def explain(
self, node_idx, graph_idx=0, graph_mode=False, unconstrained=False, model="exp"
):
"""Explain a single node prediction
"""
# index of the query node in the new adj
if graph_mode:
node_idx_new = node_idx
sub_adj = self.adj[graph_idx]
sub_feat = self.feat[graph_idx, :]
sub_label = self.label[graph_idx]
neighbors = np.asarray(range(self.adj.shape[0]))
else:
print("node label: ", self.label[graph_idx][node_idx])
node_idx_new, sub_adj, sub_feat, sub_label, neighbors = self.extract_neighborhood(
node_idx, graph_idx
)
print("neigh graph idx: ", node_idx, node_idx_new)
sub_label = np.expand_dims(sub_label, axis=0)
sub_adj = np.expand_dims(sub_adj, axis=0)
sub_feat = np.expand_dims(sub_feat, axis=0)
adj = torch.tensor(sub_adj, dtype=torch.float)
x = torch.tensor(sub_feat, requires_grad=True, dtype=torch.float)
label = torch.tensor(sub_label, dtype=torch.long)
if self.graph_mode:
pred_label = np.argmax(self.pred[0][graph_idx], axis=0)
print("Graph predicted label: ", pred_label)
else:
pred_label = np.argmax(self.pred[graph_idx][neighbors], axis=1)
print("Node predicted label: ", pred_label[node_idx_new])
explainer = ExplainModule(
adj=adj,
x=x,
model=self.model,
label=label,
args=self.args,
writer=self.writer,
graph_idx=self.graph_idx,
graph_mode=self.graph_mode,
)
if self.args.gpu:
explainer = explainer.cuda()
self.model.eval()
# gradient baseline
if model == "grad":
explainer.zero_grad()
# pdb.set_trace()
adj_grad = torch.abs(
explainer.adj_feat_grad(node_idx_new, pred_label[node_idx_new])[0]
)[graph_idx]
masked_adj = adj_grad + adj_grad.t()
masked_adj = nn.functional.sigmoid(masked_adj)
masked_adj = masked_adj.cpu().detach().numpy() * sub_adj.squeeze()
else:
explainer.train()
begin_time = time.time()
for epoch in range(self.args.num_epochs):
explainer.zero_grad()
explainer.optimizer.zero_grad()
ypred, adj_atts = explainer(node_idx_new, unconstrained=unconstrained)
loss = explainer.loss(ypred, pred_label, node_idx_new, epoch)
loss.backward()
explainer.optimizer.step()
if explainer.scheduler is not None:
explainer.scheduler.step()
mask_density = explainer.mask_density()
if self.print_training:
print(
"epoch: ",
epoch,
"; loss: ",
loss.item(),
"; mask density: ",
mask_density.item(),
"; pred: ",
ypred,
)
single_subgraph_label = sub_label.squeeze()
if self.writer is not None:
self.writer.add_scalar("mask/density", mask_density, epoch)
self.writer.add_scalar(
"optimization/lr",
explainer.optimizer.param_groups[0]["lr"],
epoch,
)
if epoch % 25 == 0:
explainer.log_mask(epoch)
explainer.log_masked_adj(
node_idx_new, epoch, label=single_subgraph_label
)
explainer.log_adj_grad(
node_idx_new, pred_label, epoch, label=single_subgraph_label
)
if epoch == 0:
if self.model.att:
# explain node
print("adj att size: ", adj_atts.size())
adj_att = torch.sum(adj_atts[0], dim=2)
# adj_att = adj_att[neighbors][:, neighbors]
node_adj_att = adj_att * adj.float().cuda()
io_utils.log_matrix(
self.writer, node_adj_att[0], "att/matrix", epoch
)
node_adj_att = node_adj_att[0].cpu().detach().numpy()
G = io_utils.denoise_graph(
node_adj_att,
node_idx_new,
threshold=3.8, # threshold_num=20,
max_component=True,
)
io_utils.log_graph(
self.writer,
G,
name="att/graph",
identify_self=not self.graph_mode,
nodecolor="label",
edge_vmax=None,
args=self.args,
)
if model != "exp":
break
print("finished training in ", time.time() - begin_time)
if model == "exp":
masked_adj = (
explainer.masked_adj[0].cpu().detach().numpy() * sub_adj.squeeze()
)
else:
adj_atts = nn.functional.sigmoid(adj_atts).squeeze()
masked_adj = adj_atts.cpu().detach().numpy() * sub_adj.squeeze()
fname = 'masked_adj_' + io_utils.gen_explainer_prefix(self.args) + (
'node_idx_'+str(node_idx)+'graph_idx_'+str(self.graph_idx)+'.npy')
with open(os.path.join(self.args.logdir, fname), 'wb') as outfile:
np.save(outfile, np.asarray(masked_adj.copy()))
print("Saved adjacency matrix to ", fname)
return masked_adj
def main():
# Load a configuration
prog_args = arg_parse()
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# Configure the logging directory
if prog_args.writer:
path = os.path.join(prog_args.logdir,
io_utils.gen_explainer_prefix(prog_args))
if os.path.isdir(path) and prog_args.clean_log:
print('Removing existing log dir: ', path)
if not input(
"Are you sure you want to remove this directory? (y/n): "
).lower().strip()[:1] == "y":
sys.exit(1)
shutil.rmtree(path)
writer = SummaryWriter(path)
else:
writer = None
# Load data and a model checkpoint
ckpt = io_utils.load_ckpt(prog_args)
cg_dict = ckpt["cg"] # get computation graph
input_dim = cg_dict["feat"].shape[2]
num_classes = cg_dict["pred"].shape[2]
print("Loaded model from {}".format(prog_args.ckptdir))
print("input dim: ", input_dim, "; num classes: ", num_classes)
# Determine explainer mode (node classif)
graph_mode = (prog_args.graph_mode or prog_args.multigraph_class >= 0
or prog_args.graph_idx >= 0)
# build model
print("Method: ", prog_args.method)
if graph_mode:
# Explain Graph prediction
model = models.GcnEncoderGraph(
input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args,
)
else:
if prog_args.dataset == "ppi_essential":
# class weight in CE loss for handling imbalanced label classes
prog_args.loss_weight = torch.tensor([1.0, 5.0],
dtype=torch.float).cuda()
# Explain Node prediction
model = models.GcnEncoderNode(
input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args,
)
if prog_args.gpu:
model = model.cuda()
# Load state_dict (obtained by model.state_dict() when saving checkpoint)
model.load_state_dict(ckpt["model_state"])
# Convertion data required to get correct model output for GraphSHAP
adj = torch.tensor(cg_dict["adj"], dtype=torch.float)
x = torch.tensor(cg_dict["feat"], requires_grad=True, dtype=torch.float)
if prog_args.gpu:
y_pred, att_adj = model(x.cuda(), adj.cuda())
else:
y_pred, att_adj = model(x, adj)
# Transform their data into our format
data = transform_data(adj, x, cg_dict["label"][0].tolist())
# Generate test nodes
# Use only these specific nodes as they are the ones added manually, part of the defined shapes
# node_indices = extract_test_nodes(data, num_samples=10, cg_dict['train_idx'])
k = 4 # number of nodes for the shape introduced (house, cycle)
K = 0
if prog_args.dataset == 'syn1':
node_indices = list(range(400, 410, 5))
elif prog_args.dataset == 'syn2':
node_indices = list(range(400, 405, 5)) + list(range(1100, 1105, 5))
elif prog_args.dataset == 'syn4':
node_indices = list(range(511, 523, 6))
if prog_args.hops == 3:
k = 5
else:
K = 5
elif prog_args.dataset == 'syn5':
node_indices = list(range(511, 529, 9))
if prog_args.hops == 3:
k = 7
K = 8
else:
k = 5
K = 8
# GraphSHAP explainer
# graphshap = GraphSHAP(data, model, adj, writer, prog_args.dataset, prog_args.gpu)
# Run GNN Explainer and retrieve produced explanations
gnne = explain.Explainer(
model=model,
adj=cg_dict["adj"],
feat=cg_dict["feat"],
label=cg_dict["label"],
pred=cg_dict["pred"],
train_idx=cg_dict["train_idx"],
args=prog_args,
writer=writer,
print_training=True,
graph_mode=graph_mode,
graph_idx=prog_args.graph_idx,
)
### GNNE
# Explain a set of nodes - accuracy on edges this time
t = time.time()
gnne_edge_accuracy, gnne_auc, gnne_node_accuracy, important_nodes_gnne =\
gnne.explain_nodes_gnn_stats(
node_indices, prog_args
)
e = time.time()
print('Time: ', e - t)
def main():
# Load a configuration
prog_args = arg_parse()
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# Configure the logging directory
if prog_args.writer:
path = os.path.join(prog_args.logdir,
io_utils.gen_explainer_prefix(prog_args))
if os.path.isdir(path) and prog_args.clean_log:
print('Removing existing log dir: ', path)
if not input(
"Are you sure you want to remove this directory? (y/n): "
).lower().strip()[:1] == "y":
sys.exit(1)
shutil.rmtree(path)
writer = SummaryWriter(path)
else:
writer = None
# Load data and a model checkpoint
ckpt = io_utils.load_ckpt(prog_args)
cg_dict = ckpt["cg"] # get computation graph
input_dim = cg_dict["feat"].shape[2]
num_classes = cg_dict["pred"].shape[2]
print("Loaded model from {}".format(prog_args.ckptdir))
print("input dim: ", input_dim, "; num classes: ", num_classes)
# Determine explainer mode (node classif)
graph_mode = (prog_args.graph_mode or prog_args.multigraph_class >= 0
or prog_args.graph_idx >= 0)
# build model
print("Method: ", prog_args.method)
if graph_mode:
# Explain Graph prediction
model = models.GcnEncoderGraph(
input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args,
)
else:
if prog_args.dataset == "ppi_essential":
# class weight in CE loss for handling imbalanced label classes
prog_args.loss_weight = torch.tensor([1.0, 5.0],
dtype=torch.float).cuda()
# Explain Node prediction
model = models.GcnEncoderNode(
input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args,
)
if prog_args.gpu:
model = model.cuda()
# Load state_dict (obtained by model.state_dict() when saving checkpoint)
model.load_state_dict(ckpt["model_state"])
# Convertion data required to get correct model output for GraphSHAP
adj = torch.tensor(cg_dict["adj"], dtype=torch.float)
x = torch.tensor(cg_dict["feat"], requires_grad=True, dtype=torch.float)
if prog_args.gpu:
y_pred, att_adj = model(x.cuda(), adj.cuda())
else:
y_pred, att_adj = model(x, adj)
# Transform their data into our format
data = transform_data(adj, x, cg_dict["label"][0].tolist())
# Generate test nodes
# Use only these specific nodes as they are the ones added manually, part of the defined shapes
# node_indices = extract_test_nodes(data, num_samples=10, cg_dict['train_idx'])
k = 4 # number of nodes for the shape introduced (house, cycle)
K = 0
if prog_args.dataset == 'syn1':
node_indices = list(range(400, 450, 5))
elif prog_args.dataset == 'syn2':
node_indices = list(range(400, 425, 5)) + list(range(1100, 1125, 5))
elif prog_args.dataset == 'syn4':
node_indices = list(range(511, 571, 6))
if prog_args.hops == 3:
k = 5
else:
K = 5
elif prog_args.dataset == 'syn5':
node_indices = list(range(511, 601, 9))
if prog_args.hops == 3:
k = 8
else:
k = 5
K = 8
# GraphSHAP explainer
graphshap = GraphSHAP(data, model, adj, writer, prog_args.dataset,
prog_args.gpu)
# Run GNN Explainer and retrieve produced explanations
gnne = explain.Explainer(
model=model,
adj=cg_dict["adj"],
feat=cg_dict["feat"],
label=cg_dict["label"],
pred=cg_dict["pred"],
train_idx=cg_dict["train_idx"],
args=prog_args,
writer=writer,
print_training=True,
graph_mode=graph_mode,
graph_idx=prog_args.graph_idx,
)
#if prog_args.explain_node is not None:
# _, gnne_edge_accuracy, gnne_auc, gnne_node_accuracy = \
# gnne.explain_nodes_gnn_stats(
# node_indices, prog_args
# )
# elif graph_mode:
# # Graph explanation
# gnne_expl = gnne.explain_graphs([1])[0]
# GraphSHAP - assess accuracy of explanations
# Loop over test nodes
accuracy = []
feat_accuracy = []
for node_idx in node_indices:
start = time.time()
graphshap_explanations = graphshap.explain(
[node_idx],
prog_args.hops,
prog_args.num_samples,
prog_args.info,
prog_args.multiclass,
prog_args.fullempty,
prog_args.S,
prog_args.hv,
prog_args.feat,
prog_args.coal,
prog_args.g,
prog_args.regu,
)[0]
end = time.time()
print('GS Time:', end - start)
# Predicted class
pred_val, predicted_class = y_pred[0, node_idx, :].max(dim=0)
# Keep only node explanations
# ,predicted_class]
graphshap_node_explanations = graphshap_explanations[graphshap.F:]
# Derive ground truth from graph structure
ground_truth = list(range(node_idx + 1, node_idx + max(k, K) + 1))
# Retrieve top k elements indices form graphshap_node_explanations
if graphshap.neighbours.shape[0] > k:
i = 0
val, indices = torch.topk(
torch.tensor(graphshap_node_explanations.T), k + 1)
# could weight importance based on val
for node in graphshap.neighbours[indices]:
if node.item() in ground_truth:
i += 1
# Sort of accruacy metric
accuracy.append(i / k)
print('There are {} from targeted shape among most imp. nodes'.
format(i))
# Look at importance distribution among features
# Identify most important features and check if it corresponds to truly imp ones
if prog_args.dataset == 'syn2':
# ,predicted_class]
graphshap_feat_explanations = graphshap_explanations[:graphshap.F]
print('Feature importance graphshap',
graphshap_feat_explanations.T)
if np.argsort(graphshap_feat_explanations)[-1] == 0:
feat_accuracy.append(1)
else:
feat_accuracy.append(0)
# Metric for graphshap
final_accuracy = sum(accuracy) / len(accuracy)
### GNNE
# Explain a set of nodes - accuracy on edges this time
_, gnne_edge_accuracy, gnne_auc, gnne_node_accuracy =\
gnne.explain_nodes_gnn_stats(
node_indices, prog_args
)
### GRAD benchmark
# MetricS to assess quality of predictionsx
"""
_, grad_edge_accuracy, grad_auc, grad_node_accuracy =\
gnne.explain_nodes_gnn_stats(
node_indices, prog_args, model="grad")
"""
grad_edge_accuracy = 0
grad_node_accuracy = 0
### GAT
# Nothing for now - implem a GAT on the side and look at weights coef
### Results
print(
'Accuracy for GraphSHAP is {:.2f} vs {:.2f},{:.2f} for GNNE vs {:.2f},{:.2f} for GRAD'
.format(final_accuracy, np.mean(gnne_edge_accuracy),
np.mean(gnne_node_accuracy), np.mean(grad_edge_accuracy),
np.mean(grad_node_accuracy)))
if prog_args.dataset == 'syn2':
print('Most important feature was found in {:.2f}% of the case'.format(
100 * np.mean(feat_accuracy)))
print('GNNE_auc is:', gnne_auc)
def main():
# Load a configuration
prog_args = arg_parse()
if prog_args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = prog_args.cuda
print("CUDA", prog_args.cuda)
else:
print("Using CPU")
# Configure the logging directory
if prog_args.writer:
path = os.path.join(prog_args.logdir,
io_utils.gen_explainer_prefix(prog_args))
if os.path.isdir(path) and prog_args.clean_log:
print('Removing existing log dir: ', path)
if not input(
"Are you sure you want to remove this directory? (y/n): "
).lower().strip()[:1] == "y":
sys.exit(1)
shutil.rmtree(path)
writer = SummaryWriter(path)
else:
writer = None
# Load a model checkpoint
ckpt = io_utils.load_ckpt(prog_args)
cg_dict = ckpt["cg"] # get computation graph
input_dim = cg_dict["feat"].shape[2]
num_classes = cg_dict["pred"].shape[2]
print("Loaded model from {}".format(prog_args.ckptdir))
print("input dim: ", input_dim, "; num classes: ", num_classes)
# Determine explainer mode
graph_mode = (prog_args.graph_mode or prog_args.multigraph_class >= 0
or prog_args.graph_idx >= 0)
# build model
print("Method: ", prog_args.method)
if graph_mode:
# Explain Graph prediction
model = models.GcnEncoderGraph(
input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args,
)
else:
if prog_args.dataset == "ppi_essential":
# class weight in CE loss for handling imbalanced label classes
prog_args.loss_weight = torch.tensor([1.0, 5.0],
dtype=torch.float).cuda()
# Explain Node prediction
model = models.GcnEncoderNode(
input_dim=input_dim,
hidden_dim=prog_args.hidden_dim,
embedding_dim=prog_args.output_dim,
label_dim=num_classes,
num_layers=prog_args.num_gc_layers,
bn=prog_args.bn,
args=prog_args,
)
if prog_args.gpu:
model = model.cuda()
# load state_dict (obtained by model.state_dict() when saving checkpoint)
model.load_state_dict(ckpt["model_state"])
# Create explainer
explainer = explain.Explainer(
model=model,
adj=cg_dict["adj"],
feat=cg_dict["feat"],
label=cg_dict["label"],
pred=cg_dict["pred"],
train_idx=cg_dict["train_idx"],
args=prog_args,
writer=writer,
print_training=True,
graph_mode=graph_mode,
graph_idx=prog_args.graph_idx,
)
# TODO: API should definitely be cleaner
# Let's define exactly which modes we support
# We could even move each mode to a different method (even file)
if prog_args.explain_node is not None:
explainer.explain(prog_args.explain_node, unconstrained=False)
elif graph_mode:
if prog_args.multigraph_class >= 0:
print(cg_dict["label"])
# only run for graphs with label specified by multigraph_class
labels = cg_dict["label"].numpy()
graph_indices = []
for i, l in enumerate(labels):
if l == prog_args.multigraph_class:
graph_indices.append(i)
if len(graph_indices) > 30:
break
print(
"Graph indices for label ",
prog_args.multigraph_class,
" : ",
graph_indices,
)
explainer.explain_graphs(graph_indices=graph_indices)
elif prog_args.graph_idx == -1:
# just run for a customized set of indices
explainer.explain_graphs(graph_indices=[1, 2, 3, 4])
else:
explainer.explain(
node_idx=0,
graph_idx=prog_args.graph_idx,
graph_mode=True,
unconstrained=False,
)
io_utils.plot_cmap_tb(writer, "tab20", 20, "tab20_cmap")
else:
if prog_args.multinode_class >= 0:
print(cg_dict["label"])
# only run for nodes with label specified by multinode_class
labels = cg_dict["label"][0] # already numpy matrix
node_indices = []
for i, l in enumerate(labels):
if len(node_indices) > 4:
break
if l == prog_args.multinode_class:
node_indices.append(i)
print(
"Node indices for label ",
prog_args.multinode_class,
" : ",
node_indices,
)
explainer.explain_nodes(node_indices, prog_args)
else:
# explain a set of nodes
masked_adj = explainer.explain_nodes_gnn_stats(
range(400, 700, 5), prog_args)
def explain(self,
node_idx,
graph_idx=0,
graph_mode=False,
unconstrained=False,
exp_model="exp"):
print('************** Explaining node : {} **************'.format(
node_idx))
print('The label for graph index {} and node index {} : {}'.format(
graph_idx, node_idx, self.label[graph_idx][node_idx]))
print("Labels of all the nodes :\n", self.label)
# Adjacency matrix of entire graph
print("Shape of retrieved neighborhoods :", self.neighborhoods.shape)
print("No. of neighborhoods :",
len(self.neighborhoods[graph_idx][node_idx]))
print(
'List of neighborhoods for explaining node {} :'.format(node_idx))
print(self.neighborhoods[graph_idx][node_idx])
# index of the query node in the new adj
if graph_mode:
node_idx_new = node_idx
sub_adj = self.adj[graph_idx]
sub_feat = self.feat[graph_idx, :]
sub_label = self.label[graph_idx]
neighbors = np.asarray(range(self.adj.shape[0]))
else:
print("Ground truth, node label :",
self.label[graph_idx][node_idx])
# Computational graph :
# Extracting subgraph adjacency matrix, subgraph features, subgraph labels and the nodes neighbours
node_idx_new, sub_adj, sub_feat, sub_label, neighbors = self.extract_neighborhood(
node_idx, graph_idx)
sub_label = np.expand_dims(sub_label, axis=0)
sub_adj = np.expand_dims(sub_adj, axis=0)
sub_feat = np.expand_dims(sub_feat, axis=0)
print("Neighbouring graph index for node " + str(node_idx) +
" with new node index " + str(node_idx_new))
#print("Expand dimension of Subgraph adjacency :\n", sub_adj)
#print("Expand dimension of Subgraph features :\n", sub_feat)
print("Expand dimension of Subgraph label :\n", sub_label)
# All the nodes in the graph (eg. indexes from 0 to 34)
print("Subgraph neighbors :\n", neighbors)
tensor_adj = torch.tensor(sub_adj, dtype=torch.float)
tensor_x = torch.tensor(sub_feat,
requires_grad=True,
dtype=torch.float)
tensor_label = torch.tensor(sub_label, dtype=torch.long)
if self.graph_mode:
pred_label = np.argmax(self.pred[0][graph_idx], axis=0)
print("Graph predicted label: ", pred_label)
else:
pred_label = np.argmax(self.pred[graph_idx][neighbors], axis=1)
print("Neighbours of predicted node labels :",
self.pred[graph_idx][neighbors])
print(
'Predicted labels for all {} neighbours (includes itself) :\n{}'
.format(len(pred_label), pred_label))
print('Predicted label for node {} : {}'.format(
node_idx, pred_label[node_idx_new]))
# Have to use the tensor version of adj for Tensor computation
explainerMod = ExplainModule(
adj=tensor_adj, # adj
x=tensor_x, # x
model=self.model, # model
label=tensor_label, # label
args=self.args, # prog_args
writer=self.writer, # None
graph_idx=self.graph_idx, # graph_idx
graph_mode=self.graph_mode # graph_mode
)
self.model.eval()
explainerMod.train()
begin_time = time.time()
# prog_args.num_epochs
for epoch in range(self.args.num_epochs):
explainerMod.zero_grad()
explainerMod.optimizer.zero_grad()
# node_idx_new is passed to explainerMod.forward to training with the new index
ypred, adj_atts = explainerMod(node_idx_new,
unconstrained=unconstrained)
loss = explainerMod.loss(ypred, pred_label, node_idx_new, epoch)
loss.backward()
explainerMod.optimizer.step()
mask_density = explainerMod.mask_density()
print("epoch: ", epoch, "; loss: ", loss.item(),
"; mask density: ", mask_density.item(), "; pred: ", ypred)
print(
"------------------------------------------------------------------"
)
if exp_model != "exp":
break
print("\n--------------------------------------------")
print("Final ypred after training : ", ypred)
print("pred_label : ", pred_label)
print("node_idx_new : ", node_idx_new)
print("Completed training in ", time.time() - begin_time)
if exp_model == "exp":
masked_adj = (explainerMod.masked_adj[0].cpu().detach().numpy() *
sub_adj.squeeze())
# Added for plotting node explanation subgraph
# explainerMod.mask.shape, masked_edges.shape
masked_edges = explainerMod.mask.cpu().detach().numpy()
masked_features = explainerMod.feat_mask.cpu().detach().numpy()
# explainerMod.feat_mask.shape, masked_features.shape
ypred_detach = ypred.cpu().detach().numpy()
ypred_node = np.argmax(ypred_detach, axis=0) # labels
# ypred = tensor([0.0119, 0.6456, 0.3307, 0.0118]
print('Detach ypred : {} and Argmax node : {}'.format(
ypred_detach, ypred_node))
# Trained masked, edges and features adjacency matrices
print("Shape of masked adjacency matrix : ", masked_adj.shape)
print("The masked adjacency matrix at index [0] :\n", masked_adj[0])
print("Shape of masked edges matrix : ", masked_edges.shape)
print("The masked edges adjacency matrix at index [0] :\n",
masked_edges[0])
print("Shape of masked features matrix : ", masked_features.shape)
print("The masked features adjacency matrix at index [0] :\n",
masked_features[0])
fname = 'masked_adj_' + io_utils.gen_explainer_prefix(
self.args) + ('_node_idx_' + str(node_idx) + '_graph_idx_' +
str(self.graph_idx) + '.npy')
with open(os.path.join(self.args.logdir, fname), 'wb') as outfile:
np.save(outfile, np.asarray(masked_adj.copy()))
print("Saved adjacency matrix to \"" + fname + "\".")
# PlotSubGraph (sub_edge_index not used)
self.PlotSubGraph(masked_adj,
masked_edges,
node_idx_new,
node_idx,
feats=sub_feat.squeeze(),
labels=tensor_label.cpu().detach().numpy().squeeze(),
threshold_num=12,
adj_mode=True)
# Shape of masked adjacency matrix : (27, 27)
# Shape of masked edges matrix : (27, 27)
# Shape of masked features matrix : (10,)
return masked_adj, masked_edges, masked_features
