Package org.apache.spark.mllib.tree.model

Class GradientBoostedTreesModel

Object
org.apache.spark.mllib.tree.model.TreeEnsembleModel
org.apache.spark.mllib.tree.model.GradientBoostedTreesModel
All Implemented Interfaces:
Serializable, Saveable
public class GradientBoostedTreesModel extends org.apache.spark.mllib.tree.model.TreeEnsembleModel implements Saveable
Represents a gradient boosted trees model.

param: algo algorithm for the ensemble model, either Classification or Regression param: trees tree ensembles param: treeWeights tree ensemble weights

See Also:

  • Constructor Details

    • GradientBoostedTreesModel

      public GradientBoostedTreesModel(scala.Enumeration.Value algo, DecisionTreeModel[] trees, double[] treeWeights)

  • Method Details

    • computeInitialPredictionAndError

      public static RDD<scala.Tuple2<Object,Object>> computeInitialPredictionAndError(RDD<LabeledPoint> data, double initTreeWeight, DecisionTreeModel initTree, Loss loss)
      Compute the initial predictions and errors for a dataset for the first iteration of gradient boosting.
      Parameters:
      data - : training data.
      initTreeWeight - : learning rate assigned to the first tree.
      initTree - : first DecisionTreeModel.
      loss - : evaluation metric.
      Returns:
      an RDD with each element being a zip of the prediction and error corresponding to every sample.

    • updatePredictionError

      public static RDD<scala.Tuple2<Object,Object>> updatePredictionError(RDD<LabeledPoint> data, RDD<scala.Tuple2<Object,Object>> predictionAndError, double treeWeight, DecisionTreeModel tree, Loss loss)
      Update a zipped predictionError RDD (as obtained with computeInitialPredictionAndError)
      Parameters:
      data - : training data.
      predictionAndError - : predictionError RDD
      treeWeight - : Learning rate.
      tree - : Tree using which the prediction and error should be updated.
      loss - : evaluation metric.
      Returns:
      an RDD with each element being a zip of the prediction and error corresponding to each sample.

    • load

      public static GradientBoostedTreesModel load(SparkContext sc, String path)
      Parameters:
      sc - Spark context used for loading model files.
      path - Path specifying the directory to which the model was saved.
      Returns:
      Model instance

    • algo

      public scala.Enumeration.Value algo()
      Overrides:
      algo in class org.apache.spark.mllib.tree.model.TreeEnsembleModel

    • trees

      public DecisionTreeModel[] trees()
      Overrides:
      trees in class org.apache.spark.mllib.tree.model.TreeEnsembleModel

    • treeWeights

      public double[] treeWeights()
      Overrides:
      treeWeights in class org.apache.spark.mllib.tree.model.TreeEnsembleModel

    • save

      public void save(SparkContext sc, String path)
      Description copied from interface: Saveable
      Save this model to the given path.

      This saves: - human-readable (JSON) model metadata to path/metadata/ - Parquet formatted data to path/data/

      The model may be loaded using Loader.load.

      Specified by:
      save in interface Saveable
      Parameters:
      sc - Spark context used to save model data.
      path - Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    • evaluateEachIteration

      public double[] evaluateEachIteration(RDD<LabeledPoint> data, Loss loss)
      Method to compute error or loss for every iteration of gradient boosting.
      Parameters:
      data - RDD of LabeledPoint
      loss - evaluation metric.
      Returns:
      an array with index i having the losses or errors for the ensemble containing the first i+1 trees