def train_sequence(self, index, batch_size=np.inf):
mask = T.indices2mask(index, self.graph.n_nodes)
index = get_indice_graph(self.structure_inputs, index, batch_size)
while index.size < self.K:
index = get_indice_graph(self.structure_inputs, index)
structure_inputs = self.structure_inputs[index][:, index]
feature_inputs = self.feature_inputs[index]
mask = mask[index]
labels = self.graph.labels[index[mask]]
sequence = FullBatchNodeSequence(
[feature_inputs, structure_inputs, mask],
labels,
device=self.device)
return sequence
def predict(self, index):
mask = T.indices2mask(index, self.graph.n_nodes)
orders_dict = {idx: order for order, idx in enumerate(index)}
batch_idx, orders = [], []
batch_x, batch_adj = [], []
for cluster in range(self.n_clusters):
nodes = self.cluster_member[cluster]
mini_mask = mask[nodes]
batch_nodes = np.asarray(nodes)[mini_mask]
if batch_nodes.size == 0:
continue
batch_x.append(self.batch_x[cluster])
batch_adj.append(self.batch_adj[cluster])
batch_idx.append(np.where(mini_mask)[0])
orders.append([orders_dict[n] for n in batch_nodes])
batch_data = tuple(zip(batch_x, batch_adj, batch_idx))
logit = np.zeros((index.size, self.graph.n_classes), dtype=self.floatx)
batch_data = T.astensors(batch_data, device=self.device)
model = self.model
if self.kind == "P":
model.eval()
with torch.no_grad():
for order, inputs in zip(orders, batch_data):
output = model(inputs).detach().cpu().numpy()
logit[order] = output
else:
with tf.device(self.device):
for order, inputs in zip(orders, batch_data):
output = model.predict_on_batch(inputs)
logit[order] = output
return logit
def train_sequence(self, index):
mask = T.indices2mask(index, self.graph.n_nodes)
labels = self.graph.labels
batch_idx, batch_labels = [], []
batch_x, batch_adj = [], []
for cluster in range(self.n_clusters):
nodes = self.cluster_member[cluster]
mini_mask = mask[nodes]
mini_labels = labels[nodes][mini_mask]
if mini_labels.size == 0:
continue
batch_x.append(self.batch_x[cluster])
batch_adj.append(self.batch_adj[cluster])
batch_idx.append(np.where(mini_mask)[0])
batch_labels.append(mini_labels)
batch_data = tuple(zip(batch_x, batch_adj, batch_idx))
sequence = MiniBatchSequence(batch_data,
batch_labels,
device=self.device)
return sequence
