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+#!/bin/sh
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+set -x
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+
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+
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+source /root/anaconda3/bin/activate py37
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+
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+# 模型保存路径
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+model_save_path=""
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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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+
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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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+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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+
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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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+while read -r line && [ ${count} -lt ${max_line} ]; do
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+
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+ # 使用 ! 取反判断，只有当行中不包含 "cid" 时才执行继续的逻辑
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+ if [[ "${line}" == *"cid"* ]]; then
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+ continue
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+ fi
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+
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+ read -a numbers <<< "${line}"
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+
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+ # 分数分别保存
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+ old_score_map[${numbers[0]}]=${numbers[6]}
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+ new_score_map[${numbers[0]}]=${numbers[7]}
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+
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+ old_total_diff=$( echo "${old_total_diff} + ${numbers[6]}" | bc -l )
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+ new_total_diff=$( echo "${new_total_diff} + ${numbers[7]}" | bc -l )
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+
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+ count=$((${count} + 1))
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+
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+done < "${predict_analyse_file_path}"
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+
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+old_incr_rate_avg=$( echo "scale=6; ${old_total_diff} / ${count}" | bc -l )
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+new_incr_rate_avg=$( echo "scale=6; ${new_total_diff} / ${count}" | bc -l )
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+
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+echo "老模型Top10差异平均值: ${old_incr_rate_avg}"
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+echo "新模型Top10差异平均值: ${new_incr_rate_avg}"
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+echo "新老模型分数对比: "
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+for cid in "${!new_score_map[@]}"; do
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+ echo "\t CID: $cid, 老模型分数: ${old_score_map[$cid]}, 新模型分数: ${new_score_map[$cid]}"
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+done
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+
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+local msg="\n\t - 广告模型文件更新完成" \
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+ "\n\t - 老模型Top10差异平均值: ${old_incr_rate_avg}" \
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+ "\n\t - 新模型Top10差异平均值: ${new_incr_rate_avg}" \
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+ "\n\t - 模型在HDFS中的路径: ${model_save_path}" \
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+ "\n\t - 模型上传路径: ${MODEL_OSS_PATH}/${model_name}.tar.gz"
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+
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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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+
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+
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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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+
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+
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