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@@ -5,9 +5,12 @@ set -x
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source /root/anaconda3/bin/activate py37
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# 模型保存路径
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-model_save_path=""
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+model_save_path=/dw/recommend/model/35_ad_model/model_xgb_351_1000_v2_1009_1015
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MODEL_OSS_PATH=oss://art-recommend.oss-cn-hangzhou.aliyuncs.com/zhangbo/
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model_name=model_xgb_351_1000_v2
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+predict_analyse_file_path=/root/zhaohp/XGB/predict_analyse_file/20241014_351_1000_analyse.txt
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+
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+start_time=$(date +%s)
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# 保存模型评估的分析结果
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old_incr_rate_avg=0
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@@ -15,10 +18,10 @@ new_incr_rate_avg=0
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declare -A old_score_map
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declare -A new_score_map
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-local count=0
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-local max_line=10
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-local old_total_diff=0
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-local new_total_diff=0
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+count=0
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+max_line=10
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+old_total_diff=0
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+new_total_diff=0
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while read -r line && [ ${count} -lt ${max_line} ]; do
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# 使用 ! 取反判断，只有当行中不包含 "cid" 时才执行继续的逻辑
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@@ -55,17 +58,13 @@ msg+="\n\t - 新模型Top10差异平均值: ${new_incr_rate_avg}"
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msg+="\n\t - 模型在HDFS中的路径: ${model_save_path}"
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msg+="\n\t - 模型上传路径: ${MODEL_OSS_PATH}/${model_name}.tar.gz"
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-local top10_msg = "| CID | 老模型 | 新模型 | \n| ---- | -------- | -------- | "
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-for cid in "${!new_score_map[@]}"; do
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-top10_msg="${top10_msg} \n| ${cid} | ${old_score_map[$cid]} | ${new_score_map[$cid]} | "
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-done
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-local top10_msg = "| CID | 老模型 | 新模型 | \n| ---- | -------- | -------- | "
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+top10_msg = "| CID | 老模型 | 新模型 | \n| ---- | -------- | -------- | "
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for cid in "${!new_score_map[@]}"; do
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top10_msg="${top10_msg} \n| ${cid} | ${old_score_map[$cid]} | ${new_score_map[$cid]} | "
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done
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-/root/anaconda3/bin/python ${sh_path}/ad_monitor_util.py --level info --msg "${msg}" --start "${start_time}" --elapsed "${elapsed}" --top10 "${top10_msg}"
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+/root/anaconda3/bin/python ${sh_path}/ad_monitor_util.py --level info --msg "${msg}" --start "${start_time}" --elapsed "10000" --top10 "${top10_msg}"
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