FastRandomForest (Fiji Javadocs 0-SNAPSHOT API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - hr.irb.fastRandomForest.FastRandomForest

All Implemented Interfaces:

Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
```
public class FastRandomForest
extends AbstractClassifier
implements OptionHandler, Randomizable, WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler
```
Based on the "weka.classifiers.trees.RandomForest" class, revision 1.12, by Richard Kirkby, with minor modifications:
- uses FastRfBagger with FastRandomTree, instead of Bagger with RandomTree.
- stores dataset header (instead of every Tree storing its own header)
- checks if only ZeroR model is possible (instead of each Tree checking)
- added "-threads" option
Class for constructing a forest of random trees.

For more information see:

Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.

BibTeX:
```
 @article{Breiman2001,
    author = {Leo Breiman},
    journal = {Machine Learning},
    number = {1},
    pages = {5-32},
    title = {Random Forests},
    volume = {45},
    year = {2001}
 }
 
```
Valid options are:
```
 -I <number of trees>
  Number of trees to build.
```
```
 -K <number of features>
  Number of features to consider (<1=int(logM+1)).
```
```
 -S
  Seed for random number generator.
  (default 1)
```
```
 -depth <num>
  The maximum depth of the trees, 0 for unlimited.
  (default 0)
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
Version:

$Revision: 0.99$

Author:

Richard Kirkby (rkirkby@cs.waikato.ac.nz) - original code, Fran Supek (fran.supek[AT]irb.hr) - adapted code

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected hr.irb.fastRandomForest.FastRfBagging`	`m_bagger` The bagger.
`protected Instances`	`m_Info` The header information.
`protected int`	`m_KValue` Final number of features that were considered in last build.
`protected int`	`m_MaxDepth` The maximum depth of the trees (0 = unlimited)
`protected int`	`m_numFeatures` Number of features to consider in random feature selection.
`protected int`	`m_NumThreads` Number of simultaneous threads to use in computation (0 = autodetect).
`protected int`	`m_numTrees` Number of trees in forest.
`protected int`	`m_randomSeed` The random seed.
`protected AbstractClassifier`	`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

Constructor Summary

Constructors
Constructor and Description

FastRandomForest()

Constructors
Constructor and Description
`FastRandomForest()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances data)` Builds a classifier for a set of instances.
`double[]`	`distributionForInstance(Instance instance)` Returns the class probability distribution for an instance.
`Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names.
`Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`boolean`	`getComputeImportances()`
`double[]`	`getFeatureImportances()`
`int`	`getMaxDepth()` Get the maximum depth of trh tree, 0 for unlimited.
`double`	`getMeasure(String additionalMeasureName)` Returns the value of the named measure.
`int`	`getNumFeatures()` Get the number of features used in random selection.
`int`	`getNumThreads()` Get the number of simultaneous threads used in training, 0 for autodetect.
`int`	`getNumTrees()` Get the value of numTrees.
`String[]`	`getOptions()` Gets the current settings of the forest.
`String`	`getRevision()`
`int`	`getSeed()` Gets the seed for the random number generations
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`String`	`globalInfo()` Returns a string describing classifier
`Enumeration`	`listOptions()` Returns an enumeration describing the available options.
`static void`	`main(String[] argv)` Main method for this class.
`String`	`maxDepthTipText()` Returns the tip text for this property
`double`	`measureOutOfBagError()` Gets the out of bag error that was calculated as the classifier was built.
`String`	`numFeaturesTipText()` Returns the tip text for this property
`String`	`numThreadsTipText()` Returns the tip text for this property
`String`	`numTreesTipText()` Returns the tip text for this property
`String`	`seedTipText()` Returns the tip text for this property
`void`	`setComputeImportances(boolean computeImportances)`
`void`	`setMaxDepth(int value)` Set the maximum depth of the tree, 0 for unlimited.
`void`	`setNumFeatures(int newNumFeatures)` Set the number of features to use in random selection.
`void`	`setNumThreads(int value)` Set the number of simultaneous threads used in training, 0 for autodetect.
`void`	`setNumTrees(int newNumTrees)` Set the value of numTrees.
`void`	`setOptions(String[] options)` Parses a given list of options.
`void`	`setSeed(int seed)` Set the seed for random number generation.
`String`	`toString()` Outputs a description of this classifier.

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

Methods inherited from interface weka.core.OptionHandler
makeCopy

- Field Detail
  - m_numTrees
```
protected int m_numTrees
```
    Number of trees in forest.
  - m_numFeatures
```
protected int m_numFeatures
```
    Number of features to consider in random feature selection. If less than 1 will use int(logM+1) )
  - m_randomSeed
```
protected int m_randomSeed
```
    The random seed.
  - m_KValue
```
protected int m_KValue
```
    Final number of features that were considered in last build.
  - m_NumThreads
```
protected int m_NumThreads
```
    Number of simultaneous threads to use in computation (0 = autodetect).
  - m_bagger
```
protected hr.irb.fastRandomForest.FastRfBagging m_bagger
```
    The bagger.
  - m_MaxDepth
```
protected int m_MaxDepth
```
    The maximum depth of the trees (0 = unlimited)
  - m_Info
```
protected Instances m_Info
```
    The header information.
  - m_ZeroR
```
protected AbstractClassifier m_ZeroR
```
    a ZeroR model in case no model can be built from the data
- Constructor Detail
  - FastRandomForest
```
public FastRandomForest()
```
- Method Detail
  - globalInfo
```
public String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - numTreesTipText
```
public String numTreesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumTrees
```
public int getNumTrees()
```
    Get the value of numTrees.
    
    Returns:
    
    Value of numTrees.
  - setNumTrees
```
public void setNumTrees(int newNumTrees)
```
    Set the value of numTrees.
    
    Parameters:
    
    newNumTrees - Value to assign to numTrees.
  - numFeaturesTipText
```
public String numFeaturesTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumFeatures
```
public int getNumFeatures()
```
    Get the number of features used in random selection.
    
    Returns:
    
    Value of numFeatures.
  - setNumFeatures
```
public void setNumFeatures(int newNumFeatures)
```
    Set the number of features to use in random selection.
    
    Parameters:
    
    newNumFeatures - Value to assign to numFeatures.
  - 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.
  - numThreadsTipText
```
public String numThreadsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumThreads
```
public int getNumThreads()
```
    Get the number of simultaneous threads used in training, 0 for autodetect.
    
    Returns:
    
    the maximum depth.
  - setNumThreads
```
public void setNumThreads(int value)
```
    Set the number of simultaneous threads used in training, 0 for autodetect.
    
    Parameters:
    
    value - the maximum depth.
  - getComputeImportances
```
public boolean getComputeImportances()
```
    Returns:
    
    compute feature importances?
  - setComputeImportances
```
public void setComputeImportances(boolean computeImportances)
```
    Parameters:
    
    computeImportances - compute feature importances?
  - measureOutOfBagError
```
public double measureOutOfBagError()
```
    Gets the out of bag error that was calculated as the classifier was built.
    
    Returns:
    
    the out of bag error
  - enumerateMeasures
```
public Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names.
    
    Specified by:
    
    enumerateMeasures in interface AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(String additionalMeasureName)
```
    Returns the value of the named measure.
    
    Specified by:
    
    getMeasure in interface AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    IllegalArgumentException - if the named measure is not supported
  - listOptions
```
public Enumeration listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options
  - getOptions
```
public String[] getOptions()
```
    Gets the current settings of the forest.
    
    Specified by:
    
    getOptions in interface OptionHandler
    
    Overrides:
    
    getOptions in class AbstractClassifier
    
    Returns:
    
    an array of strings suitable for passing to setOptions()
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Parses a given list of options.
    Valid options are:
```
 -I <number of trees>
  Number of trees to build.
```
```
 -K <number of features>
  Number of features to consider (<1=int(logM+1)).
```
```
 -S
  Seed for random number generator.
  (default 1)
```
```
 -depth <num>
  The maximum depth of the trees, 0 for unlimited.
  (default 0)
```
```
 -threads
  Number of simultaneous threads to use.
  (default 0 = autodetect number of available cores)
```
```
 -import
  Compute and output RF feature importances (slow).
```
```
 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
```
    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 Classifier
    
    Specified by:
    
    getCapabilities in interface CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(Instances data)
                     throws Exception
```
    Builds a classifier for a set of instances.
    
    Specified by:
    
    buildClassifier in interface Classifier
    
    Parameters:
    
    data - the instances to train the classifier with
    
    Throws:
    
    Exception - if something goes wrong
  - distributionForInstance
```
public double[] distributionForInstance(Instance instance)
                                 throws Exception
```
    Returns the class probability distribution for an instance.
    
    Specified by:
    
    distributionForInstance in interface Classifier
    
    Overrides:
    
    distributionForInstance in class AbstractClassifier
    
    Parameters:
    
    instance - the instance to be classified
    
    Returns:
    
    the distribution the forest generates for the instance
    
    Throws:
    
    Exception - if computation fails
  - toString
```
public String toString()
```
    Outputs a description of this classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a string containing a description of the classifier
  - main
```
public static void main(String[] argv)
```
    Main method for this class.
    
    Parameters:
    
    argv - the options
  - getRevision
```
public String getRevision()
```
    Specified by:
    
    getRevision in interface RevisionHandler
    
    Overrides:
    
    getRevision in class AbstractClassifier
  - getFeatureImportances
```
public double[] getFeatureImportances()
```
    Returns:
    
    the feature importances or null if the importances haven't been computed

Class FastRandomForest

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface weka.core.OptionHandler

Field Detail

m_numTrees

m_numFeatures

m_randomSeed

m_KValue

m_NumThreads

m_bagger

m_MaxDepth

m_Info

m_ZeroR

Constructor Detail

FastRandomForest

Method Detail

globalInfo

getTechnicalInformation

numTreesTipText

getNumTrees

setNumTrees

numFeaturesTipText

getNumFeatures

setNumFeatures

seedTipText

setSeed

getSeed

maxDepthTipText

getMaxDepth

setMaxDepth

numThreadsTipText

getNumThreads

setNumThreads

getComputeImportances

setComputeImportances

measureOutOfBagError

enumerateMeasures

getMeasure

listOptions

getOptions

setOptions

getCapabilities

buildClassifier

distributionForInstance

toString

main

getRevision

getFeatureImportances