feat:添加评估结果分析脚本

ad/01_ad_model_update.sh

@@ -111,38 +111,36 @@ init() {
   echo "init param model_name: ${model_name}"
   echo "init param model_local_path: ${model_local_path}"
   echo "init param model_oss_path: ${MODEL_OSS_PATH}"
+
+  echo "当前Python环境安装的Python版本: $(python --version)"
+  echo "当前Python环境安装的三方包: $(python -m pip list)"
 }
 
 # 校验大数据任务是否执行完成
 check_ad_hive() {
-
-    python -m pip list
-    echo $PYTHONPATH
-
-    local step_start_time=$(date +%s)
-    local max_hour=05
-    local max_minute=30
-    local elapsed=0
-    while true; do
-        local python_return_code=$(python ${sh_path}/ad_utils.py --excute_program check_ad_origin_hive --partition ${today_early_1} --hh 23)
-
-        elapsed=$(($(date +%s) - $step_start_time))
-        if [ "$python_return_code" -eq 0 ]; then
-            break
-        fi
-        echo "Python程序返回非0值，等待五分钟后再次调用。"
-        sleep 300
-        local current_hour=$(date +%H)
-        local current_minute=$(date +%M)
-        if (( current_hour > max_hour || (current_hour == max_hour && current_minute >= max_minute) )); then
-            local msg="大数据数据生产校验失败, 分区: ${today_early_1}"
-            echo -e "$LOG_PREFIX -- 大数据数据生产校验 -- ${msg}: 耗时 $elapsed"
-            # /root/anaconda3/bin/python ${sh_path}/ad_monitor_util.py --level error --msg "$msg" --start "$start_time" --elapsed "$elapsed"
-            exit 1
-        fi
-    done
-    echo "$LOG_PREFIX -- 大数据数据生产校验 -- 大数据数据生产校验通过: 耗时 $elapsed"
-
+  local step_start_time=$(date +%s)
+  local max_hour=05
+  local max_minute=30
+  local elapsed=0
+  while true; do
+      local python_return_code=$(python ${sh_path}/ad_utils.py --excute_program check_ad_origin_hive --partition ${today_early_1} --hh 23)
+
+      elapsed=$(($(date +%s) - $step_start_time))
+      if [ "$python_return_code" -eq 0 ]; then
+          break
+      fi
+      echo "Python程序返回非0值，等待五分钟后再次调用。"
+      sleep 300
+      local current_hour=$(date +%H)
+      local current_minute=$(date +%M)
+      if (( current_hour > max_hour || (current_hour == max_hour && current_minute >= max_minute) )); then
+          local msg="大数据数据生产校验失败, 分区: ${today_early_1}"
+          echo -e "$LOG_PREFIX -- 大数据数据生产校验 -- ${msg}: 耗时 $elapsed"
+          /root/anaconda3/bin/python ${sh_path}/ad_monitor_util.py --level error --msg "$msg" --start "$start_time" --elapsed "$elapsed"
+          exit 1
+      fi
+  done
+  echo "$LOG_PREFIX -- 大数据数据生产校验 -- 大数据数据生产校验通过: 耗时 $elapsed"
 }
 
 make_origin_data() {
@@ -255,7 +253,7 @@ model_predict() {
   local return_code=$?
   check_run_status $return_code $step_start_time "线上模型评估${predict_date_path: -8}的数据"
 
-  local mean_abs_diff=$(/root/anaconda3/bin/python ${sh_path}/model_predict_analyse.py -p ${online_model_predict_result_path} ${new_model_predict_result_path})
+  local mean_abs_diff=$(python ${sh_path}/model_predict_analyse.py -p ${online_model_predict_result_path} ${new_model_predict_result_path})
   if (( $(echo "${mean_abs_diff} > 0.000400" | bc -l ) ));then
     check_run_status 1 $step_start_time "线上模型评估${predict_date_path: -8}的数据，绝对误差大于0.000400，请检查"
     echo "线上模型评估${predict_date_path: -8}的数据，绝对误差大于0.000400，请检查"
@@ -297,15 +295,15 @@ main() {
 
   check_ad_hive
 
-  # make_origin_data
+  make_origin_data
 
-  # make_bucket_feature
+  make_bucket_feature
 
-  # xgb_train
+  xgb_train
 
-  # model_predict
+  model_predict
 
-  # model_upload_oss
+  model_upload_oss
 
 }