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G2S_data_stream.py
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G2S_data_stream.py
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import json
import re
import numpy as np
import random
import padding_utils
import amr_utils
def read_text_file(text_file):
lines = []
with open(text_file, "rt") as f:
for line in f:
line = line.decode('utf-8')
lines.append(line.strip())
return lines
def read_amr_file(inpath):
nodes = [] # [batch, node_num,]
in_neigh_indices = [] # [batch, node_num, neighbor_num,]
in_neigh_edges = []
out_neigh_indices = [] # [batch, node_num, neighbor_num,]
out_neigh_edges = []
sentences = [] # [batch, sent_length,]
ids = []
max_in_neigh = 0
max_out_neigh = 0
max_node = 0
max_sent = 0
with open(inpath, "rU") as f:
for inst in json.load(f):
amr = inst['amr']
sent = inst['sent'].strip().split()
id = inst['id'] if inst.has_key('id') else None
amr_node = []
amr_edge = []
amr_utils.read_anonymized(amr.strip().split(), amr_node, amr_edge)
# 1.
nodes.append(amr_node)
# 2. & 3.
in_indices = [[i,] for i, x in enumerate(amr_node)]
in_edges = [[':self',] for i, x in enumerate(amr_node)]
out_indices = [[i,] for i, x in enumerate(amr_node)]
out_edges = [[':self',] for i, x in enumerate(amr_node)]
for (i,j,lb) in amr_edge:
in_indices[j].append(i)
in_edges[j].append(lb)
out_indices[i].append(j)
out_edges[i].append(lb)
in_neigh_indices.append(in_indices)
in_neigh_edges.append(in_edges)
out_neigh_indices.append(out_indices)
out_neigh_edges.append(out_edges)
# 4.
sentences.append(sent)
ids.append(id)
# update lengths
max_in_neigh = max(max_in_neigh, max(len(x) for x in in_indices))
max_out_neigh = max(max_out_neigh, max(len(x) for x in out_indices))
max_node = max(max_node, len(amr_node))
max_sent = max(max_sent, len(sent))
return zip(nodes, in_neigh_indices, in_neigh_edges, out_neigh_indices, out_neigh_edges, sentences, ids), \
max_node, max_in_neigh, max_out_neigh, max_sent
def read_amr_from_fof(fofpath):
all_paths = read_text_file(fofpath)
all_instances = []
max_node = 0
max_in_neigh = 0
max_out_neigh = 0
max_sent = 0
for cur_path in all_paths:
print(cur_path)
cur_instances, cur_node, cur_in_neigh, cur_out_neigh, cur_sent = read_amr_file(cur_path)
all_instances.extend(cur_instances)
max_node = max(max_node, cur_node)
max_in_neigh = max(max_in_neigh, cur_in_neigh)
max_out_neigh = max(max_out_neigh, cur_out_neigh)
max_sent = max(max_sent, cur_sent)
return all_instances, max_node, max_in_neigh, max_out_neigh, max_sent
def collect_vocabs(all_instances):
all_words = set()
all_chars = set()
all_edgelabels = set()
# nodes: [corpus_size,node_num,]
# neigh_indices & neigh_edges: [corpus_size,node_num,neigh_num,]
# sentence: [corpus_size,sent_len,]
for (nodes, in_neigh_indices, in_neigh_edges, out_neigh_indices, out_neigh_edges, sentence, id) in all_instances:
all_words.update(nodes)
all_words.update(sentence)
for edges in in_neigh_edges:
all_edgelabels.update(edges)
for edges in out_neigh_edges:
all_edgelabels.update(edges)
for w in all_words:
all_chars.update(w)
return (all_words, all_chars, all_edgelabels)
class G2SDataStream(object):
def __init__(self, all_instances, word_vocab=None, char_vocab=None, edgelabel_vocab=None, options=None,
isShuffle=False, isLoop=False, isSort=True, batch_size=-1):
self.options = options
if batch_size ==-1: batch_size=options.batch_size
# index tokens and filter the dataset
instances = []
for (nodes, in_neigh_indices, in_neigh_edges, out_neigh_indices, out_neigh_edges, sentence, id) in all_instances:
if options.max_node_num != -1 and len(nodes) > options.max_node_num:
continue # remove very long passages
in_neigh_indices = [x[:options.max_in_neigh_num] for x in in_neigh_indices]
in_neigh_edges = [x[:options.max_in_neigh_num] for x in in_neigh_edges]
out_neigh_indices = [x[:options.max_out_neigh_num] for x in out_neigh_indices]
out_neigh_edges = [x[:options.max_out_neigh_num] for x in out_neigh_edges]
nodes_idx = word_vocab.to_index_sequence_for_list(nodes)
nodes_chars_idx = None
if options.with_char:
nodes_chars_idx = char_vocab.to_character_matrix_for_list(nodes, max_char_per_word=options.max_char_per_word)
in_neigh_edges_idx = [edgelabel_vocab.to_index_sequence_for_list(edges) for edges in in_neigh_edges]
out_neigh_edges_idx = [edgelabel_vocab.to_index_sequence_for_list(edges) for edges in out_neigh_edges]
sentence_idx = word_vocab.to_index_sequence_for_list(sentence[:options.max_answer_len])
instances.append((nodes_idx, nodes_chars_idx,
in_neigh_indices, in_neigh_edges_idx, out_neigh_indices, out_neigh_edges_idx, sentence_idx, sentence, id))
all_instances = instances
instances = None
# sort instances based on length
if isSort:
all_instances = sorted(all_instances, key=lambda inst: (len(inst[0]), len(inst[-2])))
self.num_instances = len(all_instances)
# distribute questions into different buckets
batch_spans = padding_utils.make_batches(self.num_instances, batch_size)
self.batches = []
for batch_index, (batch_start, batch_end) in enumerate(batch_spans):
cur_instances = []
for i in xrange(batch_start, batch_end):
cur_instances.append(all_instances[i])
cur_batch = G2SBatch(cur_instances, options, word_vocab=word_vocab)
self.batches.append(cur_batch)
self.num_batch = len(self.batches)
self.index_array = np.arange(self.num_batch)
self.isShuffle = isShuffle
if self.isShuffle: np.random.shuffle(self.index_array)
self.isLoop = isLoop
self.cur_pointer = 0
def nextBatch(self):
if self.cur_pointer>=self.num_batch:
if not self.isLoop: return None
self.cur_pointer = 0
if self.isShuffle: np.random.shuffle(self.index_array)
cur_batch = self.batches[self.index_array[self.cur_pointer]]
self.cur_pointer += 1
return cur_batch
def reset(self):
if self.isShuffle: np.random.shuffle(self.index_array)
self.cur_pointer = 0
def get_num_batch(self):
return self.num_batch
def get_num_instance(self):
return self.num_instances
def get_batch(self, i):
if i>= self.num_batch: return None
return self.batches[i]
class G2SBatch(object):
def __init__(self, instances, options, word_vocab=None):
self.options = options
self.amr_node = [x[0] for x in instances]
self.id = [x[-1] for x in instances]
self.target_ref = [x[-2] for x in instances] # list of tuples
self.batch_size = len(instances)
self.vocab = word_vocab
# create length
self.node_num = [] # [batch_size]
self.sent_len = [] # [batch_size]
for (nodes_idx, nodes_chars_idx, in_neigh_indices, in_neigh_edges_idx, out_neigh_indices, out_neigh_edges_idx,
sentence_idx, sentence, id) in instances:
self.node_num.append(len(nodes_idx))
self.sent_len.append(min(len(sentence_idx)+1, options.max_answer_len))
self.node_num = np.array(self.node_num, dtype=np.int32)
self.sent_len = np.array(self.sent_len, dtype=np.int32)
# node char num
if options.with_char:
self.nodes_chars_num = [[len(nodes_chars_idx) for nodes_chars_idx in instance[1]] for instance in instances]
self.nodes_chars_num = padding_utils.pad_2d_vals_no_size(self.nodes_chars_num)
# neigh mask
self.in_neigh_mask = [] # [batch_size, node_num, neigh_num]
self.out_neigh_mask = []
for instance in instances:
ins = []
for in_neighs in instance[2]:
ins.append([1 for _ in in_neighs])
self.in_neigh_mask.append(ins)
outs = []
for out_neighs in instance[4]:
outs.append([1 for _ in out_neighs])
self.out_neigh_mask.append(outs)
self.in_neigh_mask = padding_utils.pad_3d_vals_no_size(self.in_neigh_mask)
self.out_neigh_mask = padding_utils.pad_3d_vals_no_size(self.out_neigh_mask)
# create word representation
start_id = word_vocab.getIndex('<s>')
end_id = word_vocab.getIndex('</s>')
self.nodes = [x[0] for x in instances]
if options.with_char:
self.nodes_chars = [inst[1] for inst in instances] # [batch_size, sent_len, char_num]
self.in_neigh_indices = [x[2] for x in instances]
self.in_neigh_edges = [x[3] for x in instances]
self.out_neigh_indices = [x[4] for x in instances]
self.out_neigh_edges = [x[5] for x in instances]
self.sent_inp = []
self.sent_out = []
for _, _, _, _, _, _, sentence_idx, sentence, id in instances:
if len(sentence_idx) < options.max_answer_len:
self.sent_inp.append([start_id,]+sentence_idx)
self.sent_out.append(sentence_idx+[end_id,])
else:
self.sent_inp.append([start_id,]+sentence_idx[:-1])
self.sent_out.append(sentence_idx)
# making ndarray
self.nodes = padding_utils.pad_2d_vals_no_size(self.nodes)
if options.with_char:
self.nodes_chars = padding_utils.pad_3d_vals_no_size(self.nodes_chars)
self.in_neigh_indices = padding_utils.pad_3d_vals_no_size(self.in_neigh_indices)
self.in_neigh_edges = padding_utils.pad_3d_vals_no_size(self.in_neigh_edges)
self.out_neigh_indices = padding_utils.pad_3d_vals_no_size(self.out_neigh_indices)
self.out_neigh_edges = padding_utils.pad_3d_vals_no_size(self.out_neigh_edges)
assert self.in_neigh_mask.shape == self.in_neigh_indices.shape
assert self.in_neigh_mask.shape == self.in_neigh_edges.shape
assert self.out_neigh_mask.shape == self.out_neigh_indices.shape
assert self.out_neigh_mask.shape == self.out_neigh_edges.shape
# [batch_size, sent_len_max]
self.sent_inp = padding_utils.pad_2d_vals(self.sent_inp, len(self.sent_inp), options.max_answer_len)
self.sent_out = padding_utils.pad_2d_vals(self.sent_out, len(self.sent_out), options.max_answer_len)
if __name__ == "__main__":
print('testset')
all_instances, max_node_num, max_in_neigh_num, max_out_neigh_num, max_sent_len = read_amr_file('./data/test.json')
print(max_in_neigh_num)
print(max_out_neigh_num)
print(max_node_num)
print(max_sent_len)
print('DONE!')