com.rapidminer.operator.learner.igss
Class IteratingGSS

java.lang.Object
  com.rapidminer.operator.Operator
      com.rapidminer.operator.learner.AbstractLearner
          com.rapidminer.operator.learner.igss.IteratingGSS

All Implemented Interfaces:

ConfigurationListener, PreviewListener, Learner, ParameterHandler, LoggingHandler

public class IteratingGSS
extends AbstractLearner

This operator implements the IteratingGSS algorithmus presented in the diploma thesis 'Effiziente Entdeckung unabhaengiger Subgruppen in grossen Datenbanken' at the Department of Computer Science, University of Dortmund.

Author:

Dirk Dach

Field Summary

static java.lang.String[]	CRITERION_TYPES
static int	FIRST_TYPE_INDEX
static int	LAST_TYPE_INDEX
int	MIN_MODEL_NUMBER minimal model number for example_criterion
static java.lang.String	PARAMETER_DELTA The parameter name for "desired confidence"
static java.lang.String	PARAMETER_EPSILON The parameter name for "approximation parameter"
static java.lang.String	PARAMETER_EXAMPLE_FACTOR The parameter name for "used by example criterion to determine usefulness of a hypothesis"
static java.lang.String	PARAMETER_FORCE_ITERATIONS The parameter name for "make all iterations even if termination criterion is met"
static java.lang.String	PARAMETER_GENERATE_ALL_HYPOTHESIS The parameter name for "generate h->Y+/Y- or h->Y+ only.
static java.lang.String	PARAMETER_ITERATIONS The parameter name for "the number of iterations"
static java.lang.String	PARAMETER_LARGE The parameter name for "the number of examples a hypothesis must cover before normal approximation is used"
static java.lang.String	PARAMETER_MAX_COMPLEXITY The parameter name for "the maximum complexity of hypothesis"
static java.lang.String	PARAMETER_MIN_COMPLEXITY The parameter name for "the minimum complexity of hypothesis"
static java.lang.String	PARAMETER_MIN_UTILITY_PRUNING The parameter name for "minimum utility used for pruning"
static java.lang.String	PARAMETER_MIN_UTILITY_USEFUL The parameter name for "minimum utility for the usefulness of a rule"
static java.lang.String	PARAMETER_REJECTION_SAMPLING The parameter name for "use rejection sampling instead of weighted examples"
static java.lang.String	PARAMETER_RESET_WEIGHTS The parameter name for "Set weights back to 1 when complexity is increased.
static java.lang.String	PARAMETER_STEPSIZE The parameter name for "the number of examples drawn before the next hypothesis update"
static java.lang.String	PARAMETER_USE_BINOMIAL The parameter name for "Switch to binomial utility funtion before increasing complexity"
static java.lang.String	PARAMETER_USE_KBS The parameter name for "use kbs to reweight examples after each iteration"
static java.lang.String	PARAMETER_USEFUL_CRITERION The parameter name for "criterion to decide if the complexity is increased "
static java.lang.String	PARAMETER_UTILITY_FUNCTION The parameter name for "the utility function to be used"
static int	TYPE_BEST_UTILITY
static int	TYPE_EXAMPLE
static int	TYPE_UTILITY
static int	TYPE_WORST_UTILITY

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

PROPERTY_RAPIDMINER_GENERAL_CAPABILITIES_WARN

Constructor Summary

IteratingGSS(OperatorDescription description)
Must pass the given object to the superclass.

Method Summary

IOObject[]	apply() Trains a model using an ExampleSet from the input.
java.util.LinkedList<Hypothesis>	generate(java.util.LinkedList<Hypothesis> oldHypothesis) Generates all successors of the hypothesis in the given list.
java.lang.Class<?>[]	getOutputClasses() Returns the classes that are guaranteed to be returned by apply() as additional output.
java.util.List<ParameterType>	getParameterTypes() Returns a list of ParameterTypes describing the parameters of this operator.
java.util.LinkedList<Result>	gss(ExampleSet exampleSet, java.util.LinkedList<Hypothesis> hypothesisList, double delta, double epsilon) Returns the n best hypothesis with maximum error epsilon with confidence 1-delta.
boolean	isUseful(Result current, java.util.LinkedList<Result> otherResults, int criterion, ExampleSet exampleSet, int min_model_number) Test if the model is useful according to the given criterion.
Model	learn(ExampleSet exampleSet) Trains a model.
static double	log2(double arg) Returns the logarithm to base 2
java.util.LinkedList<Hypothesis>	prune(java.util.LinkedList<Hypothesis> hypoList, double minUtility, double totalWeight, double totalPositiveWeight, double delta_p) Prunes the given list of hypothesis.
ContingencyMatrix	reweight(ExampleSet exampleSet, Model model, boolean normalize) Reweights the examples according to knowledge based sampling.
boolean	supportsCapability(LearnerCapability lc) Checks for Learner capabilities.

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

getEstimatedPerformance, getInputClasses, getInputDescription, getOptimizationPerformance, getWeights, onlyWarnForNonSufficientCapabilities, shouldCalculateWeights, shouldDeliverOptimizationPerformance, shouldEstimatePerformance

Methods inherited from class com.rapidminer.operator.Operator

addError, addValue, addWarning, apply, checkDeprecations, checkForStop, checkIO, checkProperties, clearErrorList, cloneOperator, createExperimentTree, createExperimentTree, createFromXML, createMarkedExperimentTree, createMarkedProcessTree, createProcessTree, createProcessTree, getAddOnlyAdditionalOutput, getApplyCount, getDeliveredOutputClasses, getDeprecationInfo, getDesiredInputClasses, getEncoding, getErrorList, getExperiment, getInnerOperatorsXML, getInput, getInput, getInput, getIOContainerForInApplyLoopBreakpoint, getIODescription, getLog, getName, getOperatorClassName, getOperatorDescription, getParameter, getParameterAsBoolean, getParameterAsColor, getParameterAsDouble, getParameterAsFile, getParameterAsFile, getParameterAsInputStream, getParameterAsInt, getParameterAsMatrix, getParameterAsString, getParameterList, getParameters, getParameterType, getParent, getProcess, getStartTime, getStatus, getUserDescription, getValue, getValues, getXML, hasBreakpoint, hasBreakpoint, hasInput, inApplyLoop, isDebugMode, isEnabled, isExpanded, isParallel, isParameterSet, log, logError, logNote, logWarning, performAdditionalChecks, processFinished, processStarts, register, registerOperator, remove, rename, resume, setApplyCount, setBreakpoint, setEnabled, setExpanded, setInput, setListParameter, setOperatorParameters, setParameter, setParameters, setParent, setUserDescription, toString, unregisterOperator, writeXML

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

PARAMETER_EPSILON

public static final java.lang.String PARAMETER_EPSILON

The parameter name for "approximation parameter"

See Also:

Constant Field Values

PARAMETER_DELTA

public static final java.lang.String PARAMETER_DELTA

The parameter name for "desired confidence"

See Also:

Constant Field Values

PARAMETER_MIN_UTILITY_PRUNING

public static final java.lang.String PARAMETER_MIN_UTILITY_PRUNING

The parameter name for "minimum utility used for pruning"

See Also:

Constant Field Values

PARAMETER_MIN_UTILITY_USEFUL

public static final java.lang.String PARAMETER_MIN_UTILITY_USEFUL

The parameter name for "minimum utility for the usefulness of a rule"

See Also:

Constant Field Values

PARAMETER_STEPSIZE

public static final java.lang.String PARAMETER_STEPSIZE

The parameter name for "the number of examples drawn before the next hypothesis update"

See Also:

Constant Field Values

PARAMETER_LARGE

public static final java.lang.String PARAMETER_LARGE

The parameter name for "the number of examples a hypothesis must cover before normal approximation is used"

See Also:

Constant Field Values

PARAMETER_MAX_COMPLEXITY

public static final java.lang.String PARAMETER_MAX_COMPLEXITY

The parameter name for "the maximum complexity of hypothesis"

See Also:

Constant Field Values

PARAMETER_MIN_COMPLEXITY

public static final java.lang.String PARAMETER_MIN_COMPLEXITY

The parameter name for "the minimum complexity of hypothesis"

See Also:

Constant Field Values

PARAMETER_ITERATIONS

public static final java.lang.String PARAMETER_ITERATIONS

The parameter name for "the number of iterations"

See Also:

Constant Field Values

PARAMETER_USE_BINOMIAL

public static final java.lang.String PARAMETER_USE_BINOMIAL

The parameter name for "Switch to binomial utility funtion before increasing complexity"

See Also:

Constant Field Values

PARAMETER_UTILITY_FUNCTION

public static final java.lang.String PARAMETER_UTILITY_FUNCTION

The parameter name for "the utility function to be used"

See Also:

Constant Field Values

PARAMETER_USE_KBS

public static final java.lang.String PARAMETER_USE_KBS

The parameter name for "use kbs to reweight examples after each iteration"

See Also:

Constant Field Values

PARAMETER_REJECTION_SAMPLING

public static final java.lang.String PARAMETER_REJECTION_SAMPLING

The parameter name for "use rejection sampling instead of weighted examples"

See Also:

Constant Field Values

PARAMETER_USEFUL_CRITERION

public static final java.lang.String PARAMETER_USEFUL_CRITERION

The parameter name for "criterion to decide if the complexity is increased "

See Also:

Constant Field Values

PARAMETER_EXAMPLE_FACTOR

public static final java.lang.String PARAMETER_EXAMPLE_FACTOR

The parameter name for "used by example criterion to determine usefulness of a hypothesis"

See Also:

Constant Field Values

PARAMETER_FORCE_ITERATIONS

public static final java.lang.String PARAMETER_FORCE_ITERATIONS

The parameter name for "make all iterations even if termination criterion is met"

See Also:

Constant Field Values

PARAMETER_GENERATE_ALL_HYPOTHESIS

public static final java.lang.String PARAMETER_GENERATE_ALL_HYPOTHESIS

The parameter name for "generate h->Y+/Y- or h->Y+ only."

See Also:

Constant Field Values

PARAMETER_RESET_WEIGHTS

public static final java.lang.String PARAMETER_RESET_WEIGHTS

The parameter name for "Set weights back to 1 when complexity is increased."

See Also:

Constant Field Values

CRITERION_TYPES

public static final java.lang.String[] CRITERION_TYPES

FIRST_TYPE_INDEX

public static final int FIRST_TYPE_INDEX

See Also:

Constant Field Values

TYPE_WORST_UTILITY

public static final int TYPE_WORST_UTILITY

See Also:

Constant Field Values

TYPE_UTILITY

public static final int TYPE_UTILITY

See Also:

Constant Field Values

TYPE_BEST_UTILITY

public static final int TYPE_BEST_UTILITY

See Also:

Constant Field Values

TYPE_EXAMPLE

public static final int TYPE_EXAMPLE

See Also:

Constant Field Values

LAST_TYPE_INDEX

public static final int LAST_TYPE_INDEX

See Also:

Constant Field Values

MIN_MODEL_NUMBER

public int MIN_MODEL_NUMBER

minimal model number for example_criterion

Constructor Detail

IteratingGSS

public IteratingGSS(OperatorDescription description)

Must pass the given object to the superclass.

Method Detail

gss

public java.util.LinkedList<Result> gss(ExampleSet exampleSet,
                                        java.util.LinkedList<Hypothesis> hypothesisList,
                                        double delta,
                                        double epsilon)
                                 throws OperatorException

Returns the n best hypothesis with maximum error epsilon with confidence 1-delta.

Throws:

OperatorException

reweight

public ContingencyMatrix reweight(ExampleSet exampleSet,
                                  Model model,
                                  boolean normalize)
                           throws OperatorException

Reweights the examples according to knowledge based sampling. Normalizes weights to [0,1] if the parameter normalize is set to true.

Throws:

OperatorException

apply

public IOObject[] apply()
                 throws OperatorException

Description copied from class: AbstractLearner

Trains a model using an ExampleSet from the input. Uses the method learn(ExampleSet).

Overrides:

apply in class AbstractLearner

Throws:

OperatorException

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

isUseful

public boolean isUseful(Result current,
                        java.util.LinkedList<Result> otherResults,
                        int criterion,
                        ExampleSet exampleSet,
                        int min_model_number)

Test if the model is useful according to the given criterion.

prune

public java.util.LinkedList<Hypothesis> prune(java.util.LinkedList<Hypothesis> hypoList,
                                              double minUtility,
                                              double totalWeight,
                                              double totalPositiveWeight,
                                              double delta_p)

Prunes the given list of hypothesis. All hypothesis with an upper utility bound less than the parameter minUtility is pruned.

generate

public java.util.LinkedList<Hypothesis> generate(java.util.LinkedList<Hypothesis> oldHypothesis)

Generates all successors of the hypothesis in the given list.

log2

public static double log2(double arg)

Returns the logarithm to base 2

supportsCapability

public boolean supportsCapability(LearnerCapability lc)

Description copied from interface: Learner

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

getOutputClasses

public java.lang.Class<?>[] getOutputClasses()

Description copied from class: Operator

Returns the classes that are guaranteed to be returned by apply() as additional output. Please note that input objects which should not be consumed must also be defined by this method (e.g. an example set which is changed but not consumed in the case of a preprocessing operator must be defined in both, the methods Operator.getInputClasses() and Operator.getOutputClasses()). The default behavior for input consumation is defined by Operator.getInputDescription(Class) and can be changed by overwriting this method. Objects which are not consumed (defined by changing the implementation in Operator.getInputDescription(Class)) must not be defined as additional output in this method.

May deliver null or an empy array (no additional output is produced or guaranteed). Must return the class array of delivered output objects otherwise.

Overrides:

getOutputClasses in class AbstractLearner

getParameterTypes

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

Description copied from class: Operator

Returns a list of ParameterTypes describing the parameters of this operator. The default implementation returns an empty list if no input objects can be retained and special parameters for those input objects which can be prevented from being consumed.

Specified by:

getParameterTypes in interface ParameterHandler

Overrides:

getParameterTypes in class Operator