How to get best hyperparameter in Spark XGBoost after cross validation

#1

Hi everyone,
I can perform distributed training and prediction successfully with SparkXGBRegressor, I can do cross validation using:

  crossval = CrossValidator(estimator=estimator,
                                    estimatorParamMaps=parm_grid,
                                    evaluator=evaluator,
                                    numFolds=numFolds,
                                    parallelism = parallelism)
    cv_model = crossval.fit(data)

However after training I want to get the best hyperparameters and I am not sure how to do that since:= this is not working:

cv_model.bestModel

and returns something like this
`

SparkXGBRegressor_7651797ba9ba

`
Which does not make any sense. Can anyone explain how to return the best hyperparameters similar to Python which is like

cv_model.best_params_

I am sure there is a way to do this.