de.dfki.madm.mlwizard.landmarking
Class AbstractLandmarker
java.lang.Object
de.dfki.madm.mlwizard.landmarking.AbstractLandmarker
- Direct Known Subclasses:
- AverageNodeLm, DecisionNodeLm, KNNLearnerLm, NaiveBayesLm, RandomlyChosenNodeLm, WorstNodeLm
public abstract class AbstractLandmarker
- extends java.lang.Object
Abstract superclass of all landmark learner classes, used in the landmark
operator. Any landmark learner as a subclass of AbstractLandmarker
can be evaluated using Cross Validation depending on the value of the cross
validation parameter.
- Author:
- Sarah Daniel
Method Summary |
java.lang.Double |
getAccuracy()
|
java.lang.String |
getName()
|
protected java.lang.Double |
getPerformanceCriterion(java.lang.String criterion)
Returns the performance criterion average value specified. |
java.util.List<java.lang.Double> |
getPerformanceValues()
|
com.rapidminer.operator.performance.PerformanceVector |
getPerformanceVector()
|
protected void |
learn(com.rapidminer.example.ExampleSet exampleSet,
com.rapidminer.operator.Operator learner,
java.util.Map<java.lang.String,java.lang.String> parameters)
The Operator learner takes the ExampleSet as input and
then its model is applied to the ExampleSet . |
protected void |
learnExampleSet(com.rapidminer.example.ExampleSet exampleSet,
com.rapidminer.operator.Operator learner,
java.util.Map<java.lang.String,java.lang.String> parameters)
|
protected com.rapidminer.example.ExampleSet |
normalizeExampleSet(com.rapidminer.example.ExampleSet exampleSet,
java.util.Map<java.lang.String,java.lang.String> parameters)
|
protected void |
xValidation(com.rapidminer.example.ExampleSet exampleSet,
com.rapidminer.operator.Operator learner,
java.util.Map<java.lang.String,java.lang.String> parameters)
An XValidation is applied on the Operator learner. |
Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
PARAMETER_PERFORMANCE_ACCURACY
protected java.lang.String PARAMETER_PERFORMANCE_ACCURACY
performance
protected com.rapidminer.operator.performance.PerformanceVector performance
name
protected java.lang.String name
AbstractLandmarker
public AbstractLandmarker()
learnExampleSet
protected void learnExampleSet(com.rapidminer.example.ExampleSet exampleSet,
com.rapidminer.operator.Operator learner,
java.util.Map<java.lang.String,java.lang.String> parameters)
throws com.rapidminer.operator.OperatorCreationException,
com.rapidminer.operator.OperatorException
- Throws:
com.rapidminer.operator.OperatorCreationException
com.rapidminer.operator.OperatorException
learn
protected void learn(com.rapidminer.example.ExampleSet exampleSet,
com.rapidminer.operator.Operator learner,
java.util.Map<java.lang.String,java.lang.String> parameters)
throws com.rapidminer.operator.OperatorCreationException,
com.rapidminer.operator.OperatorException
- The
Operator
learner takes the ExampleSet
as input and
then its model is applied to the ExampleSet
. Finally the
performance is evaluated.
- Parameters:
exampleSet
- the ExampleSet
used as input for the Operator
learner.learner
- the Operator
that should be evaluated as a landmark.parameters
- the Map
containing all parameters of the landmarking.
- Throws:
com.rapidminer.operator.OperatorCreationException
com.rapidminer.operator.OperatorException
xValidation
protected void xValidation(com.rapidminer.example.ExampleSet exampleSet,
com.rapidminer.operator.Operator learner,
java.util.Map<java.lang.String,java.lang.String> parameters)
throws com.rapidminer.operator.OperatorCreationException,
com.rapidminer.operator.OperatorException
- An
XValidation
is applied on the Operator
learner.
- Parameters:
exampleSet
- the ExampleSet
used as input for the
XValidation
process.learner
- the Operator
is an inner operator of the X-Validation
process that should be evaluated as a landmark.parameters
- the Map
containing all parameters of the landmarking.
- Throws:
com.rapidminer.operator.OperatorCreationException
com.rapidminer.operator.OperatorException
normalizeExampleSet
protected com.rapidminer.example.ExampleSet normalizeExampleSet(com.rapidminer.example.ExampleSet exampleSet,
java.util.Map<java.lang.String,java.lang.String> parameters)
throws com.rapidminer.operator.OperatorException,
com.rapidminer.operator.OperatorCreationException
- Throws:
com.rapidminer.operator.OperatorException
com.rapidminer.operator.OperatorCreationException
getPerformanceCriterion
protected java.lang.Double getPerformanceCriterion(java.lang.String criterion)
- Returns the performance criterion average value specified.
- Parameters:
criterion
- the name of the criterion to be returned.
- Returns:
- The makro average if it was defined and the mikro average (the
current value) otherwise.
getPerformanceValues
public java.util.List<java.lang.Double> getPerformanceValues()
- Returns:
- a
List
of Double
values of the performance
criteria: accuracy and rootmean squared error
getAccuracy
public java.lang.Double getAccuracy()
- Returns:
- a
Double
representing the accuracy of the landmarker.
getPerformanceVector
public com.rapidminer.operator.performance.PerformanceVector getPerformanceVector()
- Returns:
- the
PerformanceVector
of the landmark.
getName
public java.lang.String getName()
- Returns:
- the name of the landmark.
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