如何在spark-ml CrossValidatorModel中获得最佳逻辑回归的系数？

Question

I train a simple CrossValidatorModel using logistic regression and spark-ml pipelines. I can predict new data, but I'd like to go beyond the black box and do some analysis of the coefficients

 val lr = new LogisticRegression().
  setFitIntercept(true).
  setMaxIter(maxIter).
  setElasticNetParam(alpha).
  setStandardization(true).
  setFamily("binomial").
  setWeightCol("weight").
  setFeaturesCol("features").
  setLabelCol("response")

val assembler = new VectorAssembler().
  setInputCols(Array("feat1", "feat2")).
  setOutputCol("features")

val modelPipeline = new Pipeline().
  setStages(Array(assembler,lr))

val evaluator = new BinaryClassificationEvaluator()
  .setLabelCol("response")

Then I define a grid of parameters and I train over the grid to get the best model wrt AUC

val paramGrid = new ParamGridBuilder().
  addGrid(lr.regParam, lambdas).
  build()

val pipeline = new CrossValidator().
  setEstimator(modelPipeline).
  setEvaluator(evaluator).
  setEstimatorParamMaps(paramGrid).
  setNumFolds(nfolds)

val cvModel = pipeline.fit(train)

How do I get coefficients (the betas) of the best logistic regression model?

Answer 1

Extract best model:

val bestModel = cvModel.bestModel match {
  case pm: PipelineModel => Some(pm)
  case _ => None
}

Find logistic regression model:

val lrm = bestModel
  .map(_.stages.collect { case lrm: LogisticRegressionModel => lrm })
  .flatMap(_.headOption)

Extract coefficients:

lrm.map(m => (m.intercept, m.coefficients))

Quick and dirty equivalent:

val lrm: LogisticRegressionModel = cvModel
  .bestModel.asInstanceOf[PipelineModel]
  .stages
  .last.asInstanceOf[LogisticRegressionModel]

(lrm.intercept, lrm.coefficients)