未验证 提交 8b3b1f8f 编写于 作者: H Huang Zhengjie 提交者: GitHub

Merge pull request #98 from ZHUI/graphsaint

add graphsaint support
...@@ -24,7 +24,7 @@ import ssl ...@@ -24,7 +24,7 @@ import ssl
ssl._create_default_https_context = ssl._create_unverified_context ssl._create_default_https_context = ssl._create_unverified_context
from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
#from pgl.sample import graph_saint_random_walk_sample from pgl.sample import graph_saint_random_walk_sample
from ogb.nodeproppred import Evaluator from ogb.nodeproppred import Evaluator
import tqdm import tqdm
from collections import namedtuple from collections import namedtuple
...@@ -78,10 +78,10 @@ def k_hop_sampler(graph, samples, batch_nodes): ...@@ -78,10 +78,10 @@ def k_hop_sampler(graph, samples, batch_nodes):
return subgraph, sub_node_index return subgraph, sub_node_index
#def graph_saint_randomwalk_sampler(graph, batch_nodes, max_depth=3): def graph_saint_randomwalk_sampler(graph, batch_nodes, max_depth=3):
# subgraph = graph_saint_random_walk_sample(graph, batch_nodes, max_depth) subgraph = graph_saint_random_walk_sample(graph, batch_nodes, max_depth)
# sub_node_index = subgraph.reindex_from_parrent_nodes(batch_nodes) sub_node_index = subgraph.reindex_from_parrent_nodes(batch_nodes)
# return subgraph, sub_node_index return subgraph, sub_node_index
class ArxivDataGenerator(BaseDataGenerator): class ArxivDataGenerator(BaseDataGenerator):
......
...@@ -321,3 +321,43 @@ def alias_sample_build_table(np.ndarray[np.float64_t, ndim=1] probs): ...@@ -321,3 +321,43 @@ def alias_sample_build_table(np.ndarray[np.float64_t, ndim=1] probs):
if alias[l_i] < 1: if alias[l_i] < 1:
smaller_num.push_back(l_i) smaller_num.push_back(l_i)
return alias, events return alias, events
@cython.boundscheck(False)
@cython.wraparound(False)
def extract_edges_from_nodes(
np.ndarray[np.int64_t, ndim=1] adj_indptr,
np.ndarray[np.int64_t, ndim=1] sorted_v,
np.ndarray[np.int64_t, ndim=1] sorted_eid,
vector[long long] sampled_nodes,
):
"""
Extract all eids of given sampled_nodes for the origin graph.
ret_edge_index: edge ids between sampled_nodes.
Refers: https://github.com/GraphSAINT/GraphSAINT
"""
cdef long long i, v, j
cdef long long num_v_orig, num_v_sub
cdef long long start_neigh, end_neigh
cdef vector[int] _arr_bit
cdef vector[long long] ret_edge_index
num_v_orig = adj_indptr.size-1
_arr_bit = vector[int](num_v_orig,-1)
num_v_sub = sampled_nodes.size()
i = 0
with nogil:
while i < num_v_sub:
_arr_bit[sampled_nodes[i]] = i
i = i + 1
i = 0
while i < num_v_sub:
v = sampled_nodes[i]
start_neigh = adj_indptr[v]
end_neigh = adj_indptr[v+1]
j = start_neigh
while j < end_neigh:
if _arr_bit[sorted_v[j]] > -1:
ret_edge_index.push_back(sorted_eid[j])
j = j + 1
i = i + 1
return ret_edge_index
...@@ -24,7 +24,7 @@ from pgl import graph_kernel ...@@ -24,7 +24,7 @@ from pgl import graph_kernel
__all__ = [ __all__ = [
'graphsage_sample', 'node2vec_sample', 'deepwalk_sample', 'graphsage_sample', 'node2vec_sample', 'deepwalk_sample',
'metapath_randomwalk', 'pinsage_sample' 'metapath_randomwalk', 'pinsage_sample', 'graph_saint_random_walk_sample'
] ]
...@@ -55,7 +55,7 @@ def edge_hash(src, dst): ...@@ -55,7 +55,7 @@ def edge_hash(src, dst):
def graphsage_sample(graph, nodes, samples, ignore_edges=[]): def graphsage_sample(graph, nodes, samples, ignore_edges=[]):
"""Implement of graphsage sample. """Implement of graphsage sample.
Reference paper: https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf. Reference paper: https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf.
Args: Args:
...@@ -63,7 +63,7 @@ def graphsage_sample(graph, nodes, samples, ignore_edges=[]): ...@@ -63,7 +63,7 @@ def graphsage_sample(graph, nodes, samples, ignore_edges=[]):
nodes: Sample starting from nodes nodes: Sample starting from nodes
samples: A list, number of neighbors in each layer samples: A list, number of neighbors in each layer
ignore_edges: list of edge(src, dst) will be ignored. ignore_edges: list of edge(src, dst) will be ignored.
Return: Return:
A list of subgraphs A list of subgraphs
""" """
...@@ -129,7 +129,7 @@ def alias_sample(size, alias, events): ...@@ -129,7 +129,7 @@ def alias_sample(size, alias, events):
size: Output shape. size: Output shape.
alias: The alias table build by `alias_sample_build_table`. alias: The alias table build by `alias_sample_build_table`.
events: The events table build by `alias_sample_build_table`. events: The events table build by `alias_sample_build_table`.
Return: Return:
samples: The generated random samples. samples: The generated random samples.
""" """
...@@ -283,13 +283,13 @@ def metapath_randomwalk(graph, ...@@ -283,13 +283,13 @@ def metapath_randomwalk(graph,
Args: Args:
graph: instance of pgl heterogeneous graph graph: instance of pgl heterogeneous graph
start_nodes: start nodes to generate walk start_nodes: start nodes to generate walk
metapath: meta path for sample nodes. metapath: meta path for sample nodes.
e.g: "c2p-p2a-a2p-p2c" e.g: "c2p-p2a-a2p-p2c"
walk_length: the walk length walk_length: the walk length
Return: Return:
a list of metapath walks. a list of metapath walks.
""" """
edge_types = metapath.split('-') edge_types = metapath.split('-')
...@@ -390,18 +390,18 @@ def pinsage_sample(graph, ...@@ -390,18 +390,18 @@ def pinsage_sample(graph,
norm_bais=1.0, norm_bais=1.0,
ignore_edges=set()): ignore_edges=set()):
"""Implement of graphsage sample. """Implement of graphsage sample.
Reference paper: . Reference paper: .
Args: Args:
graph: A pgl graph instance graph: A pgl graph instance
nodes: Sample starting from nodes nodes: Sample starting from nodes
samples: A list, number of neighbors in each layer samples: A list, number of neighbors in each layer
top_k: select the top_k visit count nodes to construct the edges top_k: select the top_k visit count nodes to construct the edges
proba: the probability to return the origin node proba: the probability to return the origin node
norm_bais: the normlization for the visit count norm_bais: the normlization for the visit count
ignore_edges: list of edge(src, dst) will be ignored. ignore_edges: list of edge(src, dst) will be ignored.
Return: Return:
A list of subgraphs A list of subgraphs
""" """
...@@ -476,3 +476,43 @@ def pinsage_sample(graph, ...@@ -476,3 +476,43 @@ def pinsage_sample(graph,
layer_nodes[0], dtype="int64") layer_nodes[0], dtype="int64")
return subgraphs return subgraphs
def extract_edges_from_nodes(graph, sample_nodes):
eids = graph_kernel.extract_edges_from_nodes(
graph.adj_src_index._indptr, graph.adj_src_index._sorted_v,
graph.adj_src_index._sorted_eid, sample_nodes)
return eids
def graph_saint_random_walk_sample(graph,
nodes,
max_depth,
alias_name=None,
events_name=None):
"""Implement of graph saint random walk sample.
First, this function will get random walks path for given nodes and depth.
Then, it will create subgraph from all sampled nodes.
Reference Paper: https://arxiv.org/abs/1907.04931
Args:
graph: A pgl graph instance
nodes: Walk starting from nodes
max_depth: Max walking depth
Return:
a subgraph of sampled nodes.
"""
graph.outdegree()
walks = deepwalk_sample(graph, nodes, max_depth, alias_name, events_name)
sample_nodes = []
for walk in walks:
sample_nodes.extend(walk)
sample_nodes = np.unique(sample_nodes)
eids = extract_edges_from_nodes(graph, sample_nodes)
subgraph = graph.subgraph(
nodes=sample_nodes, eid=eids, with_node_feat=True, with_edge_feat=True)
subgraph.node_feat["index"] = np.array(sample_nodes, dtype="int64")
return subgraph
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""graph saint sample test
"""
from __future__ import division
from __future__ import absolute_import
from __future__ import print_function
from __future__ import unicode_literals
import unittest
import numpy as np
import pgl
import paddle.fluid as fluid
from pgl.sample import graph_saint_random_walk_sample
class GraphSaintSampleTest(unittest.TestCase):
"""GraphSaintSampleTest"""
def test_randomwalk_sampler(self):
"""test_randomwalk_sampler"""
g = pgl.graph.Graph(
num_nodes=8,
edges=[(1, 2), (2, 3), (0, 2), (0, 1), (6, 7), (4, 5), (6, 4),
(7, 4), (3, 4)])
subgraph = graph_saint_random_walk_sample(g, [6, 7], 2)
print('reindex', subgraph._from_reindex)
print('subedges', subgraph.edges)
assert len(subgraph.nodes) == 4
assert len(subgraph.edges) == 4
true_edges = np.array([[0, 1], [2, 3], [2, 0], [3, 0]])
assert "{}".format(subgraph.edges) == "{}".format(true_edges)
if __name__ == '__main__':
unittest.main()
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