How to extract best parameters from a CrossValidatorModel

Question

I want to find the parameters of ParamGridBuilder that make the best model in CrossValidator in Spark 1.4.x,

In Pipeline Example in Spark documentation,

Answer 1

This SO thread kinda answers the question.

In a nutshell, you need to cast each object to its supposed-to-be class.

For the case of CrossValidatorModel, the following is what I did:

import org.apache.spark.ml.tuning.CrossValidatorModel
import org.apache.spark.ml.PipelineModel
import org.apache.spark.ml.regression.RandomForestRegressionModel

// Load CV model from S3
val inputModelPath = "s3://path/to/my/random-forest-regression-cv"
val reloadedCvModel = CrossValidatorModel.load(inputModelPath)

// To get the parameters of the best model
(
    reloadedCvModel.bestModel
        .asInstanceOf[PipelineModel]
        .stages(1)
        .asInstanceOf[RandomForestRegressionModel]
        .extractParamMap()
)

In the example, my pipeline has two stages (a VectorIndexer and a RandomForestRegressor), so the stage index is 1 for my model.