RandomTree (interruptible-weka 0.1.2 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.trees.RandomTree

All Implemented Interfaces:: java.io.Serializable, java.lang.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).

See Also:: Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

class RandomTree.Tree
The inner class for dealing with the tree.

Nested Classes
Modifier and Type	Class and Description
`class`	`RandomTree.Tree` The inner class for dealing with the tree.

Field Summary

Fields
Modifier and Type	Field and Description
`protected boolean`	`m_AllowUnclassifiedInstances` Whether unclassified instances are allowed
`protected boolean`	`m_BreakTiesRandomly` Whether to break ties randomly.
`protected boolean`	`m_computeImpurityDecreases` Whether to store the impurity decrease/gain sum
`protected double[][]`	`m_impurityDecreasees` Indexed by attribute, each two element array contains impurity decrease/gain sum in first element and count in the second
`protected Instances`	`m_Info` The header information.
`protected int`	`m_KValue` The number of attributes considered for a split.
`protected int`	`m_MaxDepth` The maximum depth of the tree (0 = unlimited)
`protected double`	`m_MinNum` Minimum number of instances for leaf.
`protected double`	`m_MinVarianceProp` The minimum proportion of the total variance (over all the data) required for split.
`protected int`	`m_NumFolds` Determines how much data is used for backfitting
`protected int`	`m_randomSeed` The random seed to use.
`protected RandomTree.Tree`	`m_Tree` The Tree object
`protected Classifier`	`m_zeroR` a ZeroR model in case no model can be built from the data

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Fields inherited from interface weka.core.Drawable
BayesNet, Newick, NOT_DRAWABLE, TREE

Constructor Summary

Constructors
Constructor and Description

RandomTree()

Constructors
Constructor and Description
`RandomTree()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String`	`allowUnclassifiedInstancesTipText()` Returns the tip text for this property
`java.lang.String`	`breakTiesRandomlyTipText()` Returns the tip text for this property
`void`	`buildClassifier(Instances data)` Builds classifier.
`double[]`	`distributionForInstance(Instance instance)` Computes class distribution of an instance using the tree.
`void`	`generatePartition(Instances data)` Builds the classifier to generate a partition.
`boolean`	`getAllowUnclassifiedInstances()` Gets whether tree is allowed to abstain from making a prediction.
`boolean`	`getBreakTiesRandomly()` Get whether to break ties randomly.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getComputeImpurityDecreases()` Get whether to compute/store impurity decreases for variable importance in RandomForest
`double[][]`	`getImpurityDecreases()` Get the array of impurity decrease/gain sums
`int`	`getKValue()` Get the value of K.
`RandomTree.Tree`	`getM_Tree()`
`int`	`getMaxDepth()` Get the maximum depth of trh tree, 0 for unlimited.
`double[]`	`getMembershipValues(Instance instance)` Computes array that indicates node membership.
`double`	`getMinNum()` Get the value of MinNum.
`double`	`getMinVarianceProp()` Get the value of MinVarianceProp.
`int`	`getNumFolds()` Get the value of NumFolds.
`java.lang.String[]`	`getOptions()` Gets options from this classifier.
`int`	`getSeed()` Gets the seed for the random number generations
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`java.lang.String`	`graph()` Returns graph describing the tree.
`int`	`graphType()` Returns the type of graph this classifier represents.
`java.lang.String`	`KValueTipText()` Returns the tip text for this property
`java.util.Enumeration<Option>`	`listOptions()` Lists the command-line options for this classifier.
`static void`	`main(java.lang.String[] argv)` Main method for this class.
`java.lang.String`	`maxDepthTipText()` Returns the tip text for this property
`java.lang.String`	`minNumTipText()` Returns the tip text for this property
`java.lang.String`	`minVariancePropTipText()` Returns the tip text for this property
`int`	`numElements()` Returns the number of elements in the partition.
`java.lang.String`	`numFoldsTipText()` Returns the tip text for this property
`java.lang.String`	`seedTipText()` Returns the tip text for this property
`void`	`setAllowUnclassifiedInstances(boolean newAllowUnclassifiedInstances)` Set the value of AllowUnclassifiedInstances.
`void`	`setBreakTiesRandomly(boolean newBreakTiesRandomly)` Set whether to break ties randomly.
`void`	`setComputeImpurityDecreases(boolean computeImpurityDecreases)` Set whether to compute/store impurity decreases for variable importance in RandomForest
`void`	`setKValue(int k)` Set the value of K.
`void`	`setMaxDepth(int value)` Set the maximum depth of the tree, 0 for unlimited.
`void`	`setMinNum(double newMinNum)` Set the value of MinNum.
`void`	`setMinVarianceProp(double newMinVarianceProp)` Set the value of MinVarianceProp.
`void`	`setNumFolds(int newNumFolds)` Set the value of NumFolds.
`void`	`setOptions(java.lang.String[] options)` Parses a given list of options.
`void`	`setSeed(int seed)` Set the seed for random number generation.
`protected static double`	`singleVariance(double s, double sS, double weight)` Computes the variance for a single set
`java.lang.String`	`toString()` Outputs the decision tree.
`protected static double`	`variance(double[] s, double[] sS, double[] sumOfWeights)` Computes variance for subsets.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_Tree
```
protected RandomTree.Tree m_Tree
```
    The Tree object
  - m_Info
```
protected Instances m_Info
```
    The header information.
  - m_MinNum
```
protected double m_MinNum
```
    Minimum number of instances for leaf.
  - m_KValue
```
protected int m_KValue
```
    The number of attributes considered for a split.
  - m_randomSeed
```
protected int m_randomSeed
```
    The random seed to use.
  - m_MaxDepth
```
protected int m_MaxDepth
```
    The maximum depth of the tree (0 = unlimited)
  - m_NumFolds
```
protected int m_NumFolds
```
    Determines how much data is used for backfitting
  - m_AllowUnclassifiedInstances
```
protected boolean m_AllowUnclassifiedInstances
```
    Whether unclassified instances are allowed
  - m_BreakTiesRandomly
```
protected boolean m_BreakTiesRandomly
```
    Whether to break ties randomly.
  - m_zeroR
```
protected Classifier m_zeroR
```
    a ZeroR model in case no model can be built from the data
  - m_MinVarianceProp
```
protected double m_MinVarianceProp
```
    The minimum proportion of the total variance (over all the data) required for split.
  - m_computeImpurityDecreases
```
protected boolean m_computeImpurityDecreases
```
    Whether to store the impurity decrease/gain sum
  - m_impurityDecreasees
```
protected double[][] m_impurityDecreasees
```
    Indexed by attribute, each two element array contains impurity decrease/gain sum in first element and count in the second
- Constructor Detail
  - RandomTree
```
public RandomTree()
```
- Method Detail
  - globalInfo
```
public java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.util.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 java.lang.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(java.lang.String[] options)
                throws java.lang.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:
    
    java.lang.Exception - if an option is not supported
  - getCapabilities
```
public Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
    
    See Also:
    
    Capabilities
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws java.lang.Exception
```
    Builds classifier.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - the data to train with
    
    Throws:
    
    java.lang.Exception - if something goes wrong or the data doesn't fit
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws java.lang.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:
    
    java.lang.Exception - if computation fails
  - toString
```
public java.lang.String toString()
```
    Outputs the decision tree.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    a string representation of the classifier
  - graph
```
public java.lang.String graph()
                       throws java.lang.Exception
```
    Returns graph describing the tree.
    
    Specified by:
    
    graph in interface Drawable
    
    Returns:
    
    the graph describing the tree
    
    Throws:
    
    java.lang.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 java.lang.Exception
```
    Builds the classifier to generate a partition.
    
    Specified by:
    
    generatePartition in interface PartitionGenerator
    
    Throws:
    
    java.lang.Exception
  - getMembershipValues
```
public double[] getMembershipValues(Instance instance)
                             throws java.lang.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:
    
    java.lang.Exception
  - numElements
```
public int numElements()
                throws java.lang.Exception
```
    Returns the number of elements in the partition.
    
    Specified by:
    
    numElements in interface PartitionGenerator
    
    Throws:
    
    java.lang.Exception
  - variance
```
protected static double variance(double[] s,
                                 double[] sS,
                                 double[] sumOfWeights)
```
    Computes variance for subsets.
    
    Parameters:
    
    s -
    
    sS -
    
    sumOfWeights -
    
    Returns:
    
    the variance
  - singleVariance
```
protected static double singleVariance(double s,
                                       double sS,
                                       double weight)
```
    Computes the variance for a single set
    
    Parameters:
    
    s -
    
    sS -
    
    weight - the weight
    
    Returns:
    
    the variance
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for this class.
    
    Parameters:
    
    argv - the commandline parameters
  - getM_Tree
```
public RandomTree.Tree getM_Tree()
```

Class RandomTree

Nested Class Summary

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface weka.core.Drawable

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_Tree

m_Info

m_MinNum

m_KValue

m_randomSeed

m_MaxDepth

m_NumFolds

m_AllowUnclassifiedInstances

m_BreakTiesRandomly

m_zeroR

m_MinVarianceProp

m_computeImpurityDecreases

m_impurityDecreasees

Constructor Detail

RandomTree

Method Detail

globalInfo

getImpurityDecreases

setComputeImpurityDecreases

getComputeImpurityDecreases

minNumTipText

getMinNum

setMinNum

minVariancePropTipText

getMinVarianceProp

setMinVarianceProp

KValueTipText

getKValue

setKValue

seedTipText

setSeed

getSeed

maxDepthTipText

getMaxDepth

setMaxDepth

numFoldsTipText

getNumFolds

setNumFolds

allowUnclassifiedInstancesTipText

getAllowUnclassifiedInstances

setAllowUnclassifiedInstances

breakTiesRandomlyTipText

getBreakTiesRandomly

setBreakTiesRandomly

listOptions

getOptions

setOptions

getCapabilities

buildClassifier

distributionForInstance

toString

graph

graphType

generatePartition

getMembershipValues

numElements

variance

singleVariance

main

getM_Tree