1 赛题名称
基于文本挖掘的企业隐患排查质量分析模型
2 赛题背景
企业自主填报安全生产隐患，对于将风险消除在事故萌芽阶段具有重要意义。企业在填报隐患时，往往存在不认真填报的情况，“虚报、假报”隐患内容，增大了企业监管的难度。采用大数据手段分析隐患内容，找出不切实履行主体责任的企业，向监管部门进行推送，实现精准执法，能够提高监管手段的有效性，增强企业安全责任意识。
3 赛题任务
本赛题提供企业填报隐患数据，
参赛选手需通过智能化手段识别其中是否存在“虚报、假报”的情况
。
看清赛题很关键，大家需要好好理解赛题目标之后，再去做题，可以避免很多弯路。
数据简介
本赛题数据集为脱敏后的企业填报自查隐患记录。
4 数据说明
训练集数据包含“【id、level_1（一级标准）、level_2（二级标准）、level_3（三级标准）、level_4（四级标准）、content（隐患内容）和label（标签）】”共7个字段。
其中“id”为主键，无业务意义；“一级标准、二级标准、三级标准、四级标准”为《深圳市安全隐患自查和巡查基本指引（2016年修订版）》规定的排查指引，一级标准对应不同隐患类型，二至四级标准是对一级标准的细化，企业自主上报隐患时，根据不同类型隐患的四级标准开展隐患自查工作；“隐患内容”为企业上报的具体隐患；“标签”标识的是该条隐患的合格性，“1”表示隐患填报不合格，“0”表示隐患填报合格。
预测结果文件results.csv
| 列名 | 说明 |
|---|---|
| id | 企业号 |
| label | 正负样本分类 |
- 文件名：results.csv，utf-8编码
- 参赛者以csv/json等文件格式，提交模型结果，平台进行在线评分，实时排名。
5 评测标准
本赛题采用F1 -score作为模型评判标准。
精确率P、召回率 R和 F1-score计算公式如下所示:
6 数据分析
-
查看数据集
训练集数据包含“【id、level_1（一级标准）、level_2（二级标准）、level_3（三级标准）、level_4（四级标准）、content（隐患内容）和label（标签）】”共7个字段。测试集没有label字段
-
标签分布
我们看下数据标签数量分布，看看有多少在划水哈哈
_
sns.countplot(train.label)
plt.xlabel('label count')
在训练集12000数据中，其中隐患填报合格的有10712条，隐患填报不合格的有1288条，差不多是9:1的比例，说明我们分类任务标签分布式极其不均衡的。
-
文本长度分布
我们将
level_
和
content
的文本拼接在一起
train['text']=train['content']+' '+train['level_1']+' '+train['level_2']+' '+train['level_3']+' '+train['level_4']
test['text']=test['content']+' '+test['level_1']+' '+test['level_2']+' '+test['level_3']+' '+test['level_4']
train['text_len']=train['text'].map(len)
test['text'].map(len).describe()
然后查看下文本最大长度分布
count 18000.000000
mean 64.762167
std 22.720117
min 27.000000
25% 50.000000
50% 60.000000
75% 76.000000
max 504.000000
Name: text, dtype: float64
train['text_len'].plot(kind='kde')
7 基于BERT的企业隐患排查质量分析模型
完整代码可以联系作者获取
7.1 导入工具包
import random
import numpy as np
import pandas as pd
from bert4keras.backend import keras, set_gelu
from bert4keras.tokenizers import Tokenizer
from bert4keras.models import build_transformer_model
from bert4keras.optimizers import Adam, extend_with_piecewise_linear_lr
from bert4keras.snippets import sequence_padding, DataGenerator
from bert4keras.snippets import open
from keras.layers import Lambda, Dense
Using TensorFlow backend.
7.2 设置参数
set_gelu('tanh') # 切换gelu版本
num_classes = 2
maxlen = 128
batch_size = 32
config_path = '../model/albert_small_zh_google/albert_config_small_google.json'
checkpoint_path = '../model/albert_small_zh_google/albert_model.ckpt'
dict_path = '../model/albert_small_zh_google/vocab.txt'
# 建立分词器
tokenizer = Tokenizer(dict_path, do_lower_case=True)
7.3 定义模型
# 加载预训练模型
bert = build_transformer_model(
config_path=config_path,
checkpoint_path=checkpoint_path,
model='albert',
return_keras_model=False,
)
output = Lambda(lambda x: x[:, 0], name='CLS-token')(bert.model.output)
output = Dense(
units=num_classes,
activation='softmax',
kernel_initializer=bert.initializer
)(output)
model = keras.models.Model(bert.model.input, output)
model.summary()
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
Input-Token (InputLayer) (None, None) 0
__________________________________________________________________________________________________
Input-Segment (InputLayer) (None, None) 0
__________________________________________________________________________________________________
Embedding-Token (Embedding) (None, None, 128) 2704384 Input-Token[0][0]
__________________________________________________________________________________________________
Embedding-Segment (Embedding) (None, None, 128) 256 Input-Segment[0][0]
__________________________________________________________________________________________________
Embedding-Token-Segment (Add) (None, None, 128) 0 Embedding-Token[0][0]
Embedding-Segment[0][0]
__________________________________________________________________________________________________
Embedding-Position (PositionEmb (None, None, 128) 65536 Embedding-Token-Segment[0][0]
__________________________________________________________________________________________________
Embedding-Norm (LayerNormalizat (None, None, 128) 256 Embedding-Position[0][0]
__________________________________________________________________________________________________
Embedding-Mapping (Dense) (None, None, 384) 49536 Embedding-Norm[0][0]
__________________________________________________________________________________________________
Transformer-MultiHeadSelfAttent (None, None, 384) 591360 Embedding-Mapping[0][0]
Embedding-Mapping[0][0]
Embedding-Mapping[0][0]
Transformer-FeedForward-Norm[0][0
Transformer-FeedForward-Norm[0][0
Transformer-FeedForward-Norm[0][0
Transformer-FeedForward-Norm[1][0
Transformer-FeedForward-Norm[1][0
Transformer-FeedForward-Norm[1][0
Transformer-FeedForward-Norm[2][0
Transformer-FeedForward-Norm[2][0
Transformer-FeedForward-Norm[2][0
Transformer-FeedForward-Norm[3][0
Transformer-FeedForward-Norm[3][0
Transformer-FeedForward-Norm[3][0
Transformer-FeedForward-Norm[4][0
Transformer-FeedForward-Norm[4][0
Transformer-FeedForward-Norm[4][0
__________________________________________________________________________________________________
Transformer-MultiHeadSelfAttent (None, None, 384) 0 Embedding-Mapping[0][0]
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward-Norm[0][0
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward-Norm[1][0
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward-Norm[2][0
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward-Norm[3][0
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward-Norm[4][0
Transformer-MultiHeadSelfAttentio
__________________________________________________________________________________________________
Transformer-MultiHeadSelfAttent (None, None, 384) 768 Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
__________________________________________________________________________________________________
Transformer-FeedForward (FeedFo (None, None, 384) 1181568 Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
Transformer-MultiHeadSelfAttentio
__________________________________________________________________________________________________
Transformer-FeedForward-Add (Ad (None, None, 384) 0 Transformer-MultiHeadSelfAttentio
Transformer-FeedForward[0][0]
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward[1][0]
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward[2][0]
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward[3][0]
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward[4][0]
Transformer-MultiHeadSelfAttentio
Transformer-FeedForward[5][0]
__________________________________________________________________________________________________
Transformer-FeedForward-Norm (L (None, None, 384) 768 Transformer-FeedForward-Add[0][0]
Transformer-FeedForward-Add[1][0]
Transformer-FeedForward-Add[2][0]
Transformer-FeedForward-Add[3][0]
Transformer-FeedForward-Add[4][0]
Transformer-FeedForward-Add[5][0]
__________________________________________________________________________________________________
CLS-token (Lambda) (None, 384) 0 Transformer-FeedForward-Norm[5][0
__________________________________________________________________________________________________
dense_7 (Dense) (None, 2) 770 CLS-token[0][0]
==================================================================================================
Total params: 4,595,202
Trainable params: 4,595,202
Non-trainable params: 0
__________________________________________________________________________________________________
# 派生为带分段线性学习率的优化器。
# 其中name参数可选，但最好填入，以区分不同的派生优化器。
# AdamLR = extend_with_piecewise_linear_lr(Adam, name='AdamLR')
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=Adam(1e-5), # 用足够小的学习率
# optimizer=AdamLR(learning_rate=1e-4, lr_schedule={
# 1000: 1,
# 2000: 0.1
# }),
metrics=['accuracy'],
)
7.4 生成数据
def load_data(valid_rate=0.3):
train_file = "../data/train.csv"
test_file = "../data/test.csv"
df_train_data = pd.read_csv("../data/train.csv")
df_test_data = pd.read_csv("../data/test.csv")
train_data, valid_data, test_data = [], [], []
for row_i, data in df_train_data.iterrows():
id, level_1, level_2, level_3, level_4, content, label = data
id, text, label = id, str(level_1) + '\t' + str(level_2) + '\t' + \
str(level_3) + '\t' + str(level_4) + '\t' + str(content), label
if random.random() > valid_rate:
train_data.append( (id, text, int(label)) )
else:
valid_data.append( (id, text, int(label)) )
for row_i, data in df_test_data.iterrows():
id, level_1, level_2, level_3, level_4, content = data
id, text, label = id, str(level_1) + '\t' + str(level_2) + '\t' + \
str(level_3) + '\t' + str(level_4) + '\t' + str(content), 0
test_data.append( (id, text, int(label)) )
return train_data, valid_data, test_data
train_data, valid_data, test_data = load_data(valid_rate=0.3)
valid_data
[(5,
'工业/危化品类（现场）—2016版\t（一）消防检查\t2、防火检查\t8、易燃易爆危险物品和场所防火防爆措施的落实情况以及其他重要物资的防火安全情况；\t防爆柜里面稀释剂，机油费混装',
0),
(3365,
'三小场所（现场）—2016版\t（一）消防安全\t2、消防通道和疏散\t2、疏散通道、安全出口设置应急照明灯和疏散指示标志。\t4楼消防楼梯安全出口指示牌坏',
0),
...]
len(train_data)
8403
class data_generator(DataGenerator):
"""数据生成器
"""
def __iter__(self, random=False):
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
for is_end, (id, text, label) in self.sample(random):
token_ids, segment_ids = tokenizer.encode(text, maxlen=maxlen)
batch_token_ids.append(token_ids)
batch_segment_ids.append(segment_ids)
batch_labels.append([label])
if len(batch_token_ids) == self.batch_size or is_end:
batch_token_ids = sequence_padding(batch_token_ids)
batch_segment_ids = sequence_padding(batch_segment_ids)
batch_labels = sequence_padding(batch_labels)
yield [batch_token_ids, batch_segment_ids], batch_labels
batch_token_ids, batch_segment_ids, batch_labels = [], [], []
# 转换数据集
train_generator = data_generator(train_data, batch_size)
valid_generator = data_generator(valid_data, batch_size)
valid_data
[(5,
'工业/危化品类（现场）—2016版\t（一）消防检查\t2、防火检查\t8、易燃易爆危险物品和场所防火防爆措施的落实情况以及其他重要物资的防火安全情况；\t防爆柜里面稀释剂，机油费混装',
0),
(8,
'工业/危化品类（现场）—2016版\t（一）消防检查\t2、防火检查\t2、安全疏散通道、疏散指示标志、应急照明和安全出口情况；\t已整改',
1),
(3365,
'三小场所（现场）—2016版\t（一）消防安全\t2、消防通道和疏散\t2、疏散通道、安全出口设置应急照明灯和疏散指示标志。\t4楼消防楼梯安全出口指示牌坏',
0),
...]
7.5 训练和验证
evaluator = Evaluator()
model.fit(
train_generator.forfit(),
steps_per_epoch=len(train_generator),
epochs=2,
callbacks=[evaluator]
)
model.load_weights('best_model.weights')
# print(u'final test acc: %05f\n' % (evaluate(test_generator)))
print(u'final test acc: %05f\n' % (evaluate(valid_generator)))
final test acc: 0.981651
print(u'final test acc: %05f\n' % (evaluate(train_generator)))
完整代码可以联系作者获取
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