forked from opengaussexamples/examples
107 lines
4.4 KiB
SQL
107 lines
4.4 KiB
SQL
-- 在这里我们将使用openGauss中的DB4AI模块对用户的画像
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-- 需要注意只有企业版的openGauss才具有AI特性，如果是轻量版请安装最新的企业版软件
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-- 1. 整理数据
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-- 为了适应模型的输入要求，需要对原始数据进行进一步修改
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CREATE OR REPLACE VIEW pm.log_train as
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SELECT row_number() OVER () AS id, "date", username,
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dense_rank() OVER (ORDER BY username) AS name_id,
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login::double precision, sys::double precision, db::double precision,
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insert_all::double precision, insert_score::double precision, insert_student::double precision, insert_teacher::double precision,
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sel_info::double precision, sel_score::double precision
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FROM log_refined
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ORDER BY log_refined."date", dense_rank() OVER (ORDER BY log_refined.username);
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-- 2. 创建模型
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-- 模型m0，没有使用insert_student, insert_teacher特征
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CREATE MODEL log_m0
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USING multiclass
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FEATURES login, sys, db, insert_all, insert_score, sel_info, sel_score
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TARGET name_id
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FROM log_train
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WITH classifier="logistic_regression";
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-- 模型m1
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CREATE MODEL log_m1
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USING multiclass
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FEATURES login, sys, db, insert_score, insert_student, insert_teacher, sel_info, sel_score
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TARGET name_id
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FROM log_train
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WITH classifier="logistic_regression", batch_size=5;
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-- 查看数据库中所有模型
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select modelname, createtime, processedtuples,iterations,modeltype, outputtype
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from gs_model_warehouse limit 20;
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-- 检查模型log_m1的详细参数信息
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SELECT gs_explain_model('log_m1');
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-- 3. 模型预测
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SELECT id,
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PREDICT BY log_m1
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(FEATURES login, sys, db, insert_score, insert_student, insert_teacher, sel_info, sel_score)
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as "PREDICT",
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name_id as "LABEL"
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INTO temp_pred_train
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FROM log_train;
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SELECT id,
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PREDICT BY log_m1
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(FEATURES login, sys, db, insert_score, insert_student, insert_teacher, sel_info, sel_score)
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as "PREDICT",
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name_id as "LABEL"
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INTO temp_pred_test
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FROM log_test;
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-- 4. 模型评估
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-- 计算分类结果的准确率
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SELECT
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COUNT(*) AS total_count,
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SUM(CASE WHEN "PREDICT"= "LABEL" THEN 1 ELSE 0 END) AS correct_count,
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CASE
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WHEN COUNT(*) = 0 THEN 0.0
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ELSE (SUM(CASE WHEN "PREDICT" = "LABEL" THEN 1 ELSE 0 END)::FLOAT / COUNT(*)) * 100.0
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END AS accuracy
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FROM temp_pred_train;
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-- 5. 模型的应用
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-- 5.1 用户画像
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-- 选择各特征下四分位值作为比较的阈值
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CREATE VIEW pm.log_25 AS
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SELECT
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY login) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY login) END AS login_25,
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sys) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sys) END AS sys_25,
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY db) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY db) END AS db_25,
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY insert_all) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY insert_all) END AS insert_all_25,
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY insert_score) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY insert_score) END AS insert_score_25,
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sel_info) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sel_info) END AS sel_info_25,
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CASE WHEN PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sel_score) = 0 THEN 1 ELSE PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY sel_score) END AS sel_score_25
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FROM
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pm.log_refined;
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-- 比较各个样本，得用户标签
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SELECT
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date,
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username,
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array_remove(ARRAY[
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CASE WHEN login > login_25 THEN '活跃' ELSE NULL END,
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CASE WHEN sys > sys_25 THEN '系统维护员' ELSE NULL END,
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CASE WHEN db > db_25 THEN '数据库维护员' ELSE NULL END,
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CASE WHEN insert_all > insert_all_25 THEN '维护数据表' ELSE NULL END,
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CASE WHEN insert_score > insert_score_25 THEN '维护成绩表' ELSE NULL END,
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CASE WHEN sel_info > sel_info_25 THEN '查询多' ELSE NULL END,
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CASE WHEN sel_score > sel_score_25 THEN '关注成绩' ELSE NULL END
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], NULL) AS labels
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FROM
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log_refined, (SELECT * FROM log_25 LIMIT 1) AS quartiles;
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-- 5.2 识别危险用户
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SELECT id, username, date,
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PREDICT BY log_m1 (FEATURES login, sys, db, insert_score, insert_student, insert_teacher, sel_info, sel_score)
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as "PREDICT",
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name_id as "LABEL"
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FROM log_test2 where "PREDICT"!="LABEL"; |