def train_sequence(self, index):
mask = gf.indices2mask(index, self.graph.num_nodes)
labels = self.graph.node_label
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
def train_sequence(self, index):
node_mask = gf.index_to_mask(index, self.graph.num_nodes)
labels = self.graph.node_label
cache = self.cache
batch_mask, batch_y = [], []
batch_x, batch_adj = [], []
for cluster in range(self.cfg.process.num_clusters):
nodes = cache.cluster_member[cluster]
mask = node_mask[nodes]
y = labels[nodes][mask]
if y.size == 0:
continue
batch_x.append(cache.batch_x[cluster])
batch_adj.append(cache.batch_adj[cluster])
batch_mask.append(mask)
batch_y.append(y)
batch_inputs = tuple(zip(batch_x, batch_adj))
sequence = MiniBatchSequence(batch_inputs,
batch_y,
out_weight=batch_mask,
device=self.device)
return sequence