com.rapidminer.operator.performance
Class PredictionTrendAccuracy

java.lang.Object
  com.rapidminer.operator.AbstractIOObject
      com.rapidminer.operator.ResultObjectAdapter
          com.rapidminer.tools.math.Averagable
              com.rapidminer.operator.performance.PerformanceCriterion
                  com.rapidminer.operator.performance.MeasuredPerformance
                      com.rapidminer.operator.performance.PredictionTrendAccuracy

All Implemented Interfaces:

IOObject, ForecastingCriterion, ResultObject, Saveable, Readable, Reportable, LoggingHandler, java.io.Serializable, java.lang.Cloneable, java.lang.Comparable<PerformanceCriterion>

public class PredictionTrendAccuracy
extends MeasuredPerformance
implements ForecastingCriterion

Measures the number of times a regression prediction correctly determines the trend. This performance measure assumes that the attributes of each example represents the values of a time window, the label is a value after a certain horizon which should be predicted. All examples build a consecutive series description, i.e. the labels of all examples build the series itself (this is, for example, the case for a windowing step size of 1). This format will be delivered by the Series2ExampleSet operators provided by RapidMiner.

Example: Lets think of a series v1...v10 and a sliding window with window width 3, step size 1 and prediction horizon 1. The resulting example set is then

    T1 T2 T3 L   P
    ---------------
    v1 v2 v3 v4  p1
    v2 v3 v4 v5  p2
    v3 v4 v5 v6  p3
    v4 v5 v6 v7  p4
    v5 v6 v7 v8  p5
    v6 v7 v8 v9  p6
    v7 v8 v9 v10 p7
 

The second last column (L) corresponds to the label, i.e. the value which should be predicted and the last column (P) corresponds to the predictions. The columns T1, T2, and T3 correspond to the regular attributes, i.e. the points which should be used as learning input.

This performance measure then calculates the actuals trend between the last time point in the series (T3 here) and the actual label (L) and compares it to the trend between T3 and the prediction (P), sums the products between both trends, and divides this sum by the total number of examples, i.e. [(if ((v4-v3)*(p1-v3)>=0), 1, 0) + (if ((v5-v4)*(p2-v4)>=0), 1, 0) +...] / 7 in this example.

Author:

Ingo Mierswa

See Also:

Serialized Form

Constructor Summary

PredictionTrendAccuracy()

PredictionTrendAccuracy(PredictionTrendAccuracy pta)

Method Summary

void	buildSingleAverage(Averagable averagable) This method should build the average of this and another averagable of the same type.
void	countExample(Example example) Counts a single example, e.g. by summing up errors.
java.lang.String	getDescription() Returns a description of the performance criterion.
double	getExampleCount() Returns the number of data points which was used to determine the criterion value.
double	getFitness() Returns the fitness depending on the value.
double	getMikroAverage() Returns the (current) value of the averagable (the average itself).
double	getMikroVariance() Returns the variance of the averagable.
java.lang.String	getName() Returns the name of this averagable.
void	setParent(Operator parent)
void	startCounting(ExampleSet eSet, boolean useExampleWeights) Initializes the criterion.

Methods inherited from class com.rapidminer.operator.performance.MeasuredPerformance

startCounting

Methods inherited from class com.rapidminer.operator.performance.PerformanceCriterion

compareTo, getMaxFitness

Methods inherited from class com.rapidminer.tools.math.Averagable

buildAverage, clone, cloneAveragable, formatPercent, getAverage, getAverageCount, getExtension, getFileDescription, getMakroAverage, getMakroStandardDeviation, getMakroVariance, getMikroStandardDeviation, getStandardDeviation, getVariance, getVisualizationComponent, setAverageCount, toString

Methods inherited from class com.rapidminer.operator.ResultObjectAdapter

addAction, getActions, getResultIcon, isSavable, log, logError, logNote, logWarning, save, toHTML, toResultString

Methods inherited from class com.rapidminer.operator.AbstractIOObject

copy, getLog, getSource, initWriting, read, setLoggingHandler, setSource, write

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.rapidminer.operator.IOObject

copy, getLog, getSource, setLoggingHandler, setSource, write

Constructor Detail

PredictionTrendAccuracy

public PredictionTrendAccuracy()

PredictionTrendAccuracy

public PredictionTrendAccuracy(PredictionTrendAccuracy pta)

Method Detail

setParent

public void setParent(Operator parent)

Specified by:

setParent in interface ForecastingCriterion

getName

public java.lang.String getName()

Description copied from class: Averagable

Returns the name of this averagable. The returned string should only contain lowercase letters and underscore (RapidMiner parameter format) since the names will be automatically used for GUI purposes.

Specified by:

getName in interface ResultObject

Specified by:

getName in class Averagable

getDescription

public java.lang.String getDescription()

Description copied from class: PerformanceCriterion

Returns a description of the performance criterion. This description is used for GUI purposes and automatic parameter type creation for the PerformanceEvaluator operator.

Specified by:

getDescription in class PerformanceCriterion

startCounting

public void startCounting(ExampleSet eSet,
                          boolean useExampleWeights)
                   throws OperatorException

Description copied from class: MeasuredPerformance

Initializes the criterion. The default implementation does nothing.

Overrides:

startCounting in class MeasuredPerformance

Throws:

OperatorException

getExampleCount

public double getExampleCount()

Description copied from class: PerformanceCriterion

Returns the number of data points which was used to determine the criterion value. If the criterion does not use example weights (or no weight was given) then the returned value will be an integer. Otherwise, the returned value is the sum of all example weights.

Specified by:

getExampleCount in class PerformanceCriterion

countExample

public void countExample(Example example)

Description copied from class: MeasuredPerformance

Counts a single example, e.g. by summing up errors.

Specified by:

countExample in class MeasuredPerformance

getFitness

public double getFitness()

Description copied from class: PerformanceCriterion

Returns the fitness depending on the value. The fitness values will be used for all optimization purposes (feature space transformations, parameter optimizations...) and must always be maximized. Hence, if your criterion is better the smaller the value is you should return something like (-1 * value) or (1 / value).

Subclasses should use Averagable.getAverage() instead of Averagable.getMikroAverage() in this method since usually the makro average (if available) should be optmized instead of the mikro average. The mikro average should only be used in the (rare) cases where no makro average is available but this is automatically done returned by Averagable.getAverage() in these cases.

Specified by:

getFitness in class PerformanceCriterion

getMikroAverage

public double getMikroAverage()

Description copied from class: Averagable

Returns the (current) value of the averagable (the average itself). If the method Averagable.buildSingleAverage(Averagable) was used, this method must return the micro average from both (or more) criteria. This is usually achieved by correctly implementing Averagable.buildSingleAverage(Averagable).

Specified by:

getMikroAverage in class Averagable

getMikroVariance

public double getMikroVariance()

Description copied from class: Averagable

Returns the variance of the averagable. The returned value must not be negative. If the averagable does not define a variance this method should return Double.NaN.

Specified by:

getMikroVariance in class Averagable

buildSingleAverage

public void buildSingleAverage(Averagable averagable)

Description copied from class: Averagable

This method should build the average of this and another averagable of the same type. The next invocation of Averagable.getMikroAverage() should return the average of this and the given averagable. Hence, this method is used to build the actual micro average value of two criteria. Please refer to SimpleCriterion for a simple implementation example.

Specified by:

buildSingleAverage in class Averagable