def build_feed_dict(self, roots, sort=True, train=False):
if sort:
roots_size = [tree_util.size_of_tree(root) for root in roots]
roots = helper.sort_by(roots, roots_size)
roots_size = [tree_util.size_of_tree(root) for root in roots]
roots_list, permutation = helper.greedy_bin_packing(roots, roots_size, np.max(roots_size))
node_list_list = []
root_indices = []
internal_nodes_array = []
lstm_prev_list = []
for i, roots in enumerate(roots_list):
node_list = []
root_index = 0
leaf_index = 0
lstm_prev = [0]
lstm_prev_count = 0
for root in roots:
tree_util.depth_first_traverse(root, node_list, lambda node, node_list: node_list.append(node))
leaf_count = tree_util.leafs_in_tree(root)
root_index += leaf_count
root_indices.append([i, root_index])
for j in range(0,leaf_count):
leaf_index += 1
internal_nodes_array.append([i, leaf_index])
leaf_count = tree_util.leafs_in_tree(root)
for x in range(0, leaf_count):
if x == 0:
lstm_prev.append(0)
else:
lstm_prev.append(lstm_prev_count)
lstm_prev_count += 1
node_list_list.append(node_list)
lstm_prev_list.append(lstm_prev)
feed_dict = {
self.dropout_rate: FLAGS.dropout_prob if train else 0,
self.lstm_prev_array: helper.lists_pad(lstm_prev_list, 0),
self.leaf_word_array: helper.lists_pad(
[[0] + [self.word_embed.get_idx(node.value) for node in node_list if node.is_leaf]
for node_list in node_list_list]
,0),
self.loss_array: root_indices if self.use_root_loss else internal_nodes_array,
self.root_array: root_indices,
self.label_array: helper.lists_pad([
[[0, 0]] + [node.label for node in node_list if node.is_leaf]
for node_list in node_list_list], [0, 0])
}
return feed_dict, permutation
def build_feed_dict_batch(self, roots):
print("Batch size:", len(roots))
node_list_list = []
node_to_index_list = []
for root in roots:
node_list = []
tree_util.depth_first_traverse(
root, node_list,
lambda node, node_list: node_list.append(node))
node_list_list.append(node_list)
node_to_index = helper.reverse_dict(node_list)
node_to_index_list.append(node_to_index)
feed_dict = {
self.root_array: [tree_util.size_of_tree(root) for root in roots],
self.is_leaf_array:
helper.lists_pad([[False] + [node.is_leaf for node in node_list]
for node_list in node_list_list], False),
self.word_index_array:
helper.lists_pad([[0] + [
self.data.word_embed_util.get_idx(node.value)
for node in node_list
] for node_list in node_list_list], 0),
self.left_child_array:
helper.lists_pad([[0] + helper.add_one([
node_to_index[node.left_child]
if node.left_child is not None else -1 for node in node_list
])
for node_list, node_to_index in zip(
node_list_list, node_to_index_list)], 0),
self.right_child_array:
helper.lists_pad([[0] + helper.add_one([
node_to_index[node.right_child]
if node.right_child is not None else -1 for node in node_list
])
for node_list, node_to_index in zip(
node_list_list, node_to_index_list)], 0),
self.label_array:
helper.lists_pad([[[0, 0]] + [node.label for node in node_list]
for node_list in node_list_list], [0, 0])
}
print(feed_dict[self.right_child_array])
return feed_dict
def build_feed_dict(self, roots, sort=True, train=False):
if sort:
roots_size = [tree_util.size_of_tree(root) for root in roots]
roots = helper.sort_by(roots, roots_size)
roots_size = [tree_util.size_of_tree(root) for root in roots]
roots_list, permutation = helper.greedy_bin_packing(
roots, roots_size, np.max(roots_size))
node_list_list = []
node_to_index_list = []
root_indices = []
lstm_idx_list = []
internal_nodes_array = []
for i, roots in enumerate(roots_list):
node_list = []
lstm_idx = [0]
root_index = 0
start = 0
for root in roots:
tree_util.depth_first_traverse(
root, node_list,
lambda node, node_list: node_list.append(node))
_, start = tree_util.get_preceding_lstm_index(
root, start, start, lstm_idx)
root_index += tree_util.size_of_tree(root)
root_indices.append([i, root_index])
node_list_list.append(node_list)
node_to_index = helper.reverse_dict(node_list)
node_to_index_list.append(node_to_index)
lstm_idx_list.append(lstm_idx)
for node in node_list:
if not node.is_leaf:
internal_nodes_array.append([i, node_to_index[node] + 1])
internal_nodes_array = internal_nodes_array if len(
internal_nodes_array) > 0 else [[0, 0]]
feed_dict = {
self.dropout_rate:
FLAGS.dropout_prob if train else 0,
self.leaf_word_array:
helper.lists_pad([[0] + [
self.word_embed.get_idx(node.value)
for node in node_list if node.is_leaf
] for node_list in node_list_list], 0),
self.lstm_index_array:
helper.lists_pad(lstm_idx_list, 0),
self.loss_array:
root_indices if self.use_root_loss else internal_nodes_array,
self.root_array:
root_indices,
self.is_leaf_array:
helper.lists_pad(
[[0] + helper.to_int([node.is_leaf for node in node_list])
for node_list in node_list_list], 0),
self.word_index_array:
helper.lists_pad(
[[0] +
[self.word_embed.get_idx(node.value) for node in node_list]
for node_list in node_list_list],
self.word_embed.get_idx("ZERO")),
self.left_child_array:
helper.lists_pad([[0] + helper.add_one([
node_to_index[node.left_child]
if node.left_child is not None else -1 for node in node_list
])
for node_list, node_to_index in zip(
node_list_list, node_to_index_list)], 0),
self.right_child_array:
helper.lists_pad([[0] + helper.add_one([
node_to_index[node.right_child]
if node.right_child is not None else -1 for node in node_list
])
for node_list, node_to_index in zip(
node_list_list, node_to_index_list)], 0),
self.label_array:
helper.lists_pad([[[0, 0]] + [node.label for node in node_list]
for node_list in node_list_list], [0, 0])
}
return feed_dict, permutation