recommend-emr-dataprocess/zhangbo/07_ad_model_update_everyday.sh

#!/bin/sh
set -ex

# 0 全局变量/参数
originDataSavePath=/dw/recommend/model/31_ad_sample_data_fix/
bucketFeatureSavePath=/dw/recommend/model/12_ros_data_v3/
model_name=ad_model_lr
today="$(date +%Y%m%d)"
today_early_1="$(date -d '1 days ago' +%Y%m%d)"
beginStr=${today_early_1}08
endStr=${today_early_1}23


# 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 utils.py --excute_program check_ad_origin_hive --partition ${endStr})
  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}"
    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.examples.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:/dw/recommend/model/31_ad_sample_data_auto/ \
# table:alg_recsys_ad_sample_all_new
# if [ $? -eq 1 ]; then
#    echo "Spark原始样本生产任务执行失败"
#    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.examples.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
# if [ $? -eq 1 ]; then
#     echo "Spark特征分桶任务执行失败"
#    exit 1
# else
#     echo "spark特征分桶执行失败"
# fi


# 4 模型训练

# 5 对比AUC

# 6 模型格式转换

# 7 模型文件上传OSS