Package weka.classifiers.trees

Class RandomTree

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.trees.RandomTree
All Implemented Interfaces:
Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, PartitionGenerator, Randomizable, RevisionHandler, WeightedInstancesHandler
public class RandomTree extends AbstractClassifier implements OptionHandler, WeightedInstancesHandler, Randomizable, Drawable, PartitionGenerator
Class for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning. Also has an option to allow estimation of class probabilities (or target mean in the regression case) based on a hold-out set (backfitting).

Valid options are: 

 -K <number of attributes>
  Number of attributes to randomly investigate. (default 0)
  (<1 = int(log_2(#predictors)+1)).
 
 -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 1)
 
 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 
 -S <num>
  Seed for random number generator.
  (default 1)
 
 -depth <num>
  The maximum depth of the tree, 0 for unlimited.
  (default 0)
 
 -N <num>
  Number of folds for backfitting (default 0, no backfitting).
 
 -U
  Allow unclassified instances.
 
 -B
  Break ties randomly when several attributes look equally good.
 
 -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
 -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
 -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).
Version:
$Revision: 15519 $
Author:
Eibe Frank (eibe@cs.waikato.ac.nz), Richard Kirkby (rkirkby@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • RandomTree

      public RandomTree()

  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing classifier
      Returns:
      a description suitable for displaying in the explorer/experimenter gui

    • getImpurityDecreases

      public double[][] getImpurityDecreases()
      Get the array of impurity decrease/gain sums
      Returns:
      the array of impurity decrease/gain sums

    • setComputeImpurityDecreases

      @ProgrammaticProperty public void setComputeImpurityDecreases(boolean computeImpurityDecreases)
      Set whether to compute/store impurity decreases for variable importance in RandomForest
      Parameters:
      computeImpurityDecreases - true to compute and store impurity decrease values for splitting attributes

    • getComputeImpurityDecreases

      public boolean getComputeImpurityDecreases()
      Get whether to compute/store impurity decreases for variable importance in RandomForest
      Returns:
      true to compute and store impurity decrease values for splitting attributes

    • minNumTipText

      public String minNumTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getMinNum

      public double getMinNum()
      Get the value of MinNum.
      Returns:
      Value of MinNum.

    • setMinNum

      public void setMinNum(double newMinNum)
      Set the value of MinNum.
      Parameters:
      newMinNum - Value to assign to MinNum.

    • minVariancePropTipText

      public String minVariancePropTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getMinVarianceProp

      public double getMinVarianceProp()
      Get the value of MinVarianceProp.
      Returns:
      Value of MinVarianceProp.

    • setMinVarianceProp

      public void setMinVarianceProp(double newMinVarianceProp)
      Set the value of MinVarianceProp.
      Parameters:
      newMinVarianceProp - Value to assign to MinVarianceProp.

    • KValueTipText

      public String KValueTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getKValue

      public int getKValue()
      Get the value of K.
      Returns:
      Value of K.

    • setKValue

      public void setKValue(int k)
      Set the value of K.
      Parameters:
      k - Value to assign to K.

    • seedTipText

      public String seedTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setSeed

      public void setSeed(int seed)
      Set the seed for random number generation.
      Specified by:
      setSeed in interface Randomizable
      Parameters:
      seed - the seed

    • getSeed

      public int getSeed()
      Gets the seed for the random number generations
      Specified by:
      getSeed in interface Randomizable
      Returns:
      the seed for the random number generation

    • maxDepthTipText

      public String maxDepthTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getMaxDepth

      public int getMaxDepth()
      Get the maximum depth of trh tree, 0 for unlimited.
      Returns:
      the maximum depth.

    • setMaxDepth

      public void setMaxDepth(int value)
      Set the maximum depth of the tree, 0 for unlimited.
      Parameters:
      value - the maximum depth.

    • numFoldsTipText

      public String numFoldsTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getNumFolds

      public int getNumFolds()
      Get the value of NumFolds.
      Returns:
      Value of NumFolds.

    • setNumFolds

      public void setNumFolds(int newNumFolds)
      Set the value of NumFolds.
      Parameters:
      newNumFolds - Value to assign to NumFolds.

    • allowUnclassifiedInstancesTipText

      public String allowUnclassifiedInstancesTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getAllowUnclassifiedInstances

      public boolean getAllowUnclassifiedInstances()
      Gets whether tree is allowed to abstain from making a prediction.
      Returns:
      true if tree is allowed to abstain from making a prediction.

    • setAllowUnclassifiedInstances

      public void setAllowUnclassifiedInstances(boolean newAllowUnclassifiedInstances)
      Set the value of AllowUnclassifiedInstances.
      Parameters:
      newAllowUnclassifiedInstances - true if tree is allowed to abstain from making a prediction

    • breakTiesRandomlyTipText

      public String breakTiesRandomlyTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getBreakTiesRandomly

      public boolean getBreakTiesRandomly()
      Get whether to break ties randomly.
      Returns:
      true if ties are to be broken randomly.

    • setBreakTiesRandomly

      public void setBreakTiesRandomly(boolean newBreakTiesRandomly)
      Set whether to break ties randomly.
      Parameters:
      newBreakTiesRandomly - true if ties are to be broken randomly

    • listOptions

      public Enumeration<Option> listOptions()
      Lists the command-line options for this classifier.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class AbstractClassifier
      Returns:
      an enumeration over all possible options

    • getOptions

      public String[] getOptions()
      Gets options from this classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class AbstractClassifier
      Returns:
      the options for the current setup

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are: 

       -K <number of attributes>
  Number of attributes to randomly investigate. (default 0)
  (<1 = int(log_2(#predictors)+1)).
 
       -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 1)
 
       -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).
 
       -S <num>
  Seed for random number generator.
  (default 1)
 
       -depth <num>
  The maximum depth of the tree, 0 for unlimited.
  (default 0)
 
       -N <num>
  Number of folds for backfitting (default 0, no backfitting).
 
       -U
  Allow unclassified instances.
 
       -B
  Break ties randomly when several attributes look equally good.
 
       -output-debug-info
  If set, classifier is run in debug mode and
  may output additional info to the console
 
       -do-not-check-capabilities
  If set, classifier capabilities are not checked before classifier is built
  (use with caution).
 
       -num-decimal-places
  The number of decimal places for the output of numbers in the model (default 2).
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class AbstractClassifier
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class AbstractClassifier
      Returns:
      the capabilities of this classifier
      See Also:

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Builds classifier.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      data - the data to train with
      Throws:
      Exception - if something goes wrong or the data doesn't fit

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Computes class distribution of an instance using the tree.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      Parameters:
      instance - the instance to compute the distribution for
      Returns:
      the computed class probabilities
      Throws:
      Exception - if computation fails

    • toString

      public String toString()
      Outputs the decision tree.
      Overrides:
      toString in class Object
      Returns:
      a string representation of the classifier

    • graph

      public String graph() throws Exception
      Returns graph describing the tree.
      Specified by:
      graph in interface Drawable
      Returns:
      the graph describing the tree
      Throws:
      Exception - if graph can't be computed

    • graphType

      public int graphType()
      Returns the type of graph this classifier represents.
      Specified by:
      graphType in interface Drawable
      Returns:
      Drawable.TREE

    • generatePartition

      public void generatePartition(Instances data) throws Exception
      Builds the classifier to generate a partition.
      Specified by:
      generatePartition in interface PartitionGenerator
      Throws:
      Exception

    • getMembershipValues

      public double[] getMembershipValues(Instance instance) throws Exception
      Computes array that indicates node membership. Array locations are allocated based on breadth-first exploration of the tree.
      Specified by:
      getMembershipValues in interface PartitionGenerator
      Throws:
      Exception

    • numElements

      public int numElements() throws Exception
      Returns the number of elements in the partition.
      Specified by:
      numElements in interface PartitionGenerator
      Throws:
      Exception

    • main

      public static void main(String[] argv)
      Main method for this class.
      Parameters:
      argv - the commandline parameters