def __init__(self,
G_list,
features='default',
normalize=True,
assign_feat='default',
max_num_nodes=0):
# keep all variables derived with *self* except for *adj_all*
self.adj_all = []
self.len_all = []
self.feature_all = []
self.label_all = []
self.assign_feat_all = []
if max_num_nodes == 0:
self.max_num_nodes = max([G.number_of_nodes() for G in G_list])
else:
self.max_num_nodes = max_num_nodes
#if features == 'default':
self.feat_dim = util.node_dict(G_list[0])[0]['feat'].shape[0]
for G in G_list:
adj = np.array(
nx.to_numpy_matrix(G)) # related to load_date.py 95 rows
if normalize:
sqrt_deg = np.diag(
1.0 / np.sqrt(np.sum(adj, axis=0, dtype=float).squeeze()))
adj = np.matmul(np.matmul(sqrt_deg, adj), sqrt_deg)
self.adj_all.append(adj)
self.len_all.append(G.number_of_nodes())
self.label_all.append(G.graph['label'])
# feat matrix: max_num_nodes x feat_dim
if features == 'default':
f = np.zeros((self.max_num_nodes, self.feat_dim), dtype=float)
for i, u in enumerate(G.nodes()):
f[i, :] = util.node_dict(G)[u]['feat']
self.feature_all.append(f)
elif features == 'id':
self.feature_all.append(np.identity(self.max_num_nodes))
elif features == 'deg-num':
degs = np.sum(np.array(adj), 1)
degs = np.expand_dims(np.pad(
degs, [0, self.max_num_nodes - G.number_of_nodes()], 0),
axis=1)
self.feature_all.append(degs)
elif features == 'deg':
self.max_deg = 10
degs = np.sum(np.array(adj), 1).astype(int)
degs[degs > max_deg] = max_deg
feat = np.zeros((len(degs), self.max_deg + 1))
feat[np.arange(len(degs)), degs] = 1
feat = np.pad(feat,
((0, self.max_num_nodes - G.number_of_nodes()),
(0, 0)),
'constant',
constant_values=0)
f = np.zeros((self.max_num_nodes, self.feat_dim), dtype=float)
for i, u in enumerate(util.node_iter(G)):
f[i, :] = util.node_dict(G)[u]['feat']
feat = np.concatenate((feat, f), axis=1)
self.feature_all.append(feat)
elif features == 'struct':
self.max_deg = 10
degs = np.sum(np.array(adj), 1).astype(int)
degs[degs > 10] = 10
feat = np.zeros((len(degs), self.max_deg + 1))
feat[np.arange(len(degs)), degs] = 1
degs = np.pad(feat,
((0, self.max_num_nodes - G.number_of_nodes()),
(0, 0)),
'constant',
constant_values=0)
clusterings = np.array(list(nx.clustering(G).values()))
clusterings = np.expand_dims(np.pad(
clusterings, [0, self.max_num_nodes - G.number_of_nodes()],
'constant'),
axis=1)
g_feat = np.hstack([degs, clusterings])
if 'feat' in util.node_dict(G)[0]:
node_feats = np.array([
util.node_dict(G)[i]['feat']
for i in range(G.number_of_nodes())
])
node_feats = np.pad(
node_feats,
((0, self.max_num_nodes - G.number_of_nodes()),
(0, 0)), 'constant')
g_feat = np.hstack([g_feat, node_feats])
self.feature_all.append(g_feat)
if assign_feat == 'id':
self.assign_feat_all.append(
np.hstack((np.identity(self.max_num_nodes),
self.feature_all[-1])))
else:
self.assign_feat_all.append(self.feature_all[-1])
self.feat_dim = self.feature_all[0].shape[1]
self.assign_feat_dim = self.assign_feat_all[0].shape[1]
def read_graphfile(datadir, dataname, max_nodes=None):
''' Read data from https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets
graph index starts with 1 in file
Returns:
List of networkx objects with graph and node labels
'''
prefix = os.path.join(datadir, dataname, dataname)
filename_graph_indic = prefix + '_graph_indicator.txt'
# index of graphs that a given node belongs to
graph_indic={}
with open(filename_graph_indic) as f:
i=1
for line in f:
line=line.strip("\n")
graph_indic[i]=int(line)
i+=1
filename_nodes=prefix + '_node_labels.txt'
node_labels=[]
try:
with open(filename_nodes) as f:
for line in f:
line=line.strip("\n")
node_labels+=[int(line) - 1]
num_unique_node_labels = max(node_labels) + 1
except IOError:
print('No node labels')
filename_node_attrs=prefix + '_node_attributes.txt'
node_attrs=[]
try:
with open(filename_node_attrs) as f:
for line in f:
line = line.strip("\s\n")
attrs = [float(attr) for attr in re.split("[,\s]+", line) if not attr == '']
node_attrs.append(np.array(attrs))
except IOError:
print('No node attributes')
label_has_zero = False
filename_graphs=prefix + '_graph_labels.txt'
graph_labels=[]
# assume that all graph labels appear in the dataset
#(set of labels don't have to be consecutive)
label_vals = []
with open(filename_graphs) as f:
for line in f:
line=line.strip("\n")
val = int(line)
#if val == 0:
# label_has_zero = True
if val not in label_vals:
label_vals.append(val)
graph_labels.append(val)
#graph_labels = np.array(graph_labels)
label_map_to_int = {val: i for i, val in enumerate(label_vals)}
graph_labels = np.array([label_map_to_int[l] for l in graph_labels])
#if label_has_zero:
# graph_labels += 1
filename_adj=prefix + '_A.txt'
adj_list={i:[] for i in range(1,len(graph_labels)+1)}
index_graph={i:[] for i in range(1,len(graph_labels)+1)}
num_edges = 0
with open(filename_adj) as f:
for line in f:
line=line.strip("\n").split(",")
e0,e1=(int(line[0].strip(" ")),int(line[1].strip(" ")))
adj_list[graph_indic[e0]].append((e0,e1))
index_graph[graph_indic[e0]]+=[e0,e1]
num_edges += 1
for k in index_graph.keys():
index_graph[k]=[u-1 for u in set(index_graph[k])]
graphs=[]
for i in range(1,1+len(adj_list)):
# indexed from 1 here
G=nx.from_edgelist(adj_list[i])
if max_nodes is not None and G.number_of_nodes() > max_nodes:
continue
# add features and labels
G.graph['label'] = graph_labels[i-1]
for u in util.node_iter(G):
if len(node_labels) > 0:
node_label_one_hot = [0] * num_unique_node_labels
node_label = node_labels[u-1]
node_label_one_hot[node_label] = 1
util.node_dict(G)[u]['label'] = node_label_one_hot
if len(node_attrs) > 0:
util.node_dict(G)[u]['feat'] = node_attrs[u-1]
if len(node_attrs) > 0:
G.graph['feat_dim'] = node_attrs[0].shape[0]
# relabeling
mapping={}
it=0
for n in util.node_iter(G):
mapping[n]=it
it+=1
# indexed from 0
graphs.append(nx.relabel_nodes(G, mapping))
return graphs
def read_graphfile2(datadir, dataname, max_nodes=None):
''' Read data from https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets
graph index starts with 1 in file
Returns:
List of networkx objects with graph and node labels
'''
prefix = os.path.join(datadir, dataname, dataname)
filename_graph_indic = prefix + '_graph_indicator.txt'
# index of graphs that a given node belongs to
graph_indic = {} # {} 表示字典
with open(filename_graph_indic) as f:
i = 1
for line in f:
line = line.strip("\n") #strip("\n") 去掉换行符号
graph_indic[i] = int(line)
i += 1
filename_nodes = prefix + '_node_labels.txt'
node_labels = []
try:
with open(filename_nodes) as f:
for line in f:
line = line.strip("\n")
node_labels += [int(line) - 1] #俩个列表之间直接相加
num_unique_node_labels = max(node_labels) + 1
except IOError:
print('No node labels')
filename_node_attrs = prefix + '_node_attributes.txt'
node_attrs = []
try:
with open(filename_node_attrs) as f:
for line in f:
line = line.strip("\s\n")
attrs = [
float(attr) for attr in re.split("[,\s]+", line)
if not attr == ''
]
node_attrs.append(np.array(attrs))
except IOError:
print('No node attributes')
label_has_zero = False
filename_graphs = prefix + '_graph_labels.txt'
graph_labels = []
# assume that all graph labels appear in the dataset
# (set of labels don't have to be consecutive)
label_vals = []
with open(filename_graphs) as f:
for line in f:
line = line.strip("\n")
val = int(line)
# if val == 0:
# label_has_zero = True
if val not in label_vals:
label_vals.append(val)
graph_labels.append(val)
# graph_labels = np.array(graph_labels)
label_map_to_int = {val: i for i, val in enumerate(label_vals)}
graph_labels = np.array([label_map_to_int[l] for l in graph_labels])
# if label_has_zero:
# graph_labels += 1
filename_adj = prefix + '_A.txt'
#underlying is the source code
# adj_list = {i: [] for i in range(1, len(graph_labels) + 1)} #
# # common_adj = [] #modify by zy
# index_graph = {i: [] for i in range(1, len(graph_labels) + 1)} #
# num_edges = 0
# with open(filename_adj) as f:
# for line in f:
# line = line.strip("\n").split(",")
# e0, e1 = (int(line[0].strip(" ")), int(line[1].strip(" ")))
# adj_list[graph_indic[e0]].append((e0, e1)) #
#
# #if e1 > max_nodes: #modify by zy
# #break #modify by zy
# #common_adj.append((e0, e1)) #modify by zy
# index_graph[graph_indic[e0]]+=[e0,e1]#
# num_edges += 1
# for k in index_graph.keys():
# index_graph[k]=[u-1 for u in set(index_graph[k])]
#
#
# graphs = []
# for i in range(1, 1 + len(adj_list)):
# # indexed from 1 here
# G=nx.from_edgelist(adj_list[i]) # related to graph_sampler.py 29 rows
# #print(isinstance(G,Dictionary))
# if max_nodes is not None and G.number_of_nodes() > max_nodes:
# continue
#
# # add features and labels
# G.graph['label'] = graph_labels[i - 1]
# for u in util.node_iter(G):
# if len(node_labels) > 0:
# node_label_one_hot = [0] * num_unique_node_labels
# node_label = node_labels[u - 1]
# node_label_one_hot[node_label] = 1
# util.node_dict(G)[u]['label'] = node_label_one_hot
# if len(node_attrs) > 0:
# util.node_dict(G)[u]['feat'] = node_attrs[u - 1]
# if len(node_attrs) > 0:
# G.graph['feat_dim'] = node_attrs[0].shape[0]
#
# # relabeling
# mapping = {}
#
#
#
# it = 0
# for n in util.node_iter(G):
# mapping[n] = it
# it += 1
# #NewG=nx.relabel_nodes(G,mapping)
# # indexed from 0
# graphs.append(nx.relabel_nodes(G, mapping))
# return graphs
common_adj = []
num_edges = 0
with open(filename_adj) as f:
for line in f:
line = line.strip("\n").split(",")
e0, e1 = (int(line[0].strip(" ")), int(line[1].strip(" ")))
if e1 > max_nodes:
break
common_adj.append((e0, e1))
num_edges += 1
Hlist = []
for i in range(1, 1 + len(graph_labels)):
# indexed from 1 here
LAlist = []
#LAlist[]用于存放标签和属性特征
G = nx.from_edgelist(common_adj) # related to graph_sampler.py 29 rows
# print(type(G))
if max_nodes is not None and G.number_of_nodes() > max_nodes:
continue
# add features and labels
G.graph['label'] = graph_labels[i - 1]
LAlist.append(G.graph['label'])
TList = []
for u in util.node_iter(G):
# if len(node_labels) > 0:
# node_label_one_hot = [0] * num_unique_node_labels
# node_label = node_labels[u - 1]
# node_label_one_hot[node_label] = 1
# util.node_dict(G)[u]['label'] = node_label_one_hot
if len(node_attrs) > 0:
util.node_dict(G)[u]['feat'] = node_attrs[(u - 1) +
(i - 1) * 2592]
TList.append(util.node_dict(G)[u]['feat'])
LAlist.append(np.array(TList))
if len(node_attrs) > 0:
G.graph['feat_dim'] = node_attrs[0].shape[0]
# relabeling
mapping = {}
it = 0
for n in util.node_iter(G):
mapping[n] = it
it += 1
New_G = nx.relabel_nodes(G, mapping)
# indexed from 0
Hlist.append(LAlist)
return Hlist, New_G