Platform For AI:Lasso Regression Prediction

Limits

You can use the Ridge Regression Training component based only on one of the following computing resources: MaxCompute, Realtime Compute for Apache Flink, or Deep Learning Containers (DLC) of Platform for AI (PAI).

How LASSO works

LASSO creates a penalty function to obtain a more refined model. LASSO can shrink some regression coefficients and set specific regression coefficients to zero. If a coefficient is shrunk, the sum of the absolute values of the coefficient is less than a fixed value. This way, LASSO retains the beneficial features of subset shrinkage and implements biased estimation on multicollinearity data.

Configure the component in the PAI console

• Input ports

  Input port (left-to-right)	Type	Recommended upstream component	Required
  Input model of the prediction	None	Lasso Regression Training	Yes
  Input data of the prediction	None	Read Table Feature engineering Prepare and preprocess data	Yes

• Parameter settings

  Tab	Parameter	Description
  Field Setting	reservedCols	The columns to be reserved by the algorithm.
  	vectorCol	The name of the vector column.
  Parameter Setting	predictionCol	The name of the prediction column.
  	numThreads	The number of threads of the component. Default value: 1.
  Execution Tuning	Number of Workers	The number of workers. This parameter must be used together with the Memory per worker, unit MB parameter. The value of this parameter must be a positive integer. Valid values: [1,9999].
  	Memory per worker, unit MB	The memory size of each worker. Valid values: 1024 to 64 × 1024. Unit: MB.

Configure the component by coding

from pyalink.alink import *

def main(sources, sinks, parameter):
    model = sources[0]
    batchData = sources[1]

    predictor = LassoRegPredictBatchOp()\
        .setPredictionCol("pred")
    result = predictor.linkFrom(model, batchData)
    result.link(sinks[0])
    BatchOperator.execute()