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component_bert_data_processor_v2.py
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component_bert_data_processor_v2.py
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#!/usr/bin/python
# coding:utf8
"""
@author: Cong Yu
@time: 2019-06-05 10:42
"""
import tensorflow as tf
import numpy as np
from sklearn.utils import shuffle
import pandas as pd
import collections
from sklearn.externals import joblib
# 8000 / 3685
labels = []
for i in range(20):
labels.append("DV" + str(i + 1))
config = {
"in_1": "./out/tokenizer.m", # 第一个输入为 tokenizer 序列化模型(由上一次传递过来)
"file": "../doc_qa/baili/train_esim.csv", # 第二个输入为 训练/测试 文件
"column_name_x1": "question",
"column_name_x2": "law_content",
"column_name_y": "label",
"label_list": ["0", "1"], # 整个样本空间的 标签集
"split": "", # 标签的分割符，默认为空，表示单标签，不为空的化，按分隔符进行分割出多标签
"max_seq_len1": 128, # 输入文本片段的最大 char级别 长度
"max_seq_len2": 128, # 输入文本片段的最大 char级别 长度
"out_1": "/datadisk3/baili/train_data_qq/train_laws.tf_record", # 输出为 tf_record 的二进制文件
}
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def process_one_example(tokenizer, text_a, text_b=None, max_seq_len=256):
"""
处理 单个样本
"""
tokens_a = tokenizer.tokenize(text_a)
tokens_b = None
if text_b:
tokens_b = tokenizer.tokenize(text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_len - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_len - 2:
tokens_a = tokens_a[0:(max_seq_len - 2)]
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_len:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_len
assert len(input_mask) == max_seq_len
assert len(segment_ids) == max_seq_len
feature = (input_ids, input_mask, segment_ids)
return feature
def prepare_tf_record_data(tokenizer, max_seq_len1, max_seq_len2, label_list, split, column_name_x1, column_name_x2,
column_name_y, path="./data/dev.csv", out_path="./out/dev.tf_record"):
"""
生成训练数据， tf.record, 单标签分类模型, 随机打乱数据
"""
df = pd.read_csv(path, index_col=0)
df = shuffle(df)
print(label_list)
label2id = {_: i for i, _ in enumerate(label_list)}
writer = tf.python_io.TFRecordWriter(out_path)
example_count = 0
for index, row in df.iterrows():
# label = label2id[row["topic"].strip()]
if not (row[column_name_x1]):
continue
if not isinstance(row[column_name_x1], str):
print(row[column_name_x1])
continue
feature_1 = process_one_example(tokenizer, row[column_name_x1], None, max_seq_len=max_seq_len1)
feature_2 = process_one_example(tokenizer, row[column_name_x2], None, max_seq_len=max_seq_len2)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
features = collections.OrderedDict()
# 区分多标签 与 多分类任务
if split == "":
label = [label2id.get(str(row[column_name_y]))]
else:
label = np.zeros(len(label_list), dtype=np.int64)
if str(row[column_name_y]) != "nan" and str(row[column_name_y]) != "":
label_index = [label2id.get(_) for _ in row[column_name_y].split(split)]
label[label_index] = 1
features["input_ids_1"] = create_int_feature(feature_1[0])
features["input_mask_1"] = create_int_feature(feature_1[1])
features["segment_ids_1"] = create_int_feature(feature_1[2])
features["input_ids_2"] = create_int_feature(feature_2[0])
features["input_mask_2"] = create_int_feature(feature_2[1])
features["segment_ids_2"] = create_int_feature(feature_2[2])
features["label_ids"] = create_int_feature(label)
if example_count < 5:
print("*** Example ***")
print("input_ids: %s" % " ".join([str(x) for x in feature_1[0]]))
print("input_mask: %s" % " ".join([str(x) for x in feature_1[1]]))
print("segment_ids: %s" % " ".join([str(x) for x in feature_1[2]]))
print("." * 100)
print("input_ids: %s" % " ".join([str(x) for x in feature_2[0]]))
print("input_mask: %s" % " ".join([str(x) for x in feature_2[1]]))
print("segment_ids: %s" % " ".join([str(x) for x in feature_2[2]]))
print("label: %s (id = %s)" % (str(row[column_name_y]), str(label)))
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
example_count += 1
# if example_count == 20000:
# break
if example_count % 3000 == 0:
print(example_count)
print("total example:", example_count)
writer.close()
def main():
tokenizer = joblib.load(config["in_1"])
prepare_tf_record_data(tokenizer, config["max_seq_len1"], config["max_seq_len2"], config["label_list"],
config["split"], config["column_name_x1"], config["column_name_x2"], config["column_name_y"],
path=config["file"], out_path=config["out_1"])
if __name__ == "__main__":
print("********* component_bert_data_processor_v2 start *********")
print("{:*^100s}".format("column_name_x1 and column_name_x2 are not none!!!"))
main()