CC (meka 1.9.1 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.SingleClassifierEnhancer
  - - meka.classifiers.multilabel.ProblemTransformationMethod
    - - meka.classifiers.multilabel.CC

All Implemented Interfaces:

Serializable, Cloneable, MultiLabelClassifier, MultiLabelDrawable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

Direct Known Subclasses:

BCC, CC, CCp, CCUpdateable, MCC, PCC
```
public class CC
extends ProblemTransformationMethod
implements weka.core.Randomizable, weka.core.TechnicalInformationHandler, MultiLabelDrawable
```
CC.java - The Classifier Chains Method. Like BR, but label outputs become new inputs for the next classifiers in the chain.
See: Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. Classifier Chains for Multi-label Classification. Machine Learning Journal. Springer. Vol. 85(3), pp 333-359. (May 2011).
See: Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank. Classifier Chains for Multi-label Classification. In Proc. of 20th European Conference on Machine Learning (ECML 2009). Bled, Slovenia, September 2009.
Note that the code was reorganised substantially since earlier versions, to accomodate additional functionality needed for e.g., MCC, PCC.

Version:

December 2013

Author:

Jesse Read

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

protected double[] confidences

protected int[] m_Chain

protected Random m_R

protected int m_S

protected CNode[] nodes
- Fields inherited from class meka.classifiers.multilabel.ProblemTransformationMethod
  m_InstancesTemplate
- Fields inherited from class weka.classifiers.SingleClassifierEnhancer
  m_Classifier
- 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 meka.core.MultiLabelDrawable
  BayesNet, Newick, NOT_DRAWABLE, TREE

Fields
Modifier and Type	Field and Description
`protected double[]`	`confidences`
`protected int[]`	`m_Chain`
`protected Random`	`m_R`
`protected int`	`m_S`
`protected CNode[]`	`nodes`

Constructor Summary

Constructors
Constructor and Description

CC()

Constructors
Constructor and Description
`CC()`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances D)`
`double[]`	`distributionForInstance(weka.core.Instance x)`
`double[]`	`getConfidences()` GetConfidences - get the posterior probabilities of the previous prediction (after calling distributionForInstance(x)).
`int`	`getDefaultSeed()`
`String`	`getModel()` Returns a string representation of the model.
`String[]`	`getOptions()`
`int`	`getSeed()`
`weka.core.TechnicalInformation`	`getTechnicalInformation()`
`weka.core.Instance[]`	`getTransformTemplates(weka.core.Instance x)` GetTransformTemplates - pre-transform the instance x, to make things faster.
`String`	`globalInfo()` Description to display in the GUI.
`Map<Integer,String>`	`graph()` Returns a string that describes a graph representing the object.
`Map<Integer,Integer>`	`graphType()` Returns the type of graph representing the object.
`Enumeration`	`listOptions()`
`static void`	`main(String[] args)`
`protected void`	`prepareChain(int L)` Prepare a Chain.
`void`	`prepareChain(int[] chain)` Prepare a Chain.
`double[]`	`probabilityForInstance(weka.core.Instance x, double[] path)` ProbabilityForInstance - Force our way down the imposed 'path'.
`void`	`rebuildClassifier(int[] new_chain, weka.core.Instances D)` Rebuild - NOT YET IMPLEMENTED.
`int[]`	`retrieveChain()`
`double[]`	`sampleForInstance(weka.core.Instance x, Random r)` SampleForInstance.
`double[]`	`sampleForInstanceFast(weka.core.Instance[] t_, Random r)` SampleForInstance - given an Instance template for each label, and a Random.
`String`	`seedTipText()`
`void`	`setOptions(String[] options)`
`void`	`setSeed(int s)`
`String`	`toString()`
`weka.core.Instance[]`	`transformInstance(weka.core.Instance x)` TransformInstances - this function is DEPRECATED.

Methods inherited from class meka.classifiers.multilabel.ProblemTransformationMethod
defaultClassifierString, evaluation, getCapabilities, getRevision, getTemplate, makeCopies, runClassifier, testCapabilities

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, defaultClassifierOptions, getClassifier, getClassifierSpec, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier
debugTipText, getDebug, setDebug

Methods inherited from interface weka.classifiers.Classifier
classifyInstance

- Field Detail
  - nodes
```
protected CNode[] nodes
```
  - m_S
```
protected int m_S
```
  - m_R
```
protected Random m_R
```
  - m_Chain
```
protected int[] m_Chain
```
  - confidences
```
protected double[] confidences
```
- Constructor Detail
  - CC
```
public CC()
```
- Method Detail
  - prepareChain
```
protected void prepareChain(int L)
```
    Prepare a Chain. One of the following:
    - Use pre-set chain. If there is none, then
    - Use default chain (1,2,...,L). Unless a different random seed has been set, then
    - Use a random chain.
    
    Parameters:
    
    L - number of labels
  - prepareChain
```
public void prepareChain(int[] chain)
```
    Prepare a Chain. Set the specified 'chain'. It must contain all indices [0,...,L-1] (but in any order)
    
    Parameters:
    
    chain - a specified chain
  - retrieveChain
```
public int[] retrieveChain()
```
  - buildClassifier
```
public void buildClassifier(weka.core.Instances D)
                     throws Exception
```
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Specified by:
    
    buildClassifier in class ProblemTransformationMethod
    
    Throws:
    
    Exception
  - getConfidences
```
public double[] getConfidences()
```
    GetConfidences - get the posterior probabilities of the previous prediction (after calling distributionForInstance(x)).
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance x)
                                 throws Exception
```
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Specified by:
    
    distributionForInstance in class ProblemTransformationMethod
    
    Throws:
    
    Exception
  - sampleForInstance
```
public double[] sampleForInstance(weka.core.Instance x,
                                  Random r)
                           throws Exception
```
    SampleForInstance. predict y[j] stochastically rather than deterministically (as with distributionForInstance(Instance x)).
    
    Parameters:
    
    x - test Instance
    
    r - Random <- TODO probably can use this.m_R instead
    
    Throws:
    
    Exception
  - getTransformTemplates
```
public weka.core.Instance[] getTransformTemplates(weka.core.Instance x)
                                           throws Exception
```
    GetTransformTemplates - pre-transform the instance x, to make things faster.
    
    Returns:
    
    the templates
    
    Throws:
    
    Exception
  - sampleForInstanceFast
```
public double[] sampleForInstanceFast(weka.core.Instance[] t_,
                                      Random r)
                               throws Exception
```
    SampleForInstance - given an Instance template for each label, and a Random.
    
    Parameters:
    
    t_ - Instance templates (pre-transformed) using #getTransformTemplates(x)
    
    Throws:
    
    Exception
  - transformInstance
```
public weka.core.Instance[] transformInstance(weka.core.Instance x)
                                       throws Exception
```
    TransformInstances - this function is DEPRECATED. this function preloads the instances with the correct class labels ... to make the chain much faster, but CNode does not yet have this functionality ... need to do something about this!
    
    Throws:
    
    Exception
  - probabilityForInstance
```
public double[] probabilityForInstance(weka.core.Instance x,
                                       double[] path)
                                throws Exception
```
    ProbabilityForInstance - Force our way down the imposed 'path'.
    TODO rename distributionForPath ? and simplify like distributionForInstance ?
    For example p (y=1010|x) = [0.9,0.8,0.1,0.2]. If the product = 1, this is probably the correct path!
    
    Parameters:
    
    x - test Instance
    
    path - the path we want to go down
    
    Returns:
    
    the probabilities associated with this path: [p(Y_1==path[1]|x),...,p(Y_L==path[L]|x)]
    
    Throws:
    
    Exception
  - rebuildClassifier
```
public void rebuildClassifier(int[] new_chain,
                              weka.core.Instances D)
                       throws Exception
```
    Rebuild - NOT YET IMPLEMENTED. For efficiency reasons, we may want to rebuild part of the chain. If chain[] = [1,2,3,4] and new_chain[] = [1,2,4,3] we only need to rebuild the final two links.
    
    Parameters:
    
    new_chain - the new chain
    
    D - the original training data
    
    Throws:
    
    Exception
  - getDefaultSeed
```
public int getDefaultSeed()
```
  - getSeed
```
public int getSeed()
```
    Specified by:
    
    getSeed in interface weka.core.Randomizable
  - setSeed
```
public void setSeed(int s)
```
    Specified by:
    
    setSeed in interface weka.core.Randomizable
  - seedTipText
```
public String seedTipText()
```
  - listOptions
```
public Enumeration listOptions()
```
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.SingleClassifierEnhancer
  - setOptions
```
public void setOptions(String[] options)
                throws Exception
```
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.SingleClassifierEnhancer
    
    Throws:
    
    Exception
  - getOptions
```
public String[] getOptions()
```
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.SingleClassifierEnhancer
  - globalInfo
```
public String globalInfo()
```
    Description to display in the GUI.
    
    Overrides:
    
    globalInfo in class ProblemTransformationMethod
    
    Returns:
    
    the description
  - getTechnicalInformation
```
public weka.core.TechnicalInformation getTechnicalInformation()
```
    Specified by:
    
    getTechnicalInformation in interface weka.core.TechnicalInformationHandler
  - graphType
```
public Map<Integer,Integer> graphType()
```
    Returns the type of graph representing the object.
    
    Specified by:
    
    graphType in interface MultiLabelDrawable
    
    Returns:
    
    the type of graph representing the object (label index as key)
  - graph
```
public Map<Integer,String> graph()
                          throws Exception
```
    Returns a string that describes a graph representing the object. The string should be in XMLBIF ver. 0.3 format if the graph is a BayesNet, otherwise it should be in dotty format.
    
    Specified by:
    
    graph in interface MultiLabelDrawable
    
    Returns:
    
    the graph described by a string (label index as key)
    
    Throws:
    
    Exception - if the graph can't be computed
  - getModel
```
public String getModel()
```
    Returns a string representation of the model.
    
    Specified by:
    
    getModel in interface MultiLabelClassifier
    
    Overrides:
    
    getModel in class ProblemTransformationMethod
    
    Returns:
    
    the model
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class ProblemTransformationMethod
  - main
```
public static void main(String[] args)
```

Class CC

Field Summary

Fields inherited from class meka.classifiers.multilabel.ProblemTransformationMethod

Fields inherited from class weka.classifiers.SingleClassifierEnhancer

Fields inherited from class weka.classifiers.AbstractClassifier

Fields inherited from interface meka.core.MultiLabelDrawable

Constructor Summary

Method Summary

Methods inherited from class meka.classifiers.multilabel.ProblemTransformationMethod

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface meka.classifiers.multilabel.MultiLabelClassifier

Methods inherited from interface weka.classifiers.Classifier

Field Detail

nodes

m_S

m_R

m_Chain

confidences

Constructor Detail

CC

Method Detail

prepareChain

prepareChain

retrieveChain

buildClassifier

getConfidences

distributionForInstance

sampleForInstance

getTransformTemplates

sampleForInstanceFast

transformInstance

probabilityForInstance

rebuildClassifier

getDefaultSeed

getSeed

setSeed

seedTipText

listOptions

setOptions

getOptions

globalInfo

getTechnicalInformation

graphType

graph

getModel

toString

main