DB4AI-Query：模型训练和推断
openGauss当前版本支持了原生DB4AI能力，通过引入原生AI算子，简化操作流程，充分利用数据库优化器、执行器的优化与执行能力，获得高性能的数据库内模型训练能力。更简化的模型训练与预测流程、更高的性能表现，让开发者在更短时间内能更专注于模型的调优与数据分析上，而避免了碎片化的技术栈与冗余的代码实现。
关键字解析
表 1 DB4AI语法及关键字
使用指导
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本版本支持的算法概述。
当前版本的DB4AI支持基于SGD算子的逻辑回归（目前支持二分类任务）、线性回归和支持向量机算法（分类任务），以及基于K-Means算子的Kmeans聚类算法。
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模型训练语法说明。
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CREATE MODEL
使用“CREATE MODEL”语句可以进行模型的创建和训练。模型训练SQL语句，现有一个数据集为kmeans_2d，该表的数据内容如下：
openGauss=# select * from kmeans_2d; id | position ----+------------------------------------- 1 | {74.5268815685995,88.2141939294524} 2 | {70.9565760521218,98.8114827475511} 3 | {76.2756086327136,23.8387574302033} 4 | {17.8495847294107,81.8449544720352} 5 | {81.2175785354339,57.1677675866522} 6 | {53.97752255667,49.3158342130482} 7 | {93.2475341879763,86.934042100329} 8 | {72.7659293473698,19.7020415100269} 9 | {16.5800288529135,75.7475957670249} 10 | {81.8520747194998,40.3476078575477} 11 | {76.796671198681,86.3827232690528} 12 | {59.9231450678781,90.9907738864422} 13 | {70.161884885747,19.7427458665334} 14 | {11.1269539105706,70.9988166182302} 15 | {80.5005071521737,65.2822235273197} 16 | {54.7030725912191,52.151339428965} 17 | {103.059707058128,80.8419883321039} 18 | {85.3574452036992,14.9910179991275} 19 | {28.6501615960151,76.6922890325077} 20 | {69.7285806713626,49.5416352967732} (20 rows)该表的字段position的数据类型为 double precision[].
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以Kmeans为例，训练一个模型。从kmeans_2d训练集中指定position为特征列，使用kmeans算法，创建并保存模型point_kmeans。
openGauss=# CREATE MODEL point_kmeans USING kmeans FEATURES position FROM kmeans_2d WITH num_centroids=3; NOTICE: Hyperparameter max_iterations takes value DEFAULT (10) NOTICE: Hyperparameter num_centroids takes value 3 NOTICE: Hyperparameter tolerance takes value DEFAULT (0.000010) NOTICE: Hyperparameter batch_size takes value DEFAULT (10) NOTICE: Hyperparameter num_features takes value DEFAULT (2) NOTICE: Hyperparameter distance_function takes value DEFAULT (L2_Squared) NOTICE: Hyperparameter seeding_function takes value DEFAULT (Random++) NOTICE: Hyperparameter verbose takes value DEFAULT (0) NOTICE: Hyperparameter seed takes value DEFAULT (0) MODEL CREATED. PROCESSED 1上述命令中：
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“CREATE MODEL”语句用于模型的训练和保存。
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USING关键字指定算法名称。
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FEATURES用于指定训练模模型的特征，需根据训练数据表的列名添加。
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TARGET指定模型的训练目标，它可以是训练所需数据表的列名，也可以是一个表达式，例如: price > 10000。
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WITH用于指定训练模型时的超参数。当超参未被用户进行设置的时候，框架会使用默认数值。
针对不同的算子，框架支持不同的超参组合：
表 2 算子支持的超参
当前各个超参数设置的默认值和取值范围如下：
表 3 超参的默认值以及取值范围
GD (logistic_regression、linear_regression、svm_classification)
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模型保存成功，则返回创建成功信息：
MODEL CREATED. PROCESSED x
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查看模型信息。
当训练完成后模型会被存储到系统表gs_model_warehouse中。系统表gs_model_warehouse可以查看到关于模型本身和训练过程的相关信息。
用户可以通过查看系统表的方式查看模型，例如查看模型名为“point_kmeans”的SQL语句如下：
openGauss=# select * from gs_model_warehouse where modelname='point_kmeans'; -[ RECORD 1 ]---------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- modelname | point_kmeans modelowner | 10 createtime | 2021-04-30 17:30:39.59044 processedtuples | 20 discardedtuples | 0 pre_process_time | 6.2001e-05 exec_time | .000185272 iterations | 5 outputtype | 23 modeltype | kmeans query | CREATE MODEL point_kmeans USING kmeans FEATURES position FROM kmeans_2d WITH num_centroids=3; modeldata | weight | hyperparametersnames | {max_iterations,num_centroids,tolerance,batch_size,num_features,distance_function,seeding_function,verbose,seed} hyperparametersvalues | {10,3,1e-05,10,2,L2_Squared,Random++,0,0} hyperparametersoids | {23,23,701,23,23,1043,1043,23,23} coefnames | {original_num_centroids,actual_num_centroids,dimension,distance_function_id,seed,coordinates} coefvalues | {3,3,2,2,572368998,"(77.282589,23.724434)(74.421616,73.239455)(18.551682,76.320914)"} coefoids | trainingscoresname | trainingscoresvalue | modeldescribe | {"id:1,objective_function:542.851169,avg_distance_to_centroid:108.570234,min_distance_to_centroid:1.027078,max_distance_to_centroid:297.210108,std_dev_distance_to_centroid:105.053257,cluster_size:5","id:2,objective_function:5825.982139,avg_distance_to_centroid:529.634740,min_distance_to_centroid:100.270449,max_distance_to_centroid:990.300588,std_dev_distance_to_centroid:285.915094,cluster_size:11","id:3,objective_function:220.792591,avg_distance_to_centroid:55.198148,min_distance_to_centroid:4.216111,max_distance_to_centroid:102.117204,std_dev_distance_to_centroid:39.319118,cluster_size:4"} -
利用已存在的模型做推断任务。
使用“SELECT”和“PREDICT BY”关键字利用已有模型完成推断任务。
查询语法：SELECT…PREDICT BY…(FEATURES…)…FROM…;
openGauss=# SELECT id, PREDICT BY point_kmeans (FEATURES position) as pos FROM (select * from kmeans_2d limit 10); id | pos ----+----- 1 | 2 2 | 2 3 | 1 4 | 3 5 | 2 6 | 2 7 | 2 8 | 1 9 | 3 10 | 1 (10 rows)针对相同的推断任务，同一个模型的结果是稳定的。且基于相同的超参数和训练集训练的模型也具有稳定性，同时AI模型训练存在随机成分（每个batch的数据分布、随机梯度下降），所以不同的模型间的计算表现、结果允许存在小的差别。
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查看执行计划。
使用explain语句可对“CREATE MODEL”和“PREDICT BY”的模型训练或预测过程中的执行计划进行分析。Explain关键字后可直接拼接CREATE MODEL/ PREDICT BY语句（子句），也可接可选的参数，支持的参数如下：
表 4 EXPLAIN支持的参数
示例：
openGauss=# Explain CREATE MODEL patient_logisitic_regression USING logistic_regression FEATURES second_attack, treatment TARGET trait_anxiety > 50 FROM patients WITH batch_size=10, learning_rate = 0.05; NOTICE: Hyperparameter batch_size takes value 10 NOTICE: Hyperparameter decay takes value DEFAULT (0.950000) NOTICE: Hyperparameter learning_rate takes value 0.050000 NOTICE: Hyperparameter max_iterations takes value DEFAULT (100) NOTICE: Hyperparameter max_seconds takes value DEFAULT (0) NOTICE: Hyperparameter optimizer takes value DEFAULT (gd) NOTICE: Hyperparameter tolerance takes value DEFAULT (0.000500) NOTICE: Hyperparameter seed takes value DEFAULT (0) NOTICE: Hyperparameter verbose takes value DEFAULT (FALSE) NOTICE: GD shuffle cache size 212369 QUERY PLAN ------------------------------------------------------------------- Gradient Descent (cost=0.00..0.00 rows=0 width=0) -> Seq Scan on patients (cost=0.00..32.20 rows=1776 width=12) (2 rows) -
异常场景。
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训练阶段。
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场景一：当超参数的设置超出取值范围，模型训练失败，返回ERROR，并提示错误，例如：
openGauss=# CREATE MODEL patient_linear_regression USING linear_regression FEATURES second_attack,treatment TARGET trait_anxiety FROM patients WITH optimizer='aa'; NOTICE: Hyperparameter batch_size takes value DEFAULT (1000) NOTICE: Hyperparameter decay takes value DEFAULT (0.950000) NOTICE: Hyperparameter learning_rate takes value DEFAULT (0.800000) NOTICE: Hyperparameter max_iterations takes value DEFAULT (100) NOTICE: Hyperparameter max_seconds takes value DEFAULT (0) NOTICE: Hyperparameter optimizer takes value aa ERROR: Invalid hyperparameter value for optimizer. Valid values are: gd, ngd. (default is gd) -
场景二：当模型名称已存在，模型保存失败，返回ERROR，并提示错误原因：
openGauss=# CREATE MODEL patient_linear_regression USING linear_regression FEATURES second_attack,treatment TARGET trait_anxiety FROM patients; NOTICE: Hyperparameter batch_size takes value DEFAULT (1000) NOTICE: Hyperparameter decay takes value DEFAULT (0.950000) NOTICE: Hyperparameter learning_rate takes value DEFAULT (0.800000) NOTICE: Hyperparameter max_iterations takes value DEFAULT (100) NOTICE: Hyperparameter max_seconds takes value DEFAULT (0) NOTICE: Hyperparameter optimizer takes value DEFAULT (gd) NOTICE: Hyperparameter tolerance takes value DEFAULT (0.000500) NOTICE: Hyperparameter seed takes value DEFAULT (0) NOTICE: Hyperparameter verbose takes value DEFAULT (FALSE) NOTICE: GD shuffle cache size 5502 ERROR: The model name "patient_linear_regression" already exists in gs_model_warehouse. -
场景三：FEATURE或者TARGETS列是*，返回ERROR，并提示错误原因：
openGauss=# CREATE MODEL patient_linear_regression USING linear_regression FEATURES * TARGET trait_anxiety FROM patients; ERROR: FEATURES clause cannot be * -----------------------------------------------------------------------------------------------------------------------、 openGauss=# CREATE MODEL patient_linear_regression USING linear_regression FEATURES second_attack,treatment TARGET * FROM patients; ERROR: TARGET clause cannot be * -
场景四：对于无监督学习方法使用TARGET关键字，或者在监督学习方法中不适用TARGET关键字，均会返回ERROR，并提示错误原因：
openGauss=# CREATE MODEL patient_linear_regression USING linear_regression FEATURES second_attack,treatment FROM patients; ERROR: Supervised ML algorithms require TARGET clause ----------------------------------------------------------------------------------------------------------------------------- CREATE MODEL patient_linear_regression USING linear_regression TARGET trait_anxiety FROM patients; ERROR: Supervised ML algorithms require FEATURES clause -
场景五：当GUC参数statement_timeout设置了时长，训练超时执行的语句将被终止：执行CREATE MODEL语句。训练集的大小、训练轮数（iteration）、提前终止条件（tolerance、max_seconds）、并行线程数（nthread）等参数都会影响训练时长。当时长超过数据库限制，语句被终止模型训练失败。
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推断阶段。
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场景六：当模型名在系统表中查找不到，数据库会报ERROR：
openGauss=# select id, PREDICT BY patient_logistic_regression (FEATURES second_attack,treatment) FROM patients; ERROR: There is no model called "patient_logistic_regression". -
场景七：当做推断任务FEATURES的数据维度和数据类型与训练集存在不一致，将报ERROR，并提示错误原因，例如：
openGauss=# select id, PREDICT BY patient_linear_regression (FEATURES second_attack) FROM patients; ERROR: Invalid number of features for prediction, provided 1, expected 2 CONTEXT: referenced column: patient_linear_regression_pred ------------------------------------------------------------------------------------------------------------------------------------- openGauss=# select id, PREDICT BY patient_linear_regression (FEATURES 1,second_attack,treatment) FROM patients; ERROR: Invalid number of features for prediction, provided 3, expected 2 CONTEXT: referenced column: patient_linear_regression_pre
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