de.jstacs.classifiers.assessment
Class Sampled_RepeatedHoldOutExperiment

java.lang.Object
  de.jstacs.classifiers.assessment.ClassifierAssessment
      de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment

public class Sampled_RepeatedHoldOutExperiment
extends ClassifierAssessment

This class is a special ClassifierAssessment that partitions the data of a user-specified reference class (typically the smallest class) and samples non-overlapping for all other classes, so that one gets the same number of sequences (and the same lengths of the sequences) in each train and test dataset.

Author:

Jens Keilwagen

See Also:

Sampled_RepeatedHoldOutAssessParameterSet

Field Summary

Fields inherited from class de.jstacs.classifiers.assessment.ClassifierAssessment

myAbstractClassifier, myModel, myTempMeanResultSets, skipLastClassifiersDuringClassifierTraining

Constructor Summary

	Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs) Creates a new Sampled_RepeatedHoldOutExperiment from a set of AbstractClassifiers.
	Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.
protected	Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength) Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers.
	Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) Creates a new Sampled_RepeatedHoldOutExperiment from a set of TrainableStatisticalModels.

Method Summary

protected void

evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU) This method must be implemented in all subclasses.

Methods inherited from class de.jstacs.classifiers.assessment.ClassifierAssessment

assess, assess, assess, getClassifier, getNameOfAssessment, prepareAssessment, test, train

Methods inherited from class java.lang.Object

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

Constructor Detail

Sampled_RepeatedHoldOutExperiment

protected Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
                                            TrainableStatisticalModel[][] aMs,
                                            boolean buildClassifiersByCrossProduct,
                                            boolean checkAlphabetConsistencyAndLength)
                                     throws IllegalArgumentException,
                                            WrongAlphabetException,
                                            CloneNotSupportedException,
                                            ClassDimensionException

Creates a new Sampled_RepeatedHoldOutExperiment from an array of AbstractClassifiers and a two-dimensional array of TrainableStatisticalModel s, which are combined to additional classifiers. If buildClassifiersByCrossProduct is true, the cross-product of all TrainableStatisticalModels in aMs is built to obtain these classifiers.

Parameters:

aCs - the predefined classifiers

aMs - the TrainableStatisticalModels that are used to build additional classifiers

buildClassifiersByCrossProduct - Determines how classifiers are constructed using the given models. Suppose a k-class problem. In this case, each classifier is supposed to consist of k models, one responsible for each class.
Let S_i be the set of all models in aMs[i]. Let S be the set S_1 x S_2 x ... x S_k (cross-product).

true: all possible classifiers consisting of a subset (set of k models) of S are constructed
false: one classifier consisting of the models aMs[0][i],aMs[1][i],..., aMs[k][i] for a fixed i is constructed. In this case, all second dimensions of aMs have to be equal, say m. In total m classifiers are constructed.

checkAlphabetConsistencyAndLength - indicates if alphabets and lengths shall be checked for consistency

Throws:

IllegalArgumentException - if the classifiers have different lengths

WrongAlphabetException - if the classifiers use different alphabets

CloneNotSupportedException - if something went wrong while cloning

ClassDimensionException - if there is something wrong with the class dimension of the classifier

See Also:

ClassifierAssessment.ClassifierAssessment(AbstractClassifier[], TrainableStatisticalModel[][], boolean, boolean)

Sampled_RepeatedHoldOutExperiment

public Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)
                                  throws IllegalArgumentException,
                                         WrongAlphabetException,
                                         CloneNotSupportedException,
                                         ClassDimensionException

Creates a new Sampled_RepeatedHoldOutExperiment from a set of AbstractClassifiers.

Parameters:

aCs - contains the classifiers to be assessed,
If model based classifiers are trained, the order of models in classifiers determines, which model will be trained using which sample in method assess( ... ).
For a two-class problem, it is recommended

• to initiate the classifiers with models in order (foreground model (positive class), background model (negative class))
• to initiate an assessment object using models in order (foreground model (positive class), background model (negative class))
• to give data s in order (s[0] contains foreground data, s[1] contains background data)

Throws:

IllegalArgumentException - if the classifiers have different lengths

WrongAlphabetException - if not all given classifiers are defined on the same AlphabetContainer

CloneNotSupportedException - if something went wrong while cloning

ClassDimensionException - if there is something wrong with the class dimension of the classifier

See Also:

ClassifierAssessment.ClassifierAssessment(AbstractClassifier...)

Sampled_RepeatedHoldOutExperiment

public Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct,
                                         TrainableStatisticalModel[]... aMs)
                                  throws IllegalArgumentException,
                                         WrongAlphabetException,
                                         CloneNotSupportedException,
                                         ClassDimensionException

Creates a new Sampled_RepeatedHoldOutExperiment from a set of TrainableStatisticalModels. The argument buildClassifiersByCrossProduct determines how these TrainableStatisticalModels are combined to classifiers.

Parameters:

buildClassifiersByCrossProduct -
Determines how classifiers are constructed using the given models. Suppose a k-class problem. In this case, each classifier is supposed to consist of k models, one responsible for each class.
Let S_i be the set of all models in aMs[i]. Let S be the set S_1 x S_2 x ... x S_k (cross-product).

true: all possible classifiers consisting of a subset (set of k models) of S are constructed
false: one classifier consisting of the models aMs[0][i],aMs[1][i],..., aMs[k][i] for a fixed i is constructed. In this case, all second dimensions of aMs have to be equal, say m. In total m classifiers are constructed.

aMs -
Contains the models in the following way (suppose a k-class problem): the first dimension encodes the class (here it is k), the second dimension (aMs[i]) contains the models according to class i.
If models are trained directly (during assessment), the order of given models during initiation of this assessment object determines, which sample will be used for training which model. In general the first model will be trained using the first sample in s... .
For a two-class problem, it is recommended

• to initiate the classifiers with models in order (foreground model (positive class), background model (negative class))
• to initiate an assessment object using models in order (foreground model (positive class), background model (negative class))
• to give data s in order (s[0] contains foreground data, s[1] contains background data)

Throws:

IllegalArgumentException - if the classifiers have different lengths

WrongAlphabetException - if not all given classifiers are defined on the same AlphabetContainer

CloneNotSupportedException - if something went wrong while cloning

ClassDimensionException - if there is something wrong with the class dimension of the classifier

See Also:

ClassifierAssessment.ClassifierAssessment(boolean, TrainableStatisticalModel[][])

Sampled_RepeatedHoldOutExperiment

public Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
                                         boolean buildClassifiersByCrossProduct,
                                         TrainableStatisticalModel[]... aMs)
                                  throws IllegalArgumentException,
                                         WrongAlphabetException,
                                         CloneNotSupportedException,
                                         ClassDimensionException

This constructor allows to assess a collection of given AbstractClassifiers and those constructed using the given TrainableStatisticalModels by a Sampled_RepeatedHoldOutExperiment.

Parameters:

aCs - contains some AbstractClassifier that should be assessed in addition to the AbstractClassifiers constructed using the given TrainableStatisticalModels

buildClassifiersByCrossProduct -
Determines how classifiers are constructed using the given models. Suppose a k-class problem. In this case, each classifier is supposed to consist of k models, one responsible for each class.
Let S_i be the set of all models in aMs[i]. Let S be the set S_1 x S_2 x ... x S_k (cross-product).

true: all possible classifiers consisting of a subset (set of k models) of S are constructed
false: one classifier consisting of the models aMs[0][i],aMs[1][i],..., aMs[k][i] for a fixed i is constructed. In this case, all second dimensions of aMs have to be equal, say m. In total m classifiers are constructed.

aMs -
Contains the models in the following way (suppose a k-class problem): the first dimension encodes the class (here it is k), the second dimension (aMs[i]) contains the models according to class i.
If models are trained directly (during assessment), the order of given models during initiation of this assessment object determines, which sample will be used for training which model. In general the first model will be trained using the first sample in s... .
For a two-class problem, it is recommended

• to initiate the classifiers with models in order (foreground model (positive class), background model (negative class))
• to initiate a assessment object using models in order (foreground model (positive class), background model (negative class))
• to give data s in order (s[0] contains foreground data, s[1] contains background data)

Throws:

IllegalArgumentException - if the classifiers have different lengths

WrongAlphabetException - if not all given classifiers are defined on the same AlphabetContainer

CloneNotSupportedException - if something went wrong while cloning

ClassDimensionException - if there is something wrong with the class dimension of the classifier

See Also:

ClassifierAssessment.ClassifierAssessment(AbstractClassifier[], boolean, TrainableStatisticalModel[][])

Method Detail

evaluateClassifier

protected void evaluateClassifier(NumericalPerformanceMeasureParameterSet mp,
                                  ClassifierAssessmentAssessParameterSet assessPS,
                                  DataSet[] s,
                                  ProgressUpdater pU)
                           throws IllegalArgumentException,
                                  Exception

Description copied from class: ClassifierAssessment

This method must be implemented in all subclasses. It should perform the following tasks:
1.) create test and train datasets
2.) call method train() to train classifiers/models using train data
3.) call method test() to cause evaluation (test) of trained classifiers

Specified by:

evaluateClassifier in class ClassifierAssessment

Parameters:

mp - defines which performance measures are used to assess classifiers

assessPS - contains assessment specific parameters (like: number of iterations of a k-fold-crossvalidation)

s - data to be used for assessment (both: test and train data)

pU - a ProgressUpdater that mainly has to be used to allow the user to cancel a current running classifier assessment. This ProgressUpdater is guaranteed to be not null. In certain cases aborting a classifier assessment will not be allowed for example in case of KFoldCrossValidation. In this case the given ProgressUpdater should be ignored.

Usage:

• pU.setMax()= number of iterations of the assessment loop
• iteration=0;
• assessment loop
  • pU.setValue()=iteration+1;
  • sample treatment
  • train();
  • test();
  • iteration++;
• repeat unless(ready or not(pU.isCancelled()))

Throws:

IllegalArgumentException - if the given ClassifierAssessmentAssessParameterSet is of wrong type

Exception - that occurred during training or using classifiers/models