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generate_data.py
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generate_data.py
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import os
import random
import collections
import tensorflow.compat.v1 as tf
import numpy as np
from bert import tokenization
from utils import file_operation, relevance_info
from utils.fold_config import FOLD_CONFIG_DICT
random.seed(118)
tf.random.set_random_seed(118)
flags = tf.flags
FLAGS = flags.FLAGS
## Required parameters
flags.DEFINE_string(
"trec_run_filename", None,
"where the trec run file (e.g. produced by BM25) is"
)
flags.DEFINE_string(
"qrels_filename", None,
"where the qrels file is"
)
flags.DEFINE_string(
"query_field", 'title',
"None if no field, else title, desc, narr, question")
flags.DEFINE_string(
"query_filename", None,
"where the query file is. support TREC file now")
flags.DEFINE_string(
"corpus_filename", None,
"where the corpus file is. format: docno \t content")
flags.DEFINE_string(
"dataset", None,
"which dataset to run on. it would correspond to the fold config of qids"
)
flags.DEFINE_integer(
"fold", 3,
"run fold")
flags.DEFINE_integer(
"plen", 150,
"length of segmented passage"
)
flags.DEFINE_integer(
"overlap", 50,
"overlap between continuous segmented passages"
)
flags.DEFINE_integer(
"max_num_train_instance_perquery", 1000,
"The maximum number of training instances utilized from initial ranking"
)
flags.DEFINE_integer(
"rerank_threshold", 100,
"the maximum number of top documents to be reranked"
)
flags.DEFINE_string(
"data_dir", None,
"The input data dir. Should contain the .tsv files (or other data files) "
"for the task.")
flags.DEFINE_string(
"bert_config_filename", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string("vocab_filename", None,
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
flags.DEFINE_integer(
"max_num_segments_perdoc", 8,
"The maximum number of segments for each document"
)
class PointwiseInstance(object):
"""A single training instance (sentence pair)."""
def __init__(self, exampleid, tokens_a, tokens_b_list, relation_label):
self.exampleid = exampleid
self.tokens_a = tokens_a
self.tokens_b_list = tokens_b_list
self.relation_label = relation_label
def __str__(self):
s = ""
s += "example id: %s\n" % self.exampleid
s += "tokens a: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens_a]))
s += "tokens b: %s\n" % (" ".join(
[tokenization.printable_text(x) for x in self.tokens_b_list]))
s += "relation label: %s\n" % self.relation_label
s += "\n"
return s
def __repr__(self):
return self.__str__()
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=values))
return feature
def convert_data_pointwise(writer, tokenizer, qid_list, relevance_dict, corpus_dict, query_dict, is_eval=False):
if is_eval:
max_num_example = FLAGS.rerank_threshold
else:
max_num_example = FLAGS.max_num_train_instance_perquery
instances = []
idx = 0
for qid in qid_list:
tf.logging.info("Generating data for query {}".format(qid))
relevance = relevance_dict.get(qid)
judged_docno_list = relevance.get_judged_docno_list()
supervised_docno_list = relevance.get_supervised_docno_list() # initial ranking
# training data from the judged docno, built from bm25 top1000 result
relevant_docno_list = set()
if judged_docno_list is not None:
relevant_docno_list = judged_docno_list[1] + judged_docno_list[2]
relevant_docno_list = set(relevant_docno_list)
for docno in supervised_docno_list[:max_num_example]:
relation_label = 1 if docno in relevant_docno_list else 0
query = query_dict[qid]
doc = corpus_dict[docno]
instance = create_instance_pointwise(tokenizer, FLAGS.max_seq_length, qid, docno, query, doc, relation_label)
# append and shuffle on training set
if not is_eval:
instances.append(instance)
else:
write_instance_to_example_files(writer, tokenizer, instance, idx)
idx += 1
tf.logging.info("Totally {} examples".format(len(instances)))
if not is_eval:
random.shuffle(instances)
for idx, instance in enumerate(instances):
write_instance_to_example_files(writer, tokenizer, instance, idx)
if is_eval:
write_padding_instance_to_example_files(writer)
writer.close()
print("Distribution of length. Key is length, Val is count.")
for key, val in stats.items():
print("{}\t{}".format(key, val))
def create_instance_pointwise(tokenizer, max_seq_length, qid, docno, query, doc, label):
query = tokenization.convert_to_unicode(query)
doc = tokenization.convert_to_unicode(doc)
passages = get_passages(doc, FLAGS.plen, FLAGS.overlap)
if len(passages) == 0:
tf.logging.warn("Passage length is 0 in qid {} docno {}".format(qid, docno))
query = tokenization.convert_to_bert_input(
text=query,
max_seq_length=64,
tokenizer=tokenizer,
add_cls=True,
convert_to_id=False
)
passages = [tokenization.convert_to_bert_input(
text=p,
max_seq_length=max_seq_length-len(query),
tokenizer=tokenizer,
add_cls=False,
convert_to_id=False
) for p in passages]
instance = PointwiseInstance(
exampleid="{}-{}".format(qid, docno),
tokens_a=query,
tokens_b_list=passages,
relation_label=label
)
return instance
def write_padding_instance_to_example_files(writer, num_examples=50):
# 1-d arrays
input_ids = np.zeros((FLAGS.max_seq_length * FLAGS.max_num_segments_perdoc), dtype=np.int)
num_segments = FLAGS.max_num_segments_perdoc
label = 0
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["tokens_a_len"] = create_int_feature([3])
features["tokens_ids_lens"] = create_int_feature([24] * FLAGS.max_num_segments_perdoc)
features["num_segments"] = create_int_feature([num_segments])
features["label"] = create_int_feature([label])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
for _ in range(num_examples):
writer.write(tf_example.SerializeToString())
tf.logging.info("write {} padding instances successfully".format(num_examples))
def write_instance_to_example_files(writer, tokenizer, instance, instance_idx):
def padding_2d(ids_list, num_tokens_per_segment, padding_value=0):
_len = len(ids_list)
if padding_value == 0:
matrix = np.zeros((_len, num_tokens_per_segment), dtype=np.int)
elif padding_value == 1:
matrix = np.ones((_len, num_tokens_per_segment), dtype=np.int)
else:
raise ValueError("Unsupport padding value")
for i, _list in enumerate(ids_list):
matrix[i, :len(_list)] = _list
return matrix.flatten()
tokens_a = instance.tokens_a
tokens_b_list = instance.tokens_b_list
tokens_a_ids = tokenizer.convert_tokens_to_ids(tokens_a)
tokens_b_list = [tokenizer.convert_tokens_to_ids(p) for p in tokens_b_list]
label = instance.relation_label
assert len(tokens_b_list) <= FLAGS.max_num_segments_perdoc
num_segments = len(tokens_b_list)
input_ids = [tokens_a_ids + tokens_b_passage_ids for tokens_b_passage_ids in tokens_b_list]
tokens_a_len = len(tokens_a_ids) # helpful for segment ids
input_ids_lens = [len(input_id) for input_id in input_ids] # helpful for input mask
input_ids_lens = input_ids_lens + [FLAGS.max_seq_length] * (FLAGS.max_num_segments_perdoc - len(input_ids_lens))
input_ids = padding_2d(input_ids,FLAGS.max_seq_length, padding_value=0)
# write to tfrecord
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(input_ids)
features["tokens_a_len"] = create_int_feature([tokens_a_len])
features["tokens_ids_lens"] = create_int_feature(input_ids_lens)
features["num_segments"] = create_int_feature([num_segments])
features["label"] = create_int_feature([label])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
if instance_idx < 5:
tf.logging.info("*** Example ***")
tf.logging.info("tokens_a: %s" % " ".join(
[tokenization.printable_text(x) for x in instance.tokens_a]))
tf.logging.info("tokens_b_list: {}".format(instance.tokens_b_list))
for feature_name in features.keys():
feature = features[feature_name]
values = []
if feature.int64_list.value:
values = feature.int64_list.value
elif feature.float_list.value:
values = feature.float_list.value
tf.logging.info(
"%s: %s" % (feature_name, " ".join([str(x) for x in values])))
stats = collections.defaultdict(int)
def get_passages(text, plen, overlap):
""" Modified from https://github.com/AdeDZY/SIGIR19-BERT-IR/blob/master/tools/gen_passages.py
:param text:
:param plen:
:param overlap:
:return:
"""
words = text.strip().split(' ')
s, e = 0, 0
passages = []
while s < len(words):
e = s + plen
if e >= len(words):
e = len(words)
# if the last one is shorter than 'overlap', it is already in the previous passage.
if len(passages) > 0 and e - s <= overlap:
break
p = ' '.join(words[s:e])
passages.append(p)
s = s + plen - overlap
if len(passages) > FLAGS.max_num_segments_perdoc:
chosen_ids = sorted(random.sample(range(1, len(passages) - 1), FLAGS.max_num_segments_perdoc - 2))
chosen_ids = [0] + chosen_ids + [len(passages) - 1]
passages = [passages[id] for id in chosen_ids]
global stats
stats[len(passages)] += 1
return passages
def main(_):
# training config
qid_list = FOLD_CONFIG_DICT[FLAGS.dataset]
qid_list = collections.deque(qid_list)
rotate = FLAGS.fold - 1
map(qid_list.rotate(rotate), qid_list)
# currently, we just set up the training step. No support for model selection now.
# train_qid_list, valid_qid_list, test_qid_list = qid_list[0] + qid_list[1] + qid_list[2], qid_list[3], qid_list[4]
train_qid_list, test_qid_list = qid_list[0] + qid_list[1] + qid_list[2] + qid_list[3], qid_list[4]
train_qid_list, test_qid_list = sorted(train_qid_list), sorted(test_qid_list)
tf.logging.info("Running on dataset: {0}, on fold {1}".format(FLAGS.dataset, FLAGS.fold))
tf.logging.info("Traing on following qid: {0}\n".format(train_qid_list))
# tf.logging.info("Validating on following qid: {0}\n".format(valid_qid_list))
tf.logging.info("Testing on following qid: {0}\n".format(test_qid_list))
relevance_dict = relevance_info.create_relevance(FLAGS.trec_run_filename, FLAGS.qrels_filename)
corpus_dict = file_operation.key_value_from_file(FLAGS.corpus_filename)
query_dict = file_operation.load_trec_topics(FLAGS.query_filename)[FLAGS.query_field]
tokenizer = tokenization.FullTokenizer(
vocab_file=FLAGS.vocab_filename,
do_lower_case=FLAGS.do_lower_case
)
# begin data convertion to TFrecord
output_path = os.path.join(FLAGS.output_dir, "dataset_train.tfrecord")
tf.logging.info("Writing data into {}".format(output_path))
writer = tf.python_io.TFRecordWriter(output_path)
convert_data_pointwise(
writer=writer,
tokenizer=tokenizer,
qid_list=train_qid_list,
relevance_dict=relevance_dict,
corpus_dict=corpus_dict,
query_dict=query_dict,
is_eval=False
)
output_path = os.path.join(FLAGS.output_dir, "dataset_test.tfrecord")
tf.logging.info("Writing data into {}".format(output_path))
writer = tf.python_io.TFRecordWriter(output_path)
convert_data_pointwise(
writer=writer,
tokenizer=tokenizer,
qid_list=test_qid_list,
relevance_dict=relevance_dict,
corpus_dict=corpus_dict,
query_dict=query_dict,
is_eval=True
)
if __name__ == '__main__':
flags.mark_flag_as_required("trec_run_filename")
flags.mark_flag_as_required("qrels_filename")
flags.mark_flag_as_required("query_field")
flags.mark_flag_as_required("query_filename")
flags.mark_flag_as_required("corpus_filename")
flags.mark_flag_as_required("dataset")
flags.mark_flag_as_required("fold")
flags.mark_flag_as_required("vocab_filename")
flags.mark_flag_as_required("output_dir")
flags.mark_flag_as_required("plen")
flags.mark_flag_as_required("overlap")
tf.app.run()