Platform For AI:PS linear regression

Configure the component

Method 1: Use the visual interface

Add the PS Linear Regression component to the workflow in Designer, and configure its parameters in the pane on the right:

Method 2: Use a PAI command

Use a PAI command to configure the parameters for the PS Linear Regression component. You can run PAI commands using the SQL script component. For more information, see SQL Script.

# Train.
PAI -name ps_linearregression
    -project algo_public
    -DinputTableName="lm_test_input"
    -DmodelName="linear_regression_model"
    -DlabelColName="label"
    -DfeatureColNames="features"
    -Dl1Weight=1.0
    -Dl2Weight=0.0
    -DmaxIter=100
    -Depsilon=1e-6
    -DenableSparse=true
# Predict.
drop table if exists logistic_regression_predict;
PAI -name prediction
    -DmodelName="linear_regression_model"
    -DoutputTableName="linear_regression_predict"
    -DinputTableName="lm_test_input"
    -DappendColNames="label,features"
    -DfeatureColNames="features"
    -DenableSparse=true

Examples

1. Use the SQL script component to run the following SQL statement to generate input data. This example uses data in key-value (KV) format.

   drop table if exists lm_test_input;
   create table lm_test_input as
   select
   *
   from
   (
   select cast(2 as BIGINT) as label, '1:0.55 2:-0.15 3:0.82 4:-0.99 5:0.17' as features
       union all
   select cast(1 as BIGINT) as label, '1:-1.26 2:1.36 3:-0.13 4:-2.82 5:-0.41' as features
       union all
   select cast(1 as BIGINT) as label, '1:-0.77 2:0.91 3:-0.23 4:-4.46 5:0.91' as features
       union all
   select cast(2 as BIGINT) as label, '1:0.86 2:-0.22 3:-0.46 4:0.08 5:-0.60' as features
       union all
   select cast(1 as BIGINT) as label, '1:-0.76 2:0.89 3:1.02 4:-0.78 5:-0.86' as features
       union all
   select cast(1 as BIGINT) as label, '1:2.22 2:-0.46 3:0.49 4:0.31 5:-1.84' as features
       union all
   select cast(0 as BIGINT) as label, '1:-1.21 2:0.09 3:0.23 4:2.04 5:0.30' as features
       union all
   select cast(1 as BIGINT) as label, '1:2.17 2:-0.45 3:-1.22 4:-0.48 5:-1.41' as features
       union all
   select cast(0 as BIGINT) as label, '1:-0.40 2:0.63 3:0.56 4:0.74 5:-1.44' as features
       union all
   select cast(1 as BIGINT) as label, '1:0.17 2:0.49 3:-1.50 4:-2.20 5:-0.35' as features
   ) tmp;

   The generated data is shown in the following figure.

   Note

   For data in KV format, feature IDs must be positive integers and feature values must be real numbers. If feature IDs are strings, you must serialize the data. If feature values are categorical strings, you must perform feature discretization.

2. Build a workflow as shown in the following figure. For more information, see Algorithm modeling.

3. Configure the component parameters.

   1. Click the Read Table-1 component. In the pane on the right, on the Select Table tab, set Table Name to lm_test_input.

   2. Configure the parameters for the PS Linear Regression component as described in the following table. Use the default values for the other parameters.

      Parameter Type	Parameter	Description
      Fields setting	Is the format sparse?	Select true.
      	Feature Columns	Select the features column.
      	Label Column	Select the label column.
      Execution tuning	Number of cores	Set to 3.
      	Memory size per core	Set to 1024 MB.

   3. Configure the parameters for the Prediction component as described in the following table. Use the default values for the other parameters.

      Parameter Type	Parameter	Description
      Fields setting	Feature Columns	Select the features column.
      	Verbatim Output Column	Select the label and features columns.
      	Sparse Matrix	Select the Sparse Matrix check box.
      	Key-value separator	Set to a colon (:).
      	KV pair separator	Leave this parameter empty to use a space as the separator.

4. Click the Run button on the canvas to run the workflow.

5. After the workflow runs, right-click the Prediction-1 component and choose View Data > Prediction Result Output from the shortcut menu.