阿里云人工智能平台PAI线性回归-云淘科技

组件配置

您可以使用以下任意一种方式，配置线性回归组件参数。

方式一：可视化方式

在Designer（原PAI-Studio）工作流页面配置组件参数。

方式二：PAI命令方式

使用PAI命令方式，配置该组件参数。您可以使用SQL脚本组件进行PAI命令调用，详情请参见SQL脚本。

PAI -name linearregression
    -project algo_public
    -DinputTableName=lm_test_input
    -DfeatureColNames=x
    -DlabelColName=y
    -DmodelName=lm_test_input_model_out;

示例

1. 使用SQL语句，生成测试数据。

    drop table if exists lm_test_input;
     create table lm_test_input as
     select
       *
     from
     (
       select 10 as y, 1.84 as x1, 1 as x2, '0:1.84 1:1' as sparsecol1 from dual
         union all
       select 20 as y, 2.13 as x1, 0 as x2, '0:2.13' as sparsecol1 from dual
         union all
       select 30 as y, 3.89 as x1, 0 as x2, '0:3.89' as sparsecol1 from dual
         union all
       select 40 as y, 4.19 as x1, 0 as x2, '0:4.19' as sparsecol1 from dual
         union all
       select 50 as y, 5.76 as x1, 0 as x2, '0:5.76' as sparsecol1 from dual
         union all
       select 60 as y, 6.68 as x1, 2 as x2, '0:6.68 1:2' as sparsecol1 from dual
         union all
       select 70 as y, 7.58 as x1, 0 as x2, '0:7.58' as sparsecol1 from dual
         union all
       select 80 as y, 8.01 as x1, 0 as x2, '0:8.01' as sparsecol1 from dual
         union all
       select 90 as y, 9.02 as x1, 3 as x2, '0:9.02 1:3' as sparsecol1 from dual
         union all
       select 100 as y, 10.56 as x1, 0 as x2, '0:10.56' as sparsecol1 from dual
     ) tmp;

2. 使用PAI命令，提交线性回归组件参数。

   PAI -name linearregression
       -project algo_public
       -DinputTableName=lm_test_input
       -DlabelColName=y
       -DfeatureColNames=x1,x2
       -DmodelName=lm_test_input_model_out
       -DoutputTableName=lm_test_input_conf_out
       -DenableCoefficientEstimate=true
       -DenableFitGoodness=true
       -Dlifecycle=1;

3. 使用PAI命令，提交预测组件参数。

   pai -name prediction
       -project algo_public
       -DmodelName=lm_test_input_model_out
       -DinputTableName=lm_test_input
       -DoutputTableName=lm_test_input_predict_out
       -DappendColNames=y;

4. 查看输出的模型评估表lm_test_input_conf_out。

   +------------+------------+------------+------------+--------------------+------------+
   | colname    | value      | tscore     | pvalue     | confidenceinterval | p          |
   +------------+------------+------------+------------+--------------------+------------+
   | Intercept  | -6.42378496687763 | -2.2725755951390028 | 0.06       | {"2.5%": -11.964027, "97.5%": -0.883543} | coefficient |
   | x1         | 10.260063429838898 | 23.270944360826963 | 0.0        | {"2.5%": 9.395908, "97.5%": 11.124219} | coefficient |
   | x2         | 0.35374498323846265 | 0.2949247320997519 | 0.81       | {"2.5%": -1.997160, "97.5%": 2.704650} | coefficient |
   | rsquared   | 0.9879675667384592 | NULL       | NULL       | NULL               | goodness   |
   | adjusted_rsquared | 0.9845297286637332 | NULL       | NULL       | NULL               | goodness   |
   | aic        | 59.331109494251805 | NULL       | NULL       | NULL               | goodness   |
   | degree_of_freedom | 7.0        | NULL       | NULL       | NULL               | goodness   |
   | standardErr_residual | 3.765777749448906 | NULL       | NULL       | NULL               | goodness   |
   | deviance   | 99.26757440771128 | NULL       | NULL       | NULL               | goodness   |
   +------------+------------+------------+------------+--------------------+------------+

5. 查看预测结果表lm_test_input_predict_out。

   +------------+-------------------+------------------+-------------------+
   | y          | prediction_result | prediction_score | prediction_detail |
   +------------+-------------------+------------------+-------------------+
   | 10         | NULL              | 12.808476727264404 | {"y": 12.8084767272644} |
   | 20         | NULL              | 15.43015013867922 | {"y": 15.43015013867922} |
   | 30         | NULL              | 33.48786177519568 | {"y": 33.48786177519568} |
   | 40         | NULL              | 36.565880804147355 | {"y": 36.56588080414735} |
   | 50         | NULL              | 52.674180388994415 | {"y": 52.67418038899442} |
   | 60         | NULL              | 62.82092871092313 | {"y": 62.82092871092313} |
   | 70         | NULL              | 71.34749583130122 | {"y": 71.34749583130122} |
   | 80         | NULL              | 75.75932310613193 | {"y": 75.75932310613193} |
   | 90         | NULL              | 87.1832221199846 | {"y": 87.18322211998461} |
   | 100        | NULL              | 101.92248485222113 | {"y": 101.9224848522211} |
   +------------+-------------------+------------------+-------------------+