零售电商客户流失模型，基于tensorflow，xgboost4j-spark实现线性模型LR，FM，GBDT，RF，进行模型效果对比，离线/在线serving部署方式总结。
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LR使用LibSVM格式的数据集， 采用 TFRecords + tf.data.Dataset + model + tf_model_server的tensorflow编程模型。
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FM分别使用了csv和LibSVM两种格式的数据，采用 tf.placeholder / tf.Sparse_placeholder+ model + tf_model_server的tensorflow编程模型。
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GBDT使用csv格式数据，采用sklearn的自定义Pipeline配合xgboost的sklearn接口整体封装特征工程和模型为一个完整的pipeline的pkl序列化文件，再包上Flask的API模型接口。GBDT也采用xgboost4j-spark进行模型效果对比。
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RF采用SparkSQL的原始数据，采用Spark ML组件，配合airflow+spark submit定时任务部署。
| 指标/模型 | LR | FM | GBDT | GBDT | RF |
|---|---|---|---|---|---|
| 框架 | tensorflow | tensorflow | xgboost | xgboost4j-spark | SparkML |
| accuracy | 0.749 | 0.759 | 0.766 | 0.763 | 0.765 |
| precision | 0.750 | 0.764 | 0.765 | 0.766 | 0.766 |
| reccall | 0.845 | 0.842 | 0.853 | 0.847 | 0.850 |
| auc_score | 0.816 | 0.826 | 0.833 | 0.832 | 0.831 |
| f1_score | 0.795 | 0.801 | 0.807 | 0.805 | 0.806 |
特征类型
| 特征 | 备注 | 特征 | 备注 |
|---|---|---|---|
| shop_duration | 购物时间跨度 | recent | 6个月R值 |
| monetary | 6个月M值 | max_amount | 6个月最大一次购物金额 |
| items_count | 总购买商品数 | valid_points_sum | 有效积分数 |
| CHANNEL_NUM_ID | 注册渠道 | member_day | 会员年限 |
| VIP_TYPE_NUM_ID | 会员卡等级 | frequence | 6个月F值 |
| avg_amount | 客单价 | item_count_turn | 单次购买商品数 |
| avg_piece_amount | 单品购买价格 | monetary3 | 3个月M值 |
| max_amount3 | 3个月最大一次购物金额 | items_count3 | 3个月购买总商品数 |
| frequence3 | 3个月F值 | shops_count | 跨门店购买数 |
| promote_percent | 促销购买比例 | wxapp_diff | 微信小程序购买R值 |
| store_diff | 门店购买R值 | shop_channel | 购物渠道 |
| week_percent | 周末购物比例 | infant_group | 母婴客群 |
| water_product_group | 水产客群 | meat_group | 肉禽客群 |
| beauty_group | 美妆客群 | health_group | 保健客群 |
| fruits_group | 水果客群 | vegetables_group | 蔬菜客群 |
| pets_group | 家有宠物 | snacks_group | 零食客群 |
| smoke_group | 烟民 | milk_group | 奶制品客群 |
| instant_group | 方便食品客群 | grain_group | 粮油食品客群 |
数据位置/LR/data/churn_train_sample.csv，展示表头和第一行数据
head -2 churn_train_sample.csv
USR_NUM_ID,shop_duration,recent,monetary,max_amount,items_count,valid_points_sum,CHANNEL_NUM_ID,member_day,VIP_TYPE_NUM_ID,frequence,avg_amount,item_count_turn,avg_piece_amount,monetary3,max_amount3,items_count3,frequence3,shops_count,promote_percent,wxapp_diff,store_diff,shop_channel,week_percent,infant_group,water_product_group,meat_group,beauty_group,health_group,fruits_group,vegetables_group,pets_group,snacks_group,smoke_group,milk_group,instant_group,grain_group,label
464087,30以下,30以下,100以下,20-50,1-5,50-100,7,30以下,0,1以下,50-100,2-5,10-20,50-100,20-50,1-5,1以下,1以下,0.2-0.4,30以下,30以下,unknow,0.8以上,unknow,unknow,unknow,美妆客
csv转LibSVM格式 ，脚本位置/FM/fm_libsvm/libsvm_transform.py 查看LibSVM的对照表/FM/fm_libsvm/libsvm_transform.py
head -5 churn_featindex.txt
0:other 0
0:30以下 1
0:30-60 2
0:60-90 3
0:90-120 4
执行转化脚本
python libsvm_transform.py
LibSVM数据预览
head -2 churn_train_sample.svm
1 1:1 7:1 13:1 21:1 28:1 34:1 42:1 55:1 61:1 67:1 76:1 81:1 86:1 93:1 98:1 104:1 109:1 115:1 120:1 125:1 131:1 137:1 146:1 148:1 151:1 154:1 158:1 160:1 163:1 166:1 169:1 172:1 175:1 178:1 181:1 184:1
0 5:1 7:1 15:1 22:1 31:1 36:1 39:1 59:1 62:1 69:1 76:1 81:1 86:1 94:1 99:1 106:1 110:1 115:1 121:1 125:1 131:1 137:1 143:1 148:1 151:1 154:1 157:1 160:1 164:1 166:1 169:1 173:1 175:1 179:1 182:1 185:1
将LIbSVM数据制作成TFRecords数据
python TFRecord_process.py
训练模型
python main.py
模型训练过程
step: 9100 loss: 0.52239525 auc: 0.81408113
step: 9200 loss: 0.50950295 auc: 0.81406915
step: 9300 loss: 0.5170015 auc: 0.8140943
step: 9400 loss: 0.5239074 auc: 0.8141037
step: 9500 loss: 0.504278 auc: 0.81413954
step: 9600 loss: 0.5412767 auc: 0.8141376
step: 9700 loss: 0.5137014 auc: 0.81412816
step: 9800 loss: 0.46152985 auc: 0.8141491
step: 9900 loss: 0.48090518 auc: 0.8141693
step: 10000 loss: 0.49998602 auc: 0.8141641
[evaluation] loss: 0.51270264 auc: 0.814165
测试集评价
accuracy: 0.7492069434817584
precision: 0.7503747423646243
reall: 0.8452554744525548
f1: 0.7949941686862588
auc: 0.8156375812964103
项目文件树结构
├── TFRecord_process.py
├── __pycache__
│ ├── model.cpython-37.pyc
│ ├── preprocessing.cpython-37.pyc
│ └── utils.cpython-37.pyc
├── churn_lr.pb
│ ├── 001
│ │ ├── saved_model.pb
│ │ └── variables
│ │ ├── variables.data-00000-of-00001
│ │ └── variables.index
│ └── models
├── config.yml
├── data
│ ├── churn_featindex.txt
│ ├── churn_test.svm
│ ├── churn_train.svm
│ ├── test.tfrecords
│ └── train.tfrecords
├── main.py
├── model.py
└── utils.py
使用docker的tensorflow_model_server镜像部署模型，rest接口测试启动服务
docker run --rm -d -p 8501:8501 -v "/****/customer_churn_prediction/LR/churn_lr.pb:/models/churn_lr/" -e MODEL_NAME=churn_lr tensorflow/serving
接口测试
curl -d '{"instances": [{"input_x": [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,0,0,0,1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1,0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]}], "signature_name":"my_signature"}' -X POST http://localhost:8501/v1/models/churn_lr:predict
{
"predictions": [0.497120261
]
fm_churn_csv.py采用csv个数数据训练模型
python fm_churn_csv.py --feature_size 186
fm_libsvm.py采用sparse_placeholder直接训练libsvm格式数据
python fm_churn_libsvm.py
模型训练过程
step: 76100 loss: 0.5005622 auc: 0.82709
step: 76200 loss: 0.50755 auc: 0.8270913
step: 76300 loss: 0.48795617 auc: 0.8270925
step: 76400 loss: 0.5073022 auc: 0.8270925
step: 76500 loss: 0.5022451 auc: 0.8270947
step: 76600 loss: 0.5266277 auc: 0.8270936
step: 76700 loss: 0.50896007 auc: 0.8270941
step: 76800 loss: 0.46825206 auc: 0.8270943
step: 76900 loss: 0.49328235 auc: 0.8270949
step: 77000 loss: 0.5090138 auc: 0.82709527
[evaluation] loss 0.4988083 auc: 0.82709527
测试集评价
accuracy: 0.7592295588733791
precision: 0.7635289710090631
reall: 0.8423797379298215
f1: 0.8010185522008003
auc: 0.8263173355592242
使用docker的tensorflow_model_server镜像部署模型，rest接口测试启动服务
docker run -t --rm -p 8501:8501 -v "/****/customer_churn_prediction/FM/fm_csv/FM_churn.pb:/models/FM/" -e MODEL_NAME=FM tensorflow/serving
接口测试
curl -d '{"instances": [{"input_x": [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,0,0,0,1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1,0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0]}], "signature_name":"my_signature"}' -X POST http://localhost:8501/v1/models/FM:predict
{
"predictions": [0.472961
]
模型训练
python churn_xgb.py
GBDT测试集模型结果
acc: 0.7656144859931294
pri: 0.7654276063379557
rec: 0.8530070349277994
auc: 0.8327608699836433
启动flask web server
python churn_xgb_server.py
postman接口测试
提交spark任务
spark-submit --master local[*] --class com.mycom.myproject.churn_xgb4j_spark myproject-1.0-SNAPSHOT.jar
accuracy: 0.763
precision: 0.766
recall: 0.847
fMeasure: 0.805
AreaUnderROC: 0.832
spark-submit --master local[*] --class com.mycom.myproject.randomforest_churn myproject-1.0-SNAPSHOT.jar
AreaUnderROC: 0.831
accuracy: 0.765
precision: 0.766
recall: 0.850
fMeasure: 0.806