How to extract model hyper-parameters from spark.ml in PySpark?

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Ran into this problem as well. I found out you need to call the java property for some reason I don’t know why. So just do this:

from pyspark.ml.tuning import TrainValidationSplit, ParamGridBuilder, CrossValidator
from pyspark.ml.regression import LinearRegression
from pyspark.ml.evaluation import RegressionEvaluator

evaluator = RegressionEvaluator(metricName="mae")
lr = LinearRegression()
grid = ParamGridBuilder().addGrid(lr.maxIter, [500]) \
                                .addGrid(lr.regParam, [0]) \
                                .addGrid(lr.elasticNetParam, [1]) \
                                .build()
lr_cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, \
                        evaluator=evaluator, numFolds=3)
lrModel = lr_cv.fit(your_training_set_here)
bestModel = lrModel.bestModel

Printing out the parameters you want:

>>> print 'Best Param (regParam): ', bestModel._java_obj.getRegParam()
0
>>> print 'Best Param (MaxIter): ', bestModel._java_obj.getMaxIter()
500
>>> print 'Best Param (elasticNetParam): ', bestModel._java_obj.getElasticNetParam()
1

This applies to other methods like extractParamMap() as well. They should fix this soon.

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