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

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

Constructors

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

Method Summary

Modifier and Type	Method and Description
scala.Enumeration.Value	algo()
static RDD<scala.Tuple2<Object,Object>>	computeInitialPredictionAndError(RDD<LabeledPoint> data, double initTreeWeight, DecisionTreeModel initTree, Loss loss) :: DeveloperApi :: 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)
static int	numTrees()
int	numTrees() Get number of trees in ensemble.
static JavaRDD<Double>	predict(JavaRDD<Vector> features)
JavaRDD<Double>	predict(JavaRDD<Vector> features) Java-friendly version of org.apache.spark.mllib.tree.model.TreeEnsembleModel.predict.
static RDD<Object>	predict(RDD<Vector> features)
RDD<Object>	predict(RDD<Vector> features) Predict values for the given data set.
static double	predict(Vector features)
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.
static String	toDebugString()
String	toDebugString() Print the full model to a string.
static String	toString()
String	toString() Print a summary of the model.
static int	totalNumNodes()
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) :: DeveloperApi :: 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)

:: DeveloperApi :: 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)

:: DeveloperApi :: 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

predict

public static double predict(Vector features)

predict

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

predict

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

toString

public static String toString()

toDebugString

public static String toDebugString()

numTrees

public static int numTrees()

totalNumNodes

public static int totalNumNodes()

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 org.apache.spark.mllib.tree.model.TreeEnsembleModel.predict.

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)