de.jstacs.classifier.assessment
Class Sampled_RepeatedHoldOutExperiment

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

public class Sampled_RepeatedHoldOutExperiment
extends ClassifierAssessment

This class is a special ClassifierAssessment that partitions the data of a reference class and samples non-overlapping for the other classes, so that one get the same number of sequences and the same lengths of the sequences.

Author:

Jens Keilwagen

Field Summary

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

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

Method Summary

protected boolean

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

Methods inherited from class de.jstacs.classifier.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,
                                            Model[][] 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 Models, which are combined to additional classifiers. If buildClassifiersByCrossProduct is true, the cross product of all Models in aMs is built to obtain these classifiers.

Parameters:

aCs - the pre-defined classifiers

aMs - the Models 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

WrongAlphabetException

CloneNotSupportedException

ClassDimensionException

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 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

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

ClassDimensionException

CloneNotSupportedException

Sampled_RepeatedHoldOutExperiment

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

Creates a new Sampled_RepeatedHoldOutExperiment from a set of Models. The argument buildClassifiersByCrossProduct determines how these Models 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 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:

WrongAlphabetException - if not all given models are defines on the same AlphabetContainer

IllegalArgumentException

CloneNotSupportedException

ClassDimensionException

Sampled_RepeatedHoldOutExperiment

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

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

Parameters:

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

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:

WrongAlphabetException - if not all given models are defines on the same AlphabetContainer

IllegalArgumentException

CloneNotSupportedException

ClassDimensionException

Method Detail

evaluateClassifier

protected boolean evaluateClassifier(MeasureParameters mp,
                                     ClassifierAssessmentAssessParameterSet assessPS,
                                     Sample[] 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 classieres

Specified by:

evaluateClassifier in class ClassifierAssessment

Parameters:

mp - defines which performance-measures are used to assess classifiers

assessPS - containes 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 alssifier 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.isCanceled()))

Returns:

true, if no errors occured

Throws:

IllegalArgumentException - if the given AssessParameterSet is of wrong type

Exception - that occured during training or using classifiers/models