recommend-emr-dataprocess/ad/01_ad_model_update_everyday.sh

#!/bin/sh
set -ex
#
## 0 全局变量/参数
#originDataSavePath=/dw/recommend/model/31_ad_sample_data_auto/
#bucketFeatureSavePath=/dw/recommend/model/33_ad_train_data_nosparse_auto/
#model_name=model_lr0
#today="$(date +%Y%m%d)"
#today_early_1="$(date -d '1 days ago' +%Y%m%d)"
#beginTime=08
#endTime=23
#beginStr=${today_early_1}${beginTime}
#endStr=${today_early_1}${endTime}
#
#MODEL_PATH=/root/zhaohp/recommend-emr-dataprocess/model
#HADOOP=/opt/apps/HADOOP-COMMON/hadoop-common-current/bin/hadoop
#FM_HOME=/root/sunmingze/alphaFM
#OSS_PATH=oss://art-recommend.oss-cn-hangzhou.aliyuncs.com/ad_model/
#
#
## 1 判断依赖的数据表是否生产完成
#source /root/anaconda3/bin/activate py37
#max_hour=15
#max_minute=00
#while true; do
#  python_return_code=$(python ad/ad_utils.py --excute_program check_ad_origin_hive --partition ${today_early_1} --hh ${endTime})
#  if [ $python_return_code -eq 0 ]; then
#    echo "Python程序返回0，退出循环。"
#    break
#  fi
#  echo "Python程序返回非0值，等待五分钟后再次调用。"
#  sleep 300
#  current_hour=$(date +%H)
#  current_minute=$(date +%M)
#  if (( current_hour > max_hour || (current_hour == max_hour && current_minute >= max_minute) )); then
#    echo "最长等待时间已到，失败:${current_hour}-${current_minute}"
#    msg="广告特征数据校验失败，大数据分区没有数据: ${today_early_1}${endTime}"
#    /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
#    exit 1
#  fi
#done
#
#
## 2 原始特征生成
#/opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
#--class com.aliyun.odps.spark.zhp.makedata_ad.makedata_ad_31_originData_20240620 \
#--master yarn --driver-memory 1G --executor-memory 2G --executor-cores 1 --num-executors 16 \
#./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
#tablePart:64 repartition:32 \
#beginStr:${beginStr} endStr:${endStr} \
#savePath:${originDataSavePath} \
#table:alg_recsys_ad_sample_all_new
#if [ $? -ne 0 ]; then
#   echo "Spark原始样本生产任务执行失败"
#   msg="广告特征数据生成失败，Spark原始样本生产任务执行失败"
#   /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
#   exit 1
#else
#    echo "spark原始样本生产执行成功"
#fi
#
#
## 3 特征分桶
#/opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8/bin/spark-class2 org.apache.spark.deploy.SparkSubmit \
#--class com.aliyun.odps.spark.zhp.makedata_ad.makedata_ad_33_bucketData_20240622 \
#--master yarn --driver-memory 2G --executor-memory 4G --executor-cores 1 --num-executors 16 \
#./target/spark-examples-1.0.0-SNAPSHOT-shaded.jar \
#beginStr:${today_early_1} endStr:${today_early_1} repartition:400 \
#filterNames:XXXXX \
#bucketFileName:20240620_ad_bucket_249_fix.txt \
#readPath:${originDataSavePath} \
#savePath:${bucketFeatureSavePath}
#if [ $? -ne 0 ]; then
#   echo "Spark特征分桶处理任务执行失败"
#   msg="广告特征分桶失败，Spark特征分桶处理任务执行失败"
#   /root/anaconda3/bin/python ad/utils_monitor.py ${msg}
#   exit 1
#else
#   echo "spark特征分桶处理执行成功"
#fi
#
#
## 4 模型训练
#$HADOOP fs -text ${bucketFeatureSavePath}/${today_early_1}/* | /root/sunmingze/alphaFM/bin/fm_train -m model/${model_name}_${today_early_1}.txt -dim 1,1,0 -core 8
#if [ $? -ne 0 ]; then
#   echo "模型训练失败"
#   /root/anaconda3/bin/python ad/utils_monitor.py "广告模型训练失败"
#   exit 1
#fi
#
#
## 5 对比AUC
## 5.1 生成今天08-10的原始特征数据
#
#
#
#
#
#
#
#
#
#
#
#
#
#
## 6 模型格式转换
#cat ${MODEL_PATH}/${model_name}_${today_early_1}.txt \
#| sed '1d' | awk -F " " '{if($2!="0") print $1"\t"$2}' \
#> ${MODEL_PATH}/${model_name}_${today_early_1}_change.txt


# 7 模型文件上传OSS
online_model_path=${OSS_PATH}/${model_name}.txt
$HADOOP fs -test -e ${online_model_path}
if [ $? -eq 0 ]; then
    echo "数据存在, 先删除。"
    $HADOOP fs -rm -r ${online_model_path}
else
    echo "数据不存在"
fi

$HADOOP fs -put ${MODEL_PATH}/${model_name}_${today_early_1}_change.txt ${online_model_path}
if [ $? -eq 0 ]; then
   echo "推荐模型文件至OSS成功"
else
   echo "推荐模型文件至OSS失败"
   /root/anaconda3/bin/python ad/utils_monitor.py "推荐模型文件至OSS失败"
fi

# 7.3 本地保存最新的线上使用的模型，用于下一次的AUC验证
cp ${MODEL_PATH}/${model_name}_${today_early_1}.txt ${MODEL_PATH}/model_online.txt