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, they add different parameters (numFeatures, regParam) by using ParamGridBuilder in the Pipeline. Then by the following line of code they make the best model:

val cvModel = crossval.fit(training.toDF)

Now, I want to know what are the parameters (numFeatures, regParam) from ParamGridBuilder that produces the best model.

I already used the following commands without success:

cvModel.bestModel.extractParamMap().toString()
cvModel.params.toList.mkString("(", ",", ")")
cvModel.estimatorParamMaps.toString()
cvModel.explainParams()
cvModel.getEstimatorParamMaps.mkString("(", ",", ")")
cvModel.toString()

Any help?

Thanks in advance,

Wylie

Best parameters are dumped to log but beats me how you can access this information from a CrossValidatorModel instance. – Gentleman

That's really frustrating. They aren't even logging it in PySpark. Such a small but important thing that's lacking... it makes me wonder if anyone is actually using this functionality. – Godoy

folks, any solution for this problem in the recent versions of Spark? – Lissome

you definitely can get it from cvModel.bestModel, please see my answer below – Steverson

This SO thread kinda answers the question. – Trost

Answer 1

One method to get a proper ParamMap object is to use CrossValidatorModel.avgMetrics: Array[Double] to find the argmax ParamMap:

implicit class BestParamMapCrossValidatorModel(cvModel: CrossValidatorModel) {
  def bestEstimatorParamMap: ParamMap = {
    cvModel.getEstimatorParamMaps
           .zip(cvModel.avgMetrics)
           .maxBy(_._2)
           ._1
  }
}

When run on the CrossValidatorModel trained in the Pipeline Example you cited gives:

scala> println(cvModel.bestEstimatorParamMap)
{
   hashingTF_2b0b8ccaeeec-numFeatures: 100,
   logreg_950a13184247-regParam: 0.1
}

Nations

Note: maxBy might need to be minBy, depending on the value of Evaluator.isLargerBetter. – Vortical

Answer 2

val bestPipelineModel = cvModel.bestModel.asInstanceOf[PipelineModel]
val stages = bestPipelineModel.stages

val hashingStage = stages(1).asInstanceOf[HashingTF]
println("numFeatures = " + hashingStage.getNumFeatures)

val lrStage = stages(2).asInstanceOf[LogisticRegressionModel]
println("regParam = " + lrStage.getRegParam)

source

Boil

Answer 3

To print everything in paramMap, you actually don't have to call parent:

cvModel.bestModel.extractParamMap()

To answer OP's question, to get a single best parameter, for example regParam:

cvModel.bestModel.extractParamMap().apply(cvModel.bestModel.getParam("regParam"))

Steverson

Note that this solution works OK with a single object. It returns an empty map in the case of a Pipeline. – Oehsen

Answer 4

This is how you get the chosen parameters

println(cvModel.bestModel.getMaxIter)   
println(cvModel.bestModel.getRegParam)  

Dye

Please don't add the same answer to multiple questions. Answer the best one and flag the rest as duplicates. See meta.stackexchange.com/questions/104227/… – Tetracaine

Answer 5

this java code should work: cvModel.bestModel().parent().extractParamMap().you can translate it to scala code parent()method will return an estimator, you can get the best params then.

Autoxidation

This is the correct answer to pySpark as well! The key is "parent"! In pySpark, I use modelOnly.bestModel.stages[-1]._java_obj.parent().getRegParam(). – Blowsy

Answer 6

This is the ParamGridBuilder()

paraGrid = ParamGridBuilder().addGrid(
hashingTF.numFeatures, [10, 100, 1000]
).addGrid(
    lr.regParam, [0.1, 0.01, 0.001]
).build()

There are 3 stages in pipeline. It seems we can assess parameters as the following:

for stage in cv_model.bestModel.stages:
    print 'stages: {}'.format(stage)
    print stage.params
    print '\n'

stage: Tokenizer_46ffb9fac5968c6c152b
[Param(parent='Tokenizer_46ffb9fac5968c6c152b', name='inputCol', doc='input column name'), Param(parent='Tokenizer_46ffb9fac5968c6c152b', name='outputCol', doc='output column name')]

stage: HashingTF_40e1af3ba73764848d43
[Param(parent='HashingTF_40e1af3ba73764848d43', name='inputCol', doc='input column name'), Param(parent='HashingTF_40e1af3ba73764848d43', name='numFeatures', doc='number of features'), Param(parent='HashingTF_40e1af3ba73764848d43', name='outputCol', doc='output column name')]

stage: LogisticRegression_451b8c8dbef84ecab7a9
[]

However, there is no parameter in the last stage, logiscRegression.

We can also get weight and intercept parameter from logistregression like the following:

cv_model.bestModel.stages[1].getNumFeatures()
10
cv_model.bestModel.stages[2].intercept
1.5791827733883774
cv_model.bestModel.stages[2].weights
DenseVector([-2.5361, -0.9541, 0.4124, 4.2108, 4.4707, 4.9451, -0.3045, 5.4348, -0.1977, -1.8361])

Full exploration: http://kuanliang.github.io/2016-06-07-SparkML-pipeline/

Kiddush

Answer 7

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.

Trost

Answer 8

I am working with Spark Scala 1.6.x and here is a full example of how i can set and fit a CrossValidator and then return the value of the parameter used to get the best model (assuming that training.toDF gives a dataframe ready to be used) :

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator

// Instantiate a LogisticRegression object
val lr = new LogisticRegression()

// Instantiate a ParamGrid with different values for the 'RegParam' parameter of the logistic regression
val paramGrid = new ParamGridBuilder().addGrid(lr.regParam, Array(0.0001, 0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1)).build()

// Setting and fitting the CrossValidator on the training set, using 'MultiClassClassificationEvaluator' as evaluator
val crossVal = new CrossValidator().setEstimator(lr).setEvaluator(new MulticlassClassificationEvaluator).setEstimatorParamMaps(paramGrid)
val cvModel = crossVal.fit(training.toDF)

// Getting the value of the 'RegParam' used to get the best model
val bestModel = cvModel.bestModel                    // Getting the best model
val paramReference = bestModel.getParam("regParam")  // Getting the reference of the parameter you want (only the reference, not the value)
val paramValue = bestModel.get(paramReference)       // Getting the value of this parameter
print(paramValue)                                    // In my case : 0.001

You can do the same for any parameter or any other type of model.

Albuminuria

Answer 9

If java，see this debug show;

bestModel.parent().extractParamMap()

Diegodiehard

Answer 10

Building in the solution of @macfeliga, a single liner that works for pipelines:

cvModel.bestModel.asInstanceOf[PipelineModel]
    .stages.foreach(stage => println(stage.extractParamMap))

Euphemize

Answer 11

For me, the @orangeHIX solution is perfect:

val cvModel = cv.fit(training)

val cvMejorModelo = cvModel.bestModel.asInstanceOf[ALSModel]

cvMejorModelo.parent.extractParamMap()

res86: org.apache.spark.ml.param.ParamMap =
{
    als_08eb64db650d-alpha: 0.05,
    als_08eb64db650d-checkpointInterval: 10,
    als_08eb64db650d-coldStartStrategy: drop,
    als_08eb64db650d-finalStorageLevel: MEMORY_AND_DISK,
    als_08eb64db650d-implicitPrefs: false,
    als_08eb64db650d-intermediateStorageLevel: MEMORY_AND_DISK,
    als_08eb64db650d-itemCol: product,
    als_08eb64db650d-maxIter: 10,
    als_08eb64db650d-nonnegative: false,
    als_08eb64db650d-numItemBlocks: 10,
    als_08eb64db650d-numUserBlocks: 10,
    als_08eb64db650d-predictionCol: prediction,
    als_08eb64db650d-rank: 1,
    als_08eb64db650d-ratingCol: rating,
    als_08eb64db650d-regParam: 0.1,
    als_08eb64db650d-seed: 1994790107,
    als_08eb64db650d-userCol: user
}

Elsaelsbeth