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

#!/bin/sh
set -x

# 0 全局变量/参数
originDataSavePath=/dw/recommend/model/31_ad_sample_data_v3_auto/
bucketFeatureSavePath=/dw/recommend/model/33_ad_train_data_v3_auto/
model_name=model_bkb8_v3
today="$(date +%Y%m%d)"
today_early_1="$(date -d '1 days ago' +%Y%m%d)"

MODEL_PATH=/root/zhaohp/recommend-emr-dataprocess/model
PREDICT_PATH=/root/zhaohp/recommend-emr-dataprocess/predict
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/zhangbo/
max_hour=17
max_minute=00

export SPARK_HOME=/opt/apps/SPARK2/spark-2.4.8-hadoop3.2-1.0.8
export PATH=$SPARK_HOME/bin:$PATH
export HADOOP_CONF_DIR=/etc/taihao-apps/hadoop-conf
export JAVA_HOME=/usr/lib/jvm/java-1.8.0

# 1 判断依赖的数据表是否生产完成
source /root/anaconda3/bin/activate py37
while true; do
  python_return_code=$(python ad/ad_utils.py --excute_program check_ad_origin_hive --partition ${today} --hh 10)
  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}10"
    /root/anaconda3/bin/python ad/ad_monitor_util.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:16 \
beginStr:${today_early_1}00 endStr:${today}10 \
savePath:${originDataSavePath} \
table:alg_recsys_ad_sample_all filterHours:00,01,02,03,04,05,06,07 \
idDefaultValue:0.01
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/ad_monitor_util.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} repartition:100 \
filterNames:adid_,targeting_conversion_ \
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/ad_monitor_util.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}/* | ${FM_HOME}/fm_train -m ${MODEL_PATH}/${model_name}_${today_early_1}.txt -dim 1,1,8  -im ${MODEL_PATH}/model_online.txt -core 8
if [ $? -ne 0 ]; then
   echo "模型训练失败"
   /root/anaconda3/bin/python ad/ad_monitor_util.py "广告模型训练失败"
   exit 1
fi


# 5 对比AUC
# 5.1 校验今天10分区的数据是否生产完成
online_model=${MODEL_PATH}/model_online.txt
$HADOOP fs -text ${bucketFeatureSavePath}/${today}/* | /root/sunmingze/alphaFM/bin/fm_predict -m ${MODEL_PATH}/${online_model} -dim 8 -core 8 -out ${PREDICT_PATH}/${model_name}_${today}_online.txt
$HADOOP fs -text ${bucketFeatureSavePath}/${today}/* | /root/sunmingze/alphaFM/bin/fm_predict -m ${MODEL_PATH}/${model_name}_${today_early_1}.txt -dim 8 -core 8 -out ${PREDICT_PATH}/${model_name}_${today}_new.txt

# shellcheck disable=SC2006
online_auc=`cat ${PREDICT_PATH}/${model_name}_${today}_online.txt | /root/sunmingze/AUC/AUC`
new_auc=`cat ${PREDICT_PATH}/${model_name}_${today}_new.txt | /root/sunmingze/AUC/AUC`
if [ "$online_auc" \< "$new_auc" ]; then
    echo "新模型优于线上模型: 线上模型AUC: ${online_auc}, 新模型AUC: ${new_auc}"
    /root/anaconda3/bin/python ad/ad_monitor_util.py "新模型优于线上模型: 线上模型AUC: ${online_auc}, 新模型AUC: ${new_auc}"
else
    echo "新模型不如线上模型: 线上模型AUC: ${online_auc}, 新模型AUC: ${new_auc}"
    /root/anaconda3/bin/python ad/ad_monitor_util.py "新模型不如线上模型: 线上模型AUC: ${online_auc}, 新模型AUC: ${new_auc}"
    exit 1
fi


# 6 模型格式转换
cat cat ${MODEL_PATH}/${model_name}_${today_early_1}.txt |
awk -F " " '{
    if (NR == 1) {
        print $1"\t"$2
    } else {
        split($0, fields, " ");
        OFS="\t";
        line=""
        for (i = 1; i <= 10 && i <= length(fields); i++) {
            line = (line ? line "\t" : "") fields[i];
        }
        print line
    }
}' > ${MODEL_PATH}/${model_name}_${today_early_1}_change.txt

#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/ad_monitor_util.py "推荐模型文件至OSS失败"
#   exit 1
#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


# 32 16 * * * cd /root/zhangbo/recommend-emr-dataprocess && /bin/sh ./ad/01_ad_model_update_everyday.sh > logs/01_update_eventday$(date +\%Y-\%m-\%d_\%H).log 2>&1