
Now convert the homogeneous graph back to a heterogeneous graph.
>>> hetero_g_2 = dgl.to_hetero(homo_g, hetero_g.ntypes, hetero_g.etypes)
>>> print(hetero_g_2)
Graph(num_nodes={'user': 2, 'activity': 3, 'developer': 2, 'game': 2},
num_edges={'develops': 2},
metagraph=[('user', 'activity'), ('developer', 'game')])
See Also
--------
dgl.to_homo
"""
# TODO(minjie): use hasattr to support DGLGraph input; should be fixed once
# DGLGraph is merged with DGLHeteroGraph
......@@ -486,29 +533,42 @@ def to_hetero(G, ntypes, etypes, ntype_field=NTYPE, etype_field=ETYPE, metagraph
return hg
def to_homo(G):
"""Convert the given graph to a homogeneous graph.
"""Convert the given heterogeneous graph to a homogeneous graph.
The returned graph has only one type of nodes and etypes.
The returned graph has only one type of nodes and edges.
Node and edge types are stored as features in the returned graph. Each feature
is an integer representing the type id, which can be used to retrieve the type
names stored in ``G.ntypes`` and ``G.etypes`` arguments.
Examples
--------
TBD
Parameters
----------
G : DGLHeteroGraph
Input heterogenous graph.
Input heterogeneous graph.
Returns
-------
DGLHeteroGraph
A homogenous graph.
The parent node and edge type/ID are stored in columns dgl.NTYPE/dgl.NID and
dgl.ETYPE/dgl.EID respectively.
A homogeneous graph. The parent node and edge type/ID are stored in
columns ``dgl.NTYPE/dgl.NID`` and ``dgl.ETYPE/dgl.EID`` respectively.
Examples
--------
>>> follows_g = dgl.graph([(0, 1), (1, 2)], 'user', 'follows')
>>> devs_g = dgl.bipartite([(0, 0), (1, 1)], 'developer', 'develops', 'game')
>>> hetero_g = dgl.hetero_from_relations([follows_g, devs_g])
>>> homo_g = dgl.to_homo(hetero_g)
>>> homo_g.ndata
{'_TYPE': tensor([0, 0, 0, 1, 1, 2, 2]), '_ID': tensor([0, 1, 2, 0, 1, 0, 1])}
First three nodes for 'user', next two for 'developer' and the last two for 'game'
>>> homo_g.edata
{'_TYPE': tensor([0, 0, 1, 1]), '_ID': tensor([0, 1, 0, 1])}
First two edges for 'follows', next two for 'develops'
See Also
--------
dgl.to_hetero
"""
num_nodes_per_ntype = [G.number_of_nodes(ntype) for ntype in G.ntypes]
offset_per_ntype = np.insert(np.cumsum(num_nodes_per_ntype), 0, 0)
......@@ -580,7 +640,7 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None):
maximum of the destination node IDs in the edge list plus 1. (Default: None)
Returns
------
-------
DGLHeteroGraph
"""
u = utils.toindex(u)
......@@ -599,14 +659,10 @@ def create_from_edges(u, v, utype, etype, vtype, urange=None, vrange=None):
return DGLHeteroGraph(hgidx, [utype, vtype], [etype])
def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None):
"""Internal function to create a graph from a list of edge tuples with types.
"""Internal function to create a heterograph from a list of edge tuples with types.
utype could be equal to vtype
Examples
--------
TBD
Parameters
----------
elist : iterable of int pairs
......@@ -637,7 +693,7 @@ def create_from_edge_list(elist, utype, etype, vtype, urange=None, vrange=None):
return create_from_edges(u, v, utype, etype, vtype, urange, vrange)
def create_from_scipy(spmat, utype, etype, vtype, with_edge_id=False):
"""Internal function to create a graph from a scipy sparse matrix with types.
"""Internal function to create a heterograph from a scipy sparse matrix with types.
Parameters
----------
......@@ -684,7 +740,26 @@ def create_from_networkx(nx_graph,
edge_id_attr_name='id',
node_attrs=None,
edge_attrs=None):
"""Create graph that has only one set of nodes and edges.
"""Create a heterograph that has only one set of nodes and edges.
Parameters
----------
nx_graph : NetworkX graph
ntype : str
Type name for both source and destination nodes
etype : str
Type name for edges
edge_id_attr_name : str, optional
Key name for edge ids in the NetworkX graph. If not found, we
will consider the graph not to have pre-specified edge ids. (Default: 'id')
node_attrs : list of str
Names for node features to retrieve from the NetworkX graph (Default: None)
edge_attrs : list of str
Names for edge features to retrieve from the NetworkX graph (Default: None)
Returns
-------
g : DGLHeteroGraph
"""
if not nx_graph.is_directed():
nx_graph = nx_graph.to_directed()
......@@ -767,13 +842,33 @@ def create_from_networkx_bipartite(nx_graph,
edge_id_attr_name='id',
node_attrs=None,
edge_attrs=None):
"""Create graph that has only one set of nodes and edges.
"""Create a heterograph that has one set of source nodes, one set of
destination nodes and one set of edges.
The input graph must follow the bipartite graph convention of networkx.
Each node has an attribute ``bipartite`` with values 0 and 1 indicating which
set it belongs to.
Parameters
----------
nx_graph : NetworkX graph
The input graph must follow the bipartite graph convention of networkx.
Each node has an attribute ``bipartite`` with values 0 and 1 indicating
which set it belongs to. Only edges from node set 0 to node set 1 are
added to the returned graph.
utype : str
Source node type name.
etype : str
Edge type name.
vtype : str
Destination node type name.
edge_id_attr_name : str, optional
Key name for edge ids in the NetworkX graph. If not found, we
will consider the graph not to have pre-specified edge ids. (Default: 'id')
node_attrs : list of str
Names for node features to retrieve from the NetworkX graph (Default: None)
edge_attrs : list of str
Names for edge features to retrieve from the NetworkX graph (Default: None)
Only edges from node set 0 to node set 1 are added to the returned graph.
Returns
-------
g : DGLHeteroGraph
"""
if not nx_graph.is_directed():
nx_graph = nx_graph.to_directed()
......@@ -810,15 +905,28 @@ def create_from_networkx_bipartite(nx_graph,
g = create_from_edges(src, dst, utype, etype, vtype, len(top_nodes), len(bottom_nodes))
# TODO attributes
assert node_attrs is None
assert edge_attrs is None
assert node_attrs is None, 'Retrieval of node attributes are not supported yet.'
assert edge_attrs is None, 'Retrieval of edge attributes are not supported yet.'
return g
def to_networkx(g, node_attrs=None, edge_attrs=None):
"""Convert to networkx graph.
See Also
--------
DGLHeteroGraph.to_networkx
The edge id will be saved as the 'id' edge attribute.
Parameters
----------
g : DGLGraph or DGLHeteroGraph
For DGLHeteroGraphs, we currently only support the
case of one node type and one edge type.
node_attrs : iterable of str, optional
The node attributes to be copied. (Default: None)
edge_attrs : iterable of str, optional
The edge attributes to be copied. (Default: None)
Returns
-------
networkx.DiGraph
The nx graph
"""
return g.to_networkx(node_attrs, edge_attrs)
python/dgl/heterograph.py
View file @ e2b7f745
This diff is collapsed.
python/dgl/heterograph_index.py
View file @ e2b7f745
......@@ -6,7 +6,7 @@ import scipy
from ._ffi.object import register_object, ObjectBase
from ._ffi.function import _init_api
from .base import DGLError
from .base import DGLError, dgl_warning
from . import backend as F
from . import utils
......
python/dgl/utils.py
View file @ e2b7f745
......@@ -280,7 +280,7 @@ class LazyDict(Mapping):
self._keys = keys
def __getitem__(self, key):
if not key in self._keys:
if key not in self._keys:
raise KeyError(key)
return self._fn(key)
......@@ -422,7 +422,7 @@ class CtxCachedObject(object):
self._ctx_dict = {}
def __call__(self, ctx):
if not ctx in self._ctx_dict:
if ctx not in self._ctx_dict:
self._ctx_dict[ctx] = self._generator(ctx)
return self._ctx_dict[ctx]
......@@ -445,7 +445,7 @@ def cached_member(cache, prefix):
def wrapper(self, *args):
dic = getattr(self, cache)
key = '%s-%s' % (prefix, '-'.join([str(a) for a in args]))
if not key in dic:
if key not in dic:
dic[key] = func(self, *args)
return dic[key]
return wrapper
......
tests/compute/test_graph.py
View file @ e2b7f745
......@@ -117,8 +117,10 @@ def test_query():
assert g.out_degree(9) == 1
assert F.allclose(g.out_degrees([8, 9]), F.tensor([0, 1]))
assert np.array_equal(F.sparse_to_numpy(g.adjacency_matrix()), scipy_coo_input().toarray().T)
assert np.array_equal(F.sparse_to_numpy(g.adjacency_matrix(transpose=True)), scipy_coo_input().toarray())
assert np.array_equal(
F.sparse_to_numpy(g.adjacency_matrix(transpose=False)), scipy_coo_input().toarray().T)
assert np.array_equal(
F.sparse_to_numpy(g.adjacency_matrix(transpose=True)), scipy_coo_input().toarray())
def _test(g):
# test twice to see whether the cached format works or not
......@@ -191,8 +193,10 @@ def test_query():
assert g.out_degree(9) == 1
assert F.allclose(g.out_degrees([8, 9]), F.tensor([0, 1]))
assert np.array_equal(F.sparse_to_numpy(g.adjacency_matrix()), scipy_coo_input().toarray().T)
assert np.array_equal(F.sparse_to_numpy(g.adjacency_matrix(transpose=True)), scipy_coo_input().toarray())
assert np.array_equal(
F.sparse_to_numpy(g.adjacency_matrix(transpose=False)), scipy_coo_input().toarray().T)
assert np.array_equal(
F.sparse_to_numpy(g.adjacency_matrix(transpose=True)), scipy_coo_input().toarray())
def _test_csr(g):
# test twice to see whether the cached format works or not
......
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