org.apache.spark.mllib.tree

Class DecisionTree

java.lang.Object

org.apache.spark.mllib.tree.DecisionTree

All Implemented Interfaces:

java.io.Serializable, Logging

public class DecisionTree
extends java.lang.Object
implements scala.Serializable, Logging

:: Experimental :: A class which implements a decision tree learning algorithm for classification and regression. It supports both continuous and categorical features. param: strategy The configuration parameters for the tree algorithm which specify the type of algorithm (classification, regression, etc.), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
DecisionTree(Strategy strategy)

Method Summary

Methods

Modifier and Type	Method and Description
protected static scala.Tuple2<Split[][],org.apache.spark.mllib.tree.model.Bin[][]>	findSplitsBins(RDD<LabeledPoint> input, org.apache.spark.mllib.tree.impl.DecisionTreeMetadata metadata) Returns splits and bins for decision tree calculation.
DecisionTreeModel	run(RDD<LabeledPoint> input) Method to train a decision tree model over an RDD
static DecisionTreeModel	train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth) Method to train a decision tree model.
static DecisionTreeModel	train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses) Method to train a decision tree model.
static DecisionTreeModel	train(RDD<LabeledPoint> input, scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, scala.Enumeration.Value quantileCalculationStrategy, scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeaturesInfo) Method to train a decision tree model.
static DecisionTreeModel	train(RDD<LabeledPoint> input, Strategy strategy) Method to train a decision tree model.
static DecisionTreeModel	trainClassifier(JavaRDD<LabeledPoint> input, int numClasses, java.util.Map<java.lang.Integer,java.lang.Integer> categoricalFeaturesInfo, java.lang.String impurity, int maxDepth, int maxBins) Java-friendly API for DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int)
static DecisionTreeModel	trainClassifier(RDD<LabeledPoint> input, int numClasses, scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeaturesInfo, java.lang.String impurity, int maxDepth, int maxBins) Method to train a decision tree model for binary or multiclass classification.
static DecisionTreeModel	trainRegressor(JavaRDD<LabeledPoint> input, java.util.Map<java.lang.Integer,java.lang.Integer> categoricalFeaturesInfo, java.lang.String impurity, int maxDepth, int maxBins) Java-friendly API for DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int)
static DecisionTreeModel	trainRegressor(RDD<LabeledPoint> input, scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeaturesInfo, java.lang.String impurity, int maxDepth, int maxBins) Method to train a decision tree model for regression.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

DecisionTree

public DecisionTree(Strategy strategy)

Method Detail

train

public static DecisionTreeModel train(RDD<LabeledPoint> input,
                      Strategy strategy)

Method to train a decision tree model. The method supports binary and multiclass classification and regression.

Note: Using DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) and DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) is recommended to clearly separate classification and regression.

Parameters:

input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

strategy - The configuration parameters for the tree algorithm which specify the type of algorithm (classification, regression, etc.), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.

Returns:

DecisionTreeModel that can be used for prediction

train

public static DecisionTreeModel train(RDD<LabeledPoint> input,
                      scala.Enumeration.Value algo,
                      Impurity impurity,
                      int maxDepth)

Method to train a decision tree model. The method supports binary and multiclass classification and regression.

Note: Using DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) and DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) is recommended to clearly separate classification and regression.

Parameters:

input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

algo - algorithm, classification or regression

impurity - impurity criterion used for information gain calculation

maxDepth - Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

Returns:

DecisionTreeModel that can be used for prediction

train

public static DecisionTreeModel train(RDD<LabeledPoint> input,
                      scala.Enumeration.Value algo,
                      Impurity impurity,
                      int maxDepth,
                      int numClasses)

Method to train a decision tree model. The method supports binary and multiclass classification and regression.

Note: Using DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) and DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) is recommended to clearly separate classification and regression.

Parameters:

input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

algo - algorithm, classification or regression

impurity - impurity criterion used for information gain calculation

maxDepth - Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

numClasses - number of classes for classification. Default value of 2.

Returns:

DecisionTreeModel that can be used for prediction

train

public static DecisionTreeModel train(RDD<LabeledPoint> input,
                      scala.Enumeration.Value algo,
                      Impurity impurity,
                      int maxDepth,
                      int numClasses,
                      int maxBins,
                      scala.Enumeration.Value quantileCalculationStrategy,
                      scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeaturesInfo)

Method to train a decision tree model. The method supports binary and multiclass classification and regression.

Note: Using DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) and DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int) is recommended to clearly separate classification and regression.

Parameters:

input - Training dataset: RDD of LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.

algo - classification or regression

impurity - criterion used for information gain calculation

maxDepth - Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

numClasses - number of classes for classification. Default value of 2.

maxBins - maximum number of bins used for splitting features

quantileCalculationStrategy - algorithm for calculating quantiles

categoricalFeaturesInfo - Map storing arity of categorical features. E.g., an entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

Returns:

DecisionTreeModel that can be used for prediction

trainClassifier

public static DecisionTreeModel trainClassifier(RDD<LabeledPoint> input,
                                int numClasses,
                                scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeaturesInfo,
                                java.lang.String impurity,
                                int maxDepth,
                                int maxBins)

Method to train a decision tree model for binary or multiclass classification.

Parameters:

input - Training dataset: RDD of LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}.

numClasses - number of classes for classification.

categoricalFeaturesInfo - Map storing arity of categorical features. E.g., an entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

impurity - Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy".

maxDepth - Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (suggested value: 5)

maxBins - maximum number of bins used for splitting features (suggested value: 32)

Returns:

DecisionTreeModel that can be used for prediction

trainClassifier

public static DecisionTreeModel trainClassifier(JavaRDD<LabeledPoint> input,
                                int numClasses,
                                java.util.Map<java.lang.Integer,java.lang.Integer> categoricalFeaturesInfo,
                                java.lang.String impurity,
                                int maxDepth,
                                int maxBins)

Java-friendly API for DecisionTree$.trainClassifier(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, int, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int)

Parameters:

input - (undocumented)

numClasses - (undocumented)

categoricalFeaturesInfo - (undocumented)

impurity - (undocumented)

maxDepth - (undocumented)

maxBins - (undocumented)

Returns:

(undocumented)

trainRegressor

public static DecisionTreeModel trainRegressor(RDD<LabeledPoint> input,
                               scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeaturesInfo,
                               java.lang.String impurity,
                               int maxDepth,
                               int maxBins)

Method to train a decision tree model for regression.

Parameters:

input - Training dataset: RDD of LabeledPoint. Labels are real numbers.

categoricalFeaturesInfo - Map storing arity of categorical features. E.g., an entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

impurity - Criterion used for information gain calculation. Supported values: "variance".

maxDepth - Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (suggested value: 5)

maxBins - maximum number of bins used for splitting features (suggested value: 32)

Returns:

DecisionTreeModel that can be used for prediction

trainRegressor

public static DecisionTreeModel trainRegressor(JavaRDD<LabeledPoint> input,
                               java.util.Map<java.lang.Integer,java.lang.Integer> categoricalFeaturesInfo,
                               java.lang.String impurity,
                               int maxDepth,
                               int maxBins)

Java-friendly API for DecisionTree$.trainRegressor(org.apache.spark.rdd.RDD<org.apache.spark.mllib.regression.LabeledPoint>, scala.collection.immutable.Map<java.lang.Object, java.lang.Object>, java.lang.String, int, int)

Parameters:

input - (undocumented)

categoricalFeaturesInfo - (undocumented)

impurity - (undocumented)

maxDepth - (undocumented)

maxBins - (undocumented)

Returns:

(undocumented)

findSplitsBins

protected static scala.Tuple2<Split[][],org.apache.spark.mllib.tree.model.Bin[][]> findSplitsBins(RDD<LabeledPoint> input,
                                                                               org.apache.spark.mllib.tree.impl.DecisionTreeMetadata metadata)

Returns splits and bins for decision tree calculation. Continuous and categorical features are handled differently.

Continuous features: For each feature, there are numBins - 1 possible splits representing the possible binary decisions at each node in the tree. This finds locations (feature values) for splits using a subsample of the data.

Categorical features: For each feature, there is 1 bin per split. Splits and bins are handled in 2 ways: (a) "unordered features" For multiclass classification with a low-arity feature (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits), the feature is split based on subsets of categories. (b) "ordered features" For regression and binary classification, and for multiclass classification with a high-arity feature, there is one bin per category.

Parameters:

input - Training data: RDD of LabeledPoint

metadata - Learning and dataset metadata

Returns:

A tuple of (splits, bins). Splits is an Array of Split of size (numFeatures, numSplits). Bins is an Array of Bin of size (numFeatures, numBins).

run

public DecisionTreeModel run(RDD<LabeledPoint> input)

Method to train a decision tree model over an RDD

Parameters:

input - Training data: RDD of LabeledPoint

Returns:

DecisionTreeModel that can be used for prediction