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+XGNN-impl
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diff --git a/GraphGenerator.py b/GraphGenerator.py
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+++ b/GraphGenerator.py
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+import random
+import copy
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from Model import GraphConvolution, GCN
+
+rollout = 10
+MAX_NUM_NODES = 28 # for mutag
+random.seed(200)
+
+class Generator(nn.Module):
+ def __init__(self, model_path: str, C: list, node_feature_dim: int ,num_class = 2, c=0, hyp1=1, hyp2=2, start=None, nfeat=7, dropout=0.1):
+ """
+ :param C: Candidate set of nodes (list)
+ :param start: Starting node (defaults to randomised node)
+ """
+ super(Generator, self).__init__()
+ self.nfeat = nfeat
+ self.dropout = dropout
+ self.c = c # c为要指定生成类别c的图
+
+ self.fc = nn.Linear(nfeat, 8)
+ self.gc1 = GraphConvolution(8, 16)
+ self.gc2 = GraphConvolution(16, 24)
+ self.gc3 = GraphConvolution(24, 32)
+
+ # MLP1
+ # 2 FC layers with hidden dimension 16
+ self.mlp1 = nn.Sequential(nn.Linear(32, 16), nn.Linear(16, 1))
+
+ # MLP2
+ # 2 FC layers with hidden dimension 24
+ self.mlp2 = nn.Sequential(nn.Linear(64, 24), nn.Linear(24, 1))
+
+ # Hyperparameters
+ self.hyp1 = hyp1
+ self.hyp2 = hyp2
+ self.candidate_set = C
+
+ # Default starting node (if any)
+ if start is not None:
+ self.start = start
+ self.random_start = False
+ else:
+ self.start = random.choice(np.arange(0, len(self.candidate_set)))
+ self.random_start = True
+
+ # Load GCN for calculating reward
+ self.model = GCN(nfeat=node_feature_dim,
+ nclass=num_class,
+ dropout=dropout)
+
+ self.model.load_state_dict(torch.load(model_path))
+ for param in self.model.parameters():
+ param.requires_grad = False
+
+ self.reset_graph()
+
+ def reset_graph(self):
+ """
+ Reset g.G to default graph with only start node， 生成一个只有1个结点的图
+ """
+ if self.random_start == True:
+ self.start = random.choice(np.arange(0, len(self.candidate_set)))
+
+ # 初始图除了第1个结点全被mask，这里由于邻接矩阵的边长为MAX_NUM_NODES + len(self.candidate_set)，所以mask的不仅为候选集结点，还有图中的所以虚结点
+ mask_start = torch.BoolTensor(
+ [False if i == 0 else True for i in range(MAX_NUM_NODES + len(self.candidate_set))])
+
+ adj = torch.zeros((MAX_NUM_NODES + len(self.candidate_set), MAX_NUM_NODES + len(self.candidate_set)),
+ dtype=torch.float32) # 这里adj shape为 [MAX_NUM_NODES + len(self.candidate_set), MAX_NUM_NODES + len(self.candidate_set)] 中间可能有空结点
+
+ feat = torch.zeros((MAX_NUM_NODES + len(self.candidate_set), len(self.candidate_set)), dtype=torch.float32)
+ feat[0, self.start] = 1
+ feat[np.arange(-len(self.candidate_set), 0), np.arange(0, len(self.candidate_set))] = 1
+
+ degrees = torch.zeros(MAX_NUM_NODES)
+
+ self.G = {'adj': adj, 'feat': feat, 'degrees': degrees, 'num_nodes': 1, 'mask_start': mask_start}
+
+ ## 计算Gt->Gt+1
+ def forward(self, G_in):
+ ## G_in为 Gt
+ G = copy.deepcopy(G_in)
+
+ x = G['feat'].detach().clone() # Gt的特征矩阵
+ adj = G['adj'].detach().clone() # Gt的邻接矩阵
+
+ ## 对应 X = GCNs(Gt,C)
+ x = F.relu6(self.fc(x))
+ x = F.dropout(x, self.dropout, training=self.training)
+ x = F.relu6(self.gc1(x, adj))
+ x = F.dropout(x, self.dropout, training=self.training)
+ x = F.relu6(self.gc2(x, adj))
+ x = F.dropout(x, self.dropout, training=self.training)
+ x = F.relu6(self.gc3(x, adj))
+ x = F.dropout(x, self.dropout, training=self.training)
+
+ ## pt,start=Softmax(MLPs(X))
+ p_start = self.mlp1(x)
+ p_start = p_start.masked_fill(G['mask_start'].unsqueeze(1), 0)
+ p_start = F.softmax(p_start, dim=0)
+ a_start_idx = torch.argmax(p_start.masked_fill(G['mask_start'].unsqueeze(1), -1))
+
+ ## pt,end=Softmax(MLPs([X,x^start))
+ # broadcast
+ x1, x2 = torch.broadcast_tensors(x, x[a_start_idx])
+ x = torch.cat((x1, x2), 1) # cat increases dim from 32 to 64
+
+ # 计算maskt,end，除了候选集和Gt结点中未被选为初始结点的结点之外，其它均被mask
+ mask_end = torch.BoolTensor([True for i in range(MAX_NUM_NODES + len(self.candidate_set))])
+ mask_end[MAX_NUM_NODES:] = False
+ mask_end[:G['num_nodes']] = False
+ mask_end[a_start_idx] = True
+
+ p_end = self.mlp2(x)
+ p_end = p_end.masked_fill(mask_end.unsqueeze(1), 0)
+ p_end = F.softmax(p_end, dim=0)
+ a_end_idx = torch.argmax(p_end.masked_fill(mask_end.unsqueeze(1), -1))
+
+ # Return new G
+ # If a_end_idx is not masked, node exists in graph, no new node added
+ if G['mask_start'][a_end_idx] == False:
+ G['adj'][a_end_idx][a_start_idx] += 1
+ G['adj'][a_start_idx][a_end_idx] += 1
+
+ # Update degrees
+ G['degrees'][a_start_idx] += 1
+ G['degrees'][G['num_nodes']] += 1
+ else:
+ # Add node
+ G['feat'][G['num_nodes']] = G['feat'][a_end_idx]
+ # Add edge
+ G['adj'][G['num_nodes']][a_start_idx] += 1
+ G['adj'][a_start_idx][G['num_nodes']] += 1
+ # Update degrees
+ G['degrees'][a_start_idx] += 1
+ G['degrees'][G['num_nodes']] += 1
+
+ # Update start mask
+ G_mask_start_copy = G['mask_start'].detach().clone()
+ G_mask_start_copy[G['num_nodes']] = False
+ G['mask_start'] = G_mask_start_copy
+
+ G['num_nodes'] += 1
+
+ return p_start, a_start_idx, p_end, a_end_idx, G
+
+
+ ### reward函数
+ def calculate_reward(self, G_t_1):
+ """
+ Rtr Calculated from graph rules to encourage generated graphs to be valid
+ 1. Only one edge to be added between any two nodes
+ 2. Generated graph cannot contain more nodes than predefined maximum node number
+ 3. (For chemical) Degree cannot exceed valency
+ If generated graph violates graph rule, Rtr = -1
+
+ Rtf Feedback from trained model
+ """
+
+ rtr = self.check_graph_rules(G_t_1)
+
+ rtf = self.calculate_reward_feedback(G_t_1)
+ rtf_sum = 0
+ for m in range(rollout):
+ p_start, a_start, p_end, a_end, G_t_1 = self.forward(G_t_1)
+ rtf_sum += self.calculate_reward_feedback(G_t_1)
+ rtf = rtf + rtf_sum * self.hyp1 / rollout
+
+ return rtf + self.hyp2 * rtr
+
+ def calculate_reward_feedback(self, G_t_1):
+ """
+ p(f(G_t_1) = c) - 1/l
+ where l denotes number of possible classes for f
+ """
+ f = self.model(G_t_1['feat'], G_t_1['adj'])
+ return f[self.c] - 1 / len(f)
+
+
+ ## graph rules
+ def check_graph_rules(self, G_t_1):
+ """
+ For mutag, node degrees cannot exceed valency
+ """
+ idx = 0
+
+ for d in G_t_1['degrees']:
+ if d is not 0:
+ node_id = torch.argmax(G_t_1['feat'][idx]) # Eg. [0, 1, 0, 0] -> 1
+ node = self.candidate_set[node_id] # Eg ['C.4', 'F.2', 'Br.7'][1] = 'F.2'
+ max_valency = int(node.split('.')[1]) # Eg. C.4 -> ['C', '4'] -> 4
+
+ # If any node degree exceeds its valency, return -1
+ if max_valency < d:
+ return -1
+
+ return 0
+
+
+ ## 计算loss
+ def calculate_loss(self, Rt, p_start, a_start, p_end, a_end, G_t_1):
+ """
+ Calculated from cross entropy loss (Lce) and reward function (Rt)
+ where loss = -Rt*(Lce_start + Lce_end)
+ """
+
+ Lce_start = F.cross_entropy(torch.reshape(p_start, (1, 35)), a_start.unsqueeze(0))
+ Lce_end = F.cross_entropy(torch.reshape(p_end, (1, 35)), a_end.unsqueeze(0))
+
+ return -Rt * (Lce_start + Lce_end)
\ No newline at end of file
diff --git a/Load_dataset.py b/Load_dataset.py
new file mode 100644
index 0000000..034f536
--- /dev/null
+++ b/Load_dataset.py
@@ -0,0 +1,117 @@
+import numpy as np
+import scipy.sparse as sp
+import torch
+
+
+# MUTAG数据集特征，188个图，总共3371个结点，7442条边，为无向图
+
+def encode_onehot(labels):
+ classes = set(labels)
+ classes_dict = {c: np.identity(len(classes))[i, :] for i, c in
+ enumerate(classes)}
+ labels_onehot = np.array(list(map(classes_dict.get, labels)),
+ dtype=np.int32)
+ return labels_onehot
+
+
+def normalize(mx):
+ """Row-normalize sparse matrix"""
+ rowsum = np.array(mx.sum(1))
+ r_inv = np.power(rowsum, -1).flatten()
+ r_inv[np.isinf(r_inv)] = 0.
+ r_mat_inv = sp.diags(r_inv)
+ mx = r_mat_inv.dot(mx)
+ return mx
+
+def accuracy(output, labels):
+ preds = output.max(1)[1].type_as(labels)
+ correct = preds.eq(labels).double()
+ correct = correct.sum()
+ return correct / len(labels)
+
+
+
+def load_split_MUTAG_data(path="datas/MUTAG/", dataset="MUTAG_", split_train=0.7, split_val=0.15):
+ """Load MUTAG data """
+ print('Loading {} dataset...'.format(dataset))
+
+ # 加载图的标签
+ graph_labels = np.genfromtxt("{}{}graph_labels.txt".format(path, dataset),
+ dtype=np.dtype(int))
+ graph_labels = encode_onehot(graph_labels) # (188, 2)
+ graph_labels = torch.LongTensor(np.where(graph_labels)[1]) # (188, 1)
+
+
+ # 图结点的索引号
+ graph_idx = np.genfromtxt("{}{}graph_indicator.txt".format(path, dataset),
+ dtype=np.dtype(int))
+
+ graph_idx = np.array(graph_idx, dtype=np.int32)
+ idx_map = {j: i for i, j in enumerate(graph_idx)} # key, value表示第key个图的起始结点索引号为value
+ length = len(idx_map.keys()) # 总共有多少个图
+ num_nodes = [idx_map[n] - idx_map[n - 1] if n - 1 > 1 else idx_map[n] for n in range(1, length + 1)] # 一个长度188的list，表示没个图有多少个结点
+ max_num_nodes = max(num_nodes) # 最大的一个图有多少个结点
+ features_list = []
+ adj_list = []
+ prev = 0
+
+ # 结点的标签
+ nodeidx_features = np.genfromtxt("{}{}node_labels.txt".format(path, dataset), delimiter=",",
+ dtype=np.dtype(int))
+ node_features = np.zeros((nodeidx_features.shape[0], max(nodeidx_features) + 1))
+ node_features[np.arange(nodeidx_features.shape[0]), nodeidx_features] = 1
+
+ # 边信息
+ edges_unordered = np.genfromtxt("{}{}A.txt".format(path, dataset), delimiter=",",
+ dtype=np.int32)
+
+ # 边的标签
+ edges_label = np.genfromtxt("{}{}edge_labels.txt".format(path, dataset), delimiter=",",
+ dtype=np.int32) # shape = (7442,)
+
+ # 生成邻接矩阵A，该邻接矩阵包括了数据集中所有的边
+ adj = sp.coo_matrix((edges_label, (edges_unordered[:, 0] - 1, edges_unordered[:, 1] - 1)))
+
+ # 论文里A^=(D~)^0.5 A~ (D~)^0.5这个公式
+ adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
+
+ node_features = normalize(node_features)
+ adj = normalize(adj + sp.eye(adj.shape[0])) # 对应公式A~=A+IN
+ adj = adj.todense()
+
+ for n in range(1, length + 1):
+ # entry为第n个图的特征矩阵X
+ entry = np.zeros((max_num_nodes, max(nodeidx_features) + 1))
+ entry[:idx_map[n] - prev] = node_features[prev:idx_map[n]]
+ entry = torch.FloatTensor(entry)
+ features_list.append(entry.tolist())
+
+ # entry为第n个图的邻接矩阵A
+ entry = np.zeros((max_num_nodes, max_num_nodes))
+ entry[:idx_map[n] - prev, :idx_map[n] - prev] = adj[prev:idx_map[n], prev:idx_map[n]]
+ entry = torch.FloatTensor(entry)
+ adj_list.append(entry.tolist())
+
+ prev = idx_map[n] # prev为下个图起始结点的索引号
+
+ num_total = max(graph_idx)
+ num_train = int(split_train * num_total)
+ num_val = int((split_train + split_val) * num_total)
+
+ if (num_train == num_val or num_val == num_total):
+ return
+
+ features_list = torch.FloatTensor(features_list)
+ adj_list = torch.FloatTensor(adj_list)
+
+ idx_train = range(num_train)
+ idx_val = range(num_train, num_val)
+ idx_test = range(num_val, num_total)
+
+ idx_train = torch.LongTensor(idx_train)
+ idx_val = torch.LongTensor(idx_val)
+ idx_test = torch.LongTensor(idx_test)
+
+ # 返回值一次为 188个图的邻接矩阵列表 188个图的特征矩阵列表 188个图的label， 每个图的起始结点索引号， 训练集索引号，
+ # 验证集索引号， 测试集索引号
+ return adj_list, features_list, graph_labels, idx_map, idx_train, idx_val, idx_test
\ No newline at end of file
diff --git a/Model.py b/Model.py
new file mode 100644
index 0000000..0e3731c
--- /dev/null
+++ b/Model.py
@@ -0,0 +1,90 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+
+
+class GraphConvolution(nn.Module):
+ """
+ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
+ paper: Semi-Supervised Classification with Graph Convolutional Networks
+ """
+ # 模型的参数包括weight和bias
+ def __init__(self, in_features, out_features):
+ super(GraphConvolution, self).__init__()
+ self.in_features = in_features
+ self.out_features = out_features
+ self.weight = Parameter(torch.FloatTensor(in_features, out_features))
+ self.bias = Parameter(torch.FloatTensor(out_features))
+ self.reset_parameters()
+
+ # 权重初始化
+ def reset_parameters(self):
+ stdv = 1. / math.sqrt(self.weight.size(1))
+ self.weight.data.uniform_(-stdv, stdv)
+ self.bias.data.uniform_(-stdv, stdv)
+
+ # 类似于tostring
+ def __repr__(self):
+ return self.__class__.__name__ + ' (' \
+ + str(self.in_features) + ' -> ' \
+ + str(self.out_features) + ')'
+
+ # 计算A~ X W(0)
+ def forward(self, input, adj):
+ # input.shape = [max_node, features] = X
+ # adj.shape = [max_node, max_node] = A~
+ # torch.mm(a, b)是矩阵a和b矩阵相乘，torch.mul(a, b)是矩阵a和b对应位相乘，a和b的维度必须相等
+ support = torch.mm(input, self.weight)
+ output = torch.spmm(adj, support)
+ return output + self.bias
+
+
+class GCN(nn.Module):
+ # feature的个数；最终的分类数
+
+ def __init__(self, nfeat, nclass, dropout):
+ """ As per paper """
+ """ 3 layers of GCNs with output dimensions equal to 32, 48, 64 respectively and average all node features """
+ """ Final classifier with 2 fully connected layers and hidden dimension set to 32 """
+ """ Activation function - ReLu (Mutag) """
+ super(GCN, self).__init__()
+
+ self.dropout = dropout
+
+ self.gc1 = GraphConvolution(nfeat, 32)
+ self.gc2 = GraphConvolution(32, 48)
+ self.gc3 = GraphConvolution(48, 64)
+ self.fc1 = nn.Linear(64, 32)
+ self.fc2 = nn.Linear(32, nclass)
+
+ def forward(self, x, adj):
+ # x.shape = [max_node, features]
+ # adj.shape = [max_node, max_node]
+ x = F.relu(self.gc1(x, adj))
+ x = F.dropout(x, self.dropout, training=self.training)
+ x = F.relu(self.gc2(x, adj))
+ x = F.dropout(x, self.dropout, training=self.training)
+ x = F.relu(self.gc3(x, adj))
+
+
+ y = torch.mean(x, 0) # 采用mean作为聚合函数聚合所有结点的特征
+ y = F.relu(self.fc1(y))
+ y = F.dropout(y, self.dropout, training=self.training)
+ y = F.softmax(self.fc2(y), dim=0)
+
+ return y
+
+
+if __name__ == '__main__':
+ input = torch.rand(29, 7)
+ adj = torch.rand(29, 29)
+
+ model = GCN(nfeat=7, # nfeat = 7
+ nclass=2, # nclass = 7
+ dropout=0.1)
+
+ output = model(input, adj)
+ print(output.size())
\ No newline at end of file
diff --git a/TrainGenerator.py b/TrainGenerator.py
new file mode 100644
index 0000000..7549ec7
--- /dev/null
+++ b/TrainGenerator.py
@@ -0,0 +1,86 @@
+from GraphGenerator import Generator
+import copy
+
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+
+import torch
+import torch.optim as optim
+
+lr = 0.01
+b1 = 0.9
+b2 = 0.99
+hyp1 = 1
+hyp2 = 2
+max_gen_step = 10 # T = 10
+
+candidate_set = ['C.4', 'N.5', 'O.2', 'F.1', 'I.7', 'Cl.7', 'Br.5'] # C.4表明碳原子的度不超过4
+model_path = 'model/gcn_first.pth'
+
+## 训练generator
+def train_generator(c=0, max_nodes=5):
+ g.c = c
+ for i in range(max_gen_step):
+ optimizer.zero_grad()
+ G = copy.deepcopy(g.G)
+ p_start, a_start, p_end, a_end, G = g.forward(G)
+
+ Rt = g.calculate_reward(G)
+ loss = g.calculate_loss(Rt, p_start, a_start, p_end, a_end, G)
+ loss.backward()
+ optimizer.step()
+
+ if G['num_nodes'] > max_nodes:
+ g.reset_graph()
+ elif Rt > 0:
+ g.G = G
+
+
+## 生成图
+def generate_graph(c=0, max_nodes=5):
+ g.c = c
+ g.reset_graph()
+
+ for i in range(max_gen_step):
+ G = copy.deepcopy(g.G)
+ p_start, a_start, p_end, a_end, G = g.forward(G)
+ Rt = g.calculate_reward(G)
+
+ if G['num_nodes'] > max_nodes:
+ return g.G
+ elif Rt > 0:
+ g.G = G
+
+ return g.G
+
+## 画图
+def display_graph(G):
+ G_nx = nx.from_numpy_matrix(np.asmatrix(G['adj'][:G['num_nodes'], :G['num_nodes']].numpy()))
+ # nx.draw_networkx(G_nx)
+
+ layout=nx.spring_layout(G_nx)
+ nx.draw(G_nx, layout)
+
+ coloring=torch.argmax(G['feat'],1)
+ colors=['b','g','r','c','m','y','k']
+
+ for i in range(7):
+ nx.draw_networkx_nodes(G_nx,pos=layout,nodelist=[x for x in G_nx.nodes() if coloring[x]==i],node_color=colors[i])
+ nx.draw_networkx_labels(G_nx,pos=layout,labels={x:candidate_set[i].split('.')[0] for x in G_nx.nodes() if coloring[x]==i})
+ nx.draw_networkx_edges(G_nx,pos=layout,width=list(nx.get_edge_attributes(G_nx,'weight').values()))
+ nx.draw_networkx_edge_labels(G_nx,pos=layout,edge_labels=nx.get_edge_attributes(G_nx, "weight"))
+
+ plt.show()
+
+if __name__ == '__main__':
+ g = Generator(model_path = model_path, C = candidate_set, node_feature_dim=7 ,c=0, start=0)
+ optimizer = optim.Adam(g.parameters(), lr=lr, betas=(b1, b2))
+
+ for i in range(1, 10):
+ ## 生成最多分别包括i个结点的图结构
+ g.reset_graph()
+ train_generator(c=1, max_nodes=i)
+ to_display = generate_graph(c=1, max_nodes=i)
+ display_graph(to_display)
+ print(g.model(to_display['feat'], to_display['adj']))
\ No newline at end of file
diff --git a/__pycache__/GraphGenerator.cpython-38.pyc b/__pycache__/GraphGenerator.cpython-38.pyc
new file mode 100644
index 0000000..81264ac
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diff --git a/__pycache__/Load_dataset.cpython-38.pyc b/__pycache__/Load_dataset.cpython-38.pyc
new file mode 100644
index 0000000..ddd1f73
Binary files /dev/null and b/__pycache__/Load_dataset.cpython-38.pyc differ
diff --git a/__pycache__/Model.cpython-38.pyc b/__pycache__/Model.cpython-38.pyc
new file mode 100644
index 0000000..e2fa6b7
Binary files /dev/null and b/__pycache__/Model.cpython-38.pyc differ
diff --git a/check.py b/check.py
new file mode 100644
index 0000000..c117885
--- /dev/null
+++ b/check.py
@@ -0,0 +1,79 @@
+import numpy as np
+import scipy.sparse as sp
+from Load_dataset import encode_onehot, normalize
+
+import torch
+
+src_path = 'datas/MUTAG/MUTAG_{}'
+split_train=0.7
+split_val=0.15
+
+if __name__ == '__main__':
+ nodeidx_features = np.genfromtxt(src_path.format('node_labels.txt'), delimiter=",",
+ dtype=np.dtype(int))
+ node_features = np.zeros((nodeidx_features.shape[0], max(nodeidx_features) + 1))
+ node_features[np.arange(nodeidx_features.shape[0]), nodeidx_features] = 1
+
+
+ graph_labels = np.genfromtxt(src_path.format('graph_labels.txt'), dtype=np.dtype(int))
+ graph_labels = encode_onehot(graph_labels)
+ graph_labels = torch.LongTensor(np.where(graph_labels)[1])
+
+
+ graph_idx = np.genfromtxt(src_path.format('graph_indicator.txt'),dtype=np.dtype(int))
+ graph_idx = np.array(graph_idx, dtype=np.int32)
+
+
+ edges_unordered = np.genfromtxt(src_path.format('A.txt'), delimiter=",",
+ dtype=np.int32) # (7442,2)
+
+ edges_label = np.genfromtxt(src_path.format('edge_labels.txt'), delimiter=",",
+ dtype=np.int32)
+
+ # 邻接矩阵
+ adj = sp.coo_matrix((edges_label, (edges_unordered[:, 0] - 1, edges_unordered[:, 1] - 1)))
+ adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj) # (3371, 3371)
+
+ idx_map = {j: i for i, j in enumerate(graph_idx)} # key, value表示第key个图的起始结点索引号为value
+ length = len(idx_map.keys()) # 总共有多少个图 , 188
+ num_nodes = [idx_map[n] - idx_map[n - 1] if n - 1 > 1 else idx_map[n] for n in range(1, length + 1)] # 一个长度188的list，表示没个图有多少个结点
+ max_num_nodes = max(num_nodes) # 最大的一个图有多少个结点 实际29
+ features_list = []
+ adj_list = []
+ prev = 0
+
+ node_features = normalize(node_features) # (3371, 7)
+ adj = normalize(adj + sp.eye(adj.shape[0]))
+ adj = adj.todense()
+
+ for n in range(1, length + 1):
+ # entry为图的特征矩阵X
+ entry = np.zeros((max_num_nodes, max(nodeidx_features) + 1))
+ entry[:idx_map[n] - prev] = node_features[prev:idx_map[n]]
+ entry = torch.FloatTensor(entry)
+ features_list.append(entry)
+
+ # entry为图的邻接矩阵A
+ entry = np.zeros((max_num_nodes, max_num_nodes))
+ entry[:idx_map[n] - prev, :idx_map[n] - prev] = adj[prev:idx_map[n], prev:idx_map[n]]
+ entry = torch.FloatTensor(entry)
+ adj_list.append(entry)
+
+ prev = idx_map[n] # prev为下个图起始结点的索引号
+
+ num_total = max(graph_idx)
+ num_train = int(split_train * num_total)
+ num_val = int((split_train + split_val) * num_total)
+
+ idx_train = range(num_train)
+ idx_val = range(num_train, num_val)
+ idx_test = range(num_val, num_total)
+
+ idx_train = torch.LongTensor(idx_train)
+ idx_val = torch.LongTensor(idx_val)
+ idx_test = torch.LongTensor(idx_test)
+
+
+ print(graph_labels[idx_train])
+ print(graph_labels[idx_val])
+
diff --git a/datas/MUTAG/MUTAG_A.txt b/datas/MUTAG/MUTAG_A.txt
new file mode 100644
index 0000000..b5cabc1
--- /dev/null
+++ b/datas/MUTAG/MUTAG_A.txt
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diff --git a/datas/MUTAG/MUTAG_edge_labels.txt b/datas/MUTAG/MUTAG_edge_labels.txt
new file mode 100644
index 0000000..66a74dc
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+++ b/datas/MUTAG/MUTAG_edge_labels.txt
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diff --git a/datas/MUTAG/MUTAG_graph_indicator.txt b/datas/MUTAG/MUTAG_graph_indicator.txt
new file mode 100644
index 0000000..74f6d67
--- /dev/null
+++ b/datas/MUTAG/MUTAG_graph_indicator.txt
@@ -0,0 +1,3371 @@
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diff --git a/datas/MUTAG/MUTAG_graph_labels.txt b/datas/MUTAG/MUTAG_graph_labels.txt
new file mode 100644
index 0000000..cd9a9c8
--- /dev/null
+++ b/datas/MUTAG/MUTAG_graph_labels.txt
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diff --git a/datas/MUTAG/MUTAG_node_labels.txt b/datas/MUTAG/MUTAG_node_labels.txt
new file mode 100644
index 0000000..cc11ed9
--- /dev/null
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diff --git a/datas/MUTAG/README.txt b/datas/MUTAG/README.txt
new file mode 100644
index 0000000..7c1cafa
--- /dev/null
+++ b/datas/MUTAG/README.txt
@@ -0,0 +1,85 @@
+README for dataset MUTAG
+
+
+=== Usage ===
+
+This folder contains the following comma separated text files
+(replace DS by the name of the dataset):
+
+n = total number of nodes
+m = total number of edges
+N = number of graphs
+
+(1) DS_A.txt (m lines)
+ sparse (block diagonal) adjacency matrix for all graphs,
+ each line corresponds to (row, col) resp. (node_id, node_id)
+
+(2) DS_graph_indicator.txt (n lines)
+ column vector of graph identifiers for all nodes of all graphs,
+ the value in the i-th line is the graph_id of the node with node_id i
+
+(3) DS_graph_labels.txt (N lines)
+ class labels for all graphs in the dataset,
+ the value in the i-th line is the class label of the graph with graph_id i
+
+(4) DS_node_labels.txt (n lines)
+ column vector of node labels,
+ the value in the i-th line corresponds to the node with node_id i
+
+There are OPTIONAL files if the respective information is available:
+
+(5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt)
+ labels for the edges in DD_A_sparse.txt
+
+(6) DS_edge_attributes.txt (m lines; same size as DS_A.txt)
+ attributes for the edges in DS_A.txt
+
+(7) DS_node_attributes.txt (n lines)
+ matrix of node attributes,
+ the comma seperated values in the i-th line is the attribute vector of the node with node_id i
+
+(8) DS_graph_attributes.txt (N lines)
+ regression values for all graphs in the dataset,
+ the value in the i-th line is the attribute of the graph with graph_id i
+
+
+=== Description of the dataset ===
+
+The MUTAG dataset consists of 188 chemical compounds divided into two
+classes according to their mutagenic effect on a bacterium.
+
+The chemical data was obtained form http://cdb.ics.uci.edu and converted
+to graphs, where vertices represent atoms and edges represent chemical
+bonds. Explicit hydrogen atoms have been removed and vertices are labeled
+by atom type and edges by bond type (single, double, triple or aromatic).
+Chemical data was processed using the Chemistry Development Kit (v1.4).
+
+Node labels:
+
+ 0 C
+ 1 N
+ 2 O
+ 3 F
+ 4 I
+ 5 Cl
+ 6 Br
+
+Edge labels:
+
+ 0 aromatic
+ 1 single
+ 2 double
+ 3 triple
+
+
+=== Previous Use of the Dataset ===
+
+Kriege, N., Mutzel, P.: Subgraph matching kernels for attributed graphs. In: Proceedings
+of the 29th International Conference on Machine Learning (ICML-2012) (2012).
+
+
+=== References ===
+
+Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J., and Hansch, C.
+Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds.
+Correlation with molecular orbital energies and hydrophobicity. J. Med. Chem. 34(2):786-797 (1991).
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diff --git a/model/gcn_first.pth b/model/gcn_first.pth
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diff --git a/test.py b/test.py
new file mode 100644
index 0000000..1e42417
--- /dev/null
+++ b/test.py
@@ -0,0 +1,29 @@
+from Load_dataset import load_split_MUTAG_data, accuracy
+from Model import GCN
+import time
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
+
+model_path = 'model/gcn_first.pth'
+
+if __name__ == '__main__':
+ adj_list, features_list, graph_labels, idx_map, idx_train, idx_val, idx_test = load_split_MUTAG_data()
+ model = GCN(nfeat=features_list[0].shape[1], # nfeat = 7
+ nclass=graph_labels.max().item() + 1, # nclass = 2
+ dropout=0.1)
+
+ model.eval()
+ outputs = []
+ for i in idx_test:
+ output = model(features_list[i], adj_list[i])
+ output = output.unsqueeze(0)
+ outputs.append(output)
+ output = torch.cat(outputs, dim=0)
+
+ loss_test = F.cross_entropy(output, graph_labels[idx_test])
+ acc_test = accuracy(output, graph_labels[idx_test])
+ print(loss_test)
+ print(acc_test)
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..3037b75
--- /dev/null
+++ b/train.py
@@ -0,0 +1,118 @@
+from Load_dataset import load_split_MUTAG_data, accuracy
+from Model import GCN
+import time
+
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.nn.functional as F
+
+model_path = 'model/gcn_first.pth'
+
+epochs = 1000
+seed = 200
+lr = 0.001
+dropout = 0.1
+weight_decay = 5e-4
+
+np.random.seed(seed)
+torch.manual_seed(seed)
+torch.cuda.manual_seed(seed)
+
+
+class EarlyStopping():
+ def __init__(self, patience=10, min_loss=0.5, hit_min_before_stopping=False):
+ self.patience = patience
+ self.counter = 0
+ self.hit_min_before_stopping = hit_min_before_stopping
+ if hit_min_before_stopping:
+ self.min_loss = min_loss
+ self.best_loss = None
+ self.early_stop = False
+
+ def __call__(self, loss):
+ if self.best_loss is None:
+ self.best_loss = loss
+ elif loss > self.best_loss:
+ self.counter += 1
+ if self.counter > self.patience:
+ if self.hit_min_before_stopping == True and loss > self.min_loss:
+ print("Cannot hit mean loss, will continue")
+ self.counter -= self.patience
+ else:
+ self.early_stop = True
+ else:
+ self.best_loss = loss
+ counter = 0
+
+
+if __name__ == '__main__':
+ # adj_list: [188, 29, 29]
+ # features_list: [188, 29, 7]
+ # graph_labels: [188]
+ adj_list, features_list, graph_labels, idx_map, idx_train, idx_val, idx_test = load_split_MUTAG_data()
+ idx_train = torch.cat([idx_train, idx_val, idx_test])
+
+ model = GCN(nfeat=features_list[0].shape[1], # nfeat = 7
+ nclass=graph_labels.max().item() + 1, # nclass = 2
+ dropout=dropout)
+ optimizer = optim.Adam(model.parameters(),
+ lr=lr, weight_decay=weight_decay)
+
+ model.cuda()
+ features_list = features_list.cuda()
+ adj_list = adj_list.cuda()
+ graph_labels = graph_labels.cuda()
+ idx_train = idx_train.cuda()
+ idx_val = idx_val.cuda()
+ idx_test = idx_test.cuda()
+
+ # 训练模型
+ early_stopping = EarlyStopping(10, hit_min_before_stopping=True)
+ t_total = time.time()
+
+ for epoch in range(epochs):
+ t = time.time()
+ model.train()
+ optimizer.zero_grad()
+
+ # # Split
+ outputs = []
+ for i in idx_train:
+ output = model(features_list[i], adj_list[i])
+ output = output.unsqueeze(0)
+ outputs.append(output)
+ output = torch.cat(outputs, dim=0)
+
+
+ loss_train = F.cross_entropy(output, graph_labels[idx_train])
+ acc_train = accuracy(output, graph_labels[idx_train])
+ loss_train.backward()
+ optimizer.step()
+
+ model.eval()
+ outputs = []
+ for i in idx_val:
+ output = model(features_list[i], adj_list[i])
+ output = output.unsqueeze(0)
+ outputs.append(output)
+ output = torch.cat(outputs, dim=0)
+ loss_val = F.cross_entropy(output, graph_labels[idx_val])
+ acc_val = accuracy(output, graph_labels[idx_val])
+
+ print('Epoch: {:04d}'.format(epoch + 1),
+ 'loss_train: {:.4f}'.format(loss_train.item()),
+ 'acc_train: {:.4f}'.format(acc_train.item()),
+ 'loss_val: {:.4f}'.format(loss_val.item()),
+ 'acc_val: {:.4f}'.format(acc_val.item()),
+ 'time: {:.4f}s'.format(time.time() - t))
+
+ print(loss_val)
+ early_stopping(loss_val)
+ if early_stopping.early_stop == True:
+ break
+
+ print("Optimization Finished!")
+ print("Total time elapsed: {:.4f}s".format(time.time() - t_total))
+
+ torch.save(model.state_dict(), model_path)