org.apache.spark.mllib.tree.model
Class GradientBoostedTreesModel

Object
  org.apache.spark.mllib.tree.model.GradientBoostedTreesModel

All Implemented Interfaces:

java.io.Serializable, Saveable

public class GradientBoostedTreesModel
extends Object
implements Saveable

:: Experimental :: 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:

Serialized Form

Constructor Summary

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

Method Summary

scala.Enumeration.Value	algo()
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.
double[]	evaluateEachIteration(RDD<LabeledPoint> data, Loss loss) Method to compute error or loss for every iteration of gradient boosting.
static GradientBoostedTreesModel	load(SparkContext sc, String path)
int	numTrees() Get number of trees in ensemble.
JavaRDD<Double>	predict(JavaRDD<Vector> features) Java-friendly version of TreeEnsembleModel.predict(org.apache.spark.mllib.linalg.Vector).
RDD<Object>	predict(RDD<Vector> features) Predict values for the given data set.
double	predict(Vector features) Predict values for a single data point using the model trained.
void	save(SparkContext sc, String path) Save this model to the given path.
String	toDebugString() Print the full model to a string.
String	toString() Print a summary of the model.
int	totalNumNodes() Get total number of nodes, summed over all trees in the ensemble.
DecisionTreeModel[]	trees()
double[]	treeWeights()
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)

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

GradientBoostedTreesModel

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

Method Detail

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:

a 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:

a 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)

algo

public scala.Enumeration.Value algo()

trees

public DecisionTreeModel[] trees()

treeWeights

public double[] treeWeights()

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

predict

public double predict(Vector features)

Predict values for a single data point using the model trained.

Parameters:

features - array representing a single data point

Returns:

predicted category from the trained model

predict

public RDD<Object> predict(RDD<Vector> features)

Predict values for the given data set.

Parameters:

features - RDD representing data points to be predicted

Returns:

RDD[Double] where each entry contains the corresponding prediction

predict

public JavaRDD<Double> predict(JavaRDD<Vector> features)

Java-friendly version of TreeEnsembleModel.predict(org.apache.spark.mllib.linalg.Vector).

Parameters:

features - (undocumented)

Returns:

(undocumented)

toString

public String toString()

Print a summary of the model.

Overrides:

toString in class Object

Returns:

(undocumented)

toDebugString

public String toDebugString()

Print the full model to a string.

Returns:

(undocumented)

numTrees

public int numTrees()

Get number of trees in ensemble.

Returns:

(undocumented)

totalNumNodes

public int totalNumNodes()

Get total number of nodes, summed over all trees in the ensemble.

Returns:

(undocumented)