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

Question

I'm tinkering with some cross-validation code from the PySpark documentation, and trying to get PySpark to tell me what model was selected:

from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import BinaryClassificationEvaluator
from pyspark.mllib.linalg import Vectors
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator

dataset = sqlContext.createDataFrame(
    [(Vectors.dense([0.0]), 0.0),
     (Vectors.dense([0.4]), 1.0),
     (Vectors.dense([0.5]), 0.0),
     (Vectors.dense([0.6]), 1.0),
     (Vectors.dense([1.0]), 1.0)] * 10,
    ["features", "label"])
lr = LogisticRegression()
grid = ParamGridBuilder().addGrid(lr.regParam, [0.1, 0.01, 0.001, 0.0001]).build()
evaluator = BinaryClassificationEvaluator()
cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
cvModel = cv.fit(dataset)

Running this in PySpark shell, I can get the linear regression model's coefficients, but I can't seem to find the value of lr.regParam selected by the cross validation procedure. Any ideas?

In [3]: cvModel.bestModel.coefficients
Out[3]: DenseVector([3.1573])

In [4]: cvModel.bestModel.explainParams()
Out[4]: ''

In [5]: cvModel.bestModel.extractParamMap()
Out[5]: {}

In [15]: cvModel.params
Out[15]: []

In [36]: cvModel.bestModel.params
Out[36]: []

Answer 1

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.

Answer 2

This might not be as good as wernerchao answer (because it's not convenient to store hyperparameters in variables), but you can quickly look at the best hyper-parameters of a cross validation model this way :

cvModel.getEstimatorParamMaps()[ np.argmax(cvModel.avgMetrics) ]

Answer 3

Assuming cvModel3Day is your model names, params can be extracted as shown below in Spark Scala

val params = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].extractParamMap()

val depth = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].getMaxDepth

val iter = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].getMaxIter

val bins = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].getMaxBins

val features  = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].getFeaturesCol

val step = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].getStepSize

val samplingRate  = cvModel3Day.bestModel.asInstanceOf[PipelineModel].stages(2).asInstanceOf[GBTClassificationModel].getSubsamplingRate