java - 如何从 Java 中的 TrainValidationSplitModel 中提取最佳参数集？

Question

我正在使用 ParamGridBuilder 构建参数网格以进行搜索，并使用 TrainValidationSplit 来确定 Java 中的最佳模型 (RandomForestClassifier)。现在，我想知道 ParamGridBuilder 中生成最佳模型的参数(maxDepth、numTrees)是什么。

      Pipeline pipeline = new Pipeline().setStages(new PipelineStage[]{
          new VectorAssembler()
         .setInputCols(new String[]{"a", "b"}).setOutputCol("features"), 
          new RandomForestClassifier()
         .setLabelCol("label")
         .setFeaturesCol("features")});

      ParamMap[] paramGrid = new ParamGridBuilder()
            .addGrid(rf.maxDepth(), new int[]{10, 15})
            .addGrid(rf.numTrees(), new int[]{5, 10})
            .build();

      BinaryClassificationEvaluator evaluator = new BinaryClassificationEvaluator().setLabelCol("label");

      TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
            .setEstimator(pipeline)
            .setEstimatorParamMaps(paramGrid)
            .setEvaluator(evaluator)
            .setTrainRatio(0.85);
      TrainValidationSplitModel model = trainValidationSplit.fit(dataLog);

      System.out.println("paramMap size: " + model.bestModel().paramMap().size());
      System.out.println("defaultParamMap size: " + model.bestModel().defaultParamMap().size());
      System.out.println("extractParamMap: " + model.bestModel().extractParamMap());
      System.out.println("explainParams: " + model.bestModel().explainParams());
      System.out.println("numTrees: " + model.bestModel().getParam("numTrees"))//NoSuchElementException: Param numTrees does not exist.

这些尝试没有帮助...

paramMap size: 0
defaultParamMap size: 0
extractParamMap: {

}
explainParams: 

Answer 1

我找到了一种方法:

Pipeline bestModelPipeline = (Pipeline) model.bestModel().parent();
RandomForestClassifier bestRf = (RandomForestClassifier) bestModelPipeline.getStages()[1];

System.out.println("maxDepth : " + bestRf.getMaxDepth());
System.out.println("numTrees : " + bestRf.getNumTrees());
System.out.println("maxBins : " + bestRf.getMaxBins());