com.rapidminer.operator.learner.rules
Class BestRuleInduction

java.lang.Object
  com.rapidminer.tools.AbstractObservable<Operator>
      com.rapidminer.operator.Operator
          com.rapidminer.operator.learner.AbstractLearner
              com.rapidminer.operator.learner.rules.BestRuleInduction

All Implemented Interfaces:

ConfigurationListener, PreviewListener, ResourceConsumer, CapabilityProvider, Learner, ParameterHandler, LoggingHandler, Observable<Operator>

public class BestRuleInduction
extends AbstractLearner

This operator returns the best rule regarding WRAcc using exhaustive search. Features like the incorporation of other metrics and the search for more than a single rule are prepared. The search strategy is BFS, with save pruning whenever applicable. This operator can easily be extended to support other search strategies.

Author:

Martin Scholz

Nested Class Summary

static class

BestRuleInduction.RuleWithScoreUpperBound Helper class containing a rule and an upper bound for the score.

Field Summary

protected ConjunctiveRuleModel

bestRule

Fields inherited from interface com.rapidminer.operator.learner.CapabilityProvider

PROPERTY_RAPIDMINER_GENERAL_CAPABILITIES_WARN

Constructor Summary

BestRuleInduction(OperatorDescription description)

Method Summary

protected boolean	communicateToHighscore(ConjunctiveRuleModel rule, double[] counts) Adds a rule to the set of best rules if its score is high enough.
protected ConjunctiveRuleModel	getBestRule()
protected double[]	getCounts(ConjunctiveRuleModel rule, ExampleSet exampleSet)
java.lang.Class<? extends PredictionModel>	getModelClass() This method might be overridden from subclasses in order to specify exactly which model class they use.
protected double	getOptimisticScore(double[] counts) Computes the best possible score that might be achieved by refining the rule.
java.util.List<ParameterType>	getParameterTypes() Adds the parameters "number of iterations" and "model file".
protected double	getPruningScore()
protected double	getScore(double[] counts, boolean predictPositives) Computes the WRAcc or BINOMIAL TEST FUNCTION based on p, n, and the global values P and N stored in this object.
protected void	initHighscore()
boolean	isRefinementOfPrunedRule(ConjunctiveRuleModel rule)
Model	learn(ExampleSet exampleSet) Trains a model.
boolean	supportsCapability(OperatorCapability lc) Checks for Learner capabilities.

Methods inherited from class com.rapidminer.operator.learner.AbstractLearner

canCalculateWeights, canEstimatePerformance, doWork, doWork, getEstimatedPerformance, getExampleSetInputPort, getOptimizationPerformance, getWeightCalculationError, getWeights, getWeights, onlyWarnForNonSufficientCapabilities, shouldAutoConnect, shouldCalculateWeights, shouldDeliverOptimizationPerformance, shouldEstimatePerformance

Methods inherited from class com.rapidminer.operator.Operator

acceptsInput, addError, addError, addValue, addWarning, apply, apply, assumePreconditionsSatisfied, checkAll, checkAllExcludingMetaData, checkDeprecations, checkForStop, checkIO, checkProperties, clear, clearErrorList, cloneOperator, collectErrors, createExperimentTree, createExperimentTree, createFromXML, createFromXML, createFromXML, createMarkedExperimentTree, createMarkedProcessTree, createProcessTree, createProcessTree, disconnectPorts, execute, fireUpdate, freeMemory, getAddOnlyAdditionalOutput, getApplyCount, getCompatibilityLevel, getDeliveredOutputClasses, getDeprecationInfo, getDesiredInputClasses, getDOMRepresentation, getEncoding, getErrorList, getExecutionUnit, getExperiment, getIncompatibleVersionChanges, getInput, getInput, getInput, getInputClasses, getInputDescription, getInputPorts, getIODescription, getLog, getLogger, getName, getNumberOfBreakpoints, getOperatorClassName, getOperatorDescription, getOutputClasses, getOutputPorts, getParameter, getParameterAsBoolean, getParameterAsChar, getParameterAsColor, getParameterAsDouble, getParameterAsFile, getParameterAsFile, getParameterAsInputStream, getParameterAsInt, getParameterAsMatrix, getParameterAsRepositoryLocation, getParameterAsString, getParameterHandler, getParameterList, getParameters, getParameterTupel, getParameterType, getParent, getPortOwner, getProcess, getResourceConsumptionEstimator, getRoot, getStartTime, getTransformer, getUserDescription, getValue, getValues, getXML, getXML, getXML, hasBreakpoint, hasBreakpoint, hasInput, inApplyLoop, isDebugMode, isDirty, isEnabled, isExpanded, isParallel, isParameterSet, isRunning, log, log, logError, logNote, logWarning, lookupOperator, makeDirty, makeDirtyOnUpdate, notifyRenaming, performAdditionalChecks, preAutoWire, processFinished, processStarts, producesOutput, propagateDirtyness, register, registerOperator, remove, removeAndKeepConnections, rename, resume, setBreakpoint, setCompatibilityLevel, setEnabled, setEnclosingProcess, setExpanded, setInput, setListParameter, setPairParameter, setParameter, setParameters, setUserDescription, shouldAutoConnect, shouldStopStandaloneExecution, toString, transformMetaData, unregisterOperator, updateExecutionOrder, walk, writeXML, writeXML

Methods inherited from class com.rapidminer.tools.AbstractObservable

addObserver, addObserverAsFirst, fireUpdate, removeObserver

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.rapidminer.operator.learner.Learner

getName

Field Detail

bestRule

protected ConjunctiveRuleModel bestRule

Constructor Detail

BestRuleInduction

public BestRuleInduction(OperatorDescription description)

Method Detail

supportsCapability

public boolean supportsCapability(OperatorCapability lc)

Description copied from interface: CapabilityProvider

Checks for Learner capabilities. Should return true if the given capability is supported.

initHighscore

protected void initHighscore()

communicateToHighscore

protected boolean communicateToHighscore(ConjunctiveRuleModel rule,
                                         double[] counts)
                                  throws UndefinedParameterError

Adds a rule to the set of best rules if its score is high enough. Currently just a single rule is stored. Additionally it is checked whether the rule is bad enough to be pruned.

Returns:

true iff the rule can be pruned

Throws:

UndefinedParameterError

getBestRule

protected ConjunctiveRuleModel getBestRule()

Returns:

the best rule found

getPruningScore

protected double getPruningScore()

Returns:

the lowest score of the stored best rules for pruning

learn

public Model learn(ExampleSet exampleSet)
            throws OperatorException

Description copied from interface: Learner

Trains a model. This method should be called by apply() and is implemented by subclasses.

Throws:

OperatorException

isRefinementOfPrunedRule

public boolean isRefinementOfPrunedRule(ConjunctiveRuleModel rule)

Parameters:

rule - a ConjuctiveRuleModel for which it is checked whether a more general rule has already been pruned.

Returns:

true, if this rule is a refinement of a pruned rule. The rules are compared using the method ConjunctiveRuleModel.isRefinementOf(ConjunctiveRuleModel model)

getScore

protected double getScore(double[] counts,
                          boolean predictPositives)
                   throws UndefinedParameterError

Computes the WRAcc or BINOMIAL TEST FUNCTION based on p, n, and the global values P and N stored in this object. First two entries of counts are p and n, optionally estimates for p and n can be supplied as further parameters.

Throws:

UndefinedParameterError

getOptimisticScore

protected double getOptimisticScore(double[] counts)
                             throws UndefinedParameterError

Computes the best possible score that might be achieved by refining the rule. During learning the conclusion is normalized to "positive", so the better of the estimates of the better conclusion is returned.

Throws:

UndefinedParameterError

getCounts

protected double[] getCounts(ConjunctiveRuleModel rule,
                             ExampleSet exampleSet)
                      throws OperatorException

Parameters:

rule - the rule to evaluate

exampleSet - the exampleSet to get the counts for

Returns:

a double[2] object, first parameter is p, second is n. If rule discovery relative to a predicted label is activated, then a double[4] is returned, which contains the estimated positives and negatives in the covered part as fields 3 and 4.

Throws:

OperatorException

getModelClass

public java.lang.Class<? extends PredictionModel> getModelClass()

Description copied from class: AbstractLearner

This method might be overridden from subclasses in order to specify exactly which model class they use. This is to ensure the proper postprocessing of some models like KernelModels (SupportVectorCounter) or TreeModels (Rule generation)

Overrides:

getModelClass in class AbstractLearner

getParameterTypes

public java.util.List<ParameterType> getParameterTypes()

Adds the parameters "number of iterations" and "model file".

Specified by:

getParameterTypes in interface ParameterHandler

Overrides:

getParameterTypes in class Operator