如何在CrossValidatorModel中访问每个折页的计算指标 [英] How can I access computed metrics for each fold in a CrossValidatorModel

问题描述

如何从 spark.ml 中的 CrossValidatorModel 获取每个折叠的计算指标?我知道我可以使用 model.avgMetrics 来获取平均指标，但是是否有可能获得每折的原始结果，例如.结果的差异?

How can I get the computed metrics for each fold from a CrossValidatorModel in spark.ml? I know I can get the average metrics using model.avgMetrics but is it possible to get the raw results on each fold to look at eg. the variance of the results?

我正在使用Spark 2.0.0.

I am using Spark 2.0.0.

推荐答案

研究对于折叠，您可以这样自己进行迭代:

For the folds, you can do the iteration yourself like this:

    val splits = MLUtils.kFold(dataset.toDF.rdd, $(numFolds), $(seed))
    //K-folding operation starting
    //for each fold you have multiple models created cfm. the paramgrid
    splits.zipWithIndex.foreach { case ((training, validation), splitIndex) =>
      val trainingDataset = sparkSession.createDataFrame(training, schema).cache()
      val validationDataset = sparkSession.createDataFrame(validation, schema).cache()


      val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
      trainingDataset.unpersist()
      var i = 0
      while (i < numModels) {
        val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
        logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
        metrics(i) += metric
        i += 1
      }

这是在scala中，但是思路很清晰.

This is in scala, but the ideas are very clearly outlined.

看看此答案，它概述了结果每折.希望这会有所帮助.

Take a look at this answer that outlines results per fold. Hope this helps.

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