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java.lang.Objectde.jstacs.classifiers.assessment.ClassifierAssessment
de.jstacs.classifiers.assessment.Sampled_RepeatedHoldOutExperiment
public class Sampled_RepeatedHoldOutExperiment
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.
Sampled_RepeatedHoldOutAssessParameterSet
Field Summary |
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Fields inherited from class de.jstacs.classifiers.assessment.ClassifierAssessment |
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myAbstractClassifier, myModel, myTempMeanResultSets, skipLastClassifiersDuringClassifierTraining |
Constructor Summary | |
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Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs)
Creates a new Sampled_RepeatedHoldOutExperiment from a set of
AbstractClassifier s. |
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Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given AbstractClassifier s and those constructed using the given
TrainableStatisticalModel s by a
Sampled_RepeatedHoldOutExperiment . |
protected |
Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new Sampled_RepeatedHoldOutExperiment from an array of
AbstractClassifier s and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
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Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new Sampled_RepeatedHoldOutExperiment from a set of
TrainableStatisticalModel s. |
Method Summary | |
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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 |
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assess, assess, assess, getClassifier, getNameOfAssessment, prepareAssessment, test, train |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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protected Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
Sampled_RepeatedHoldOutExperiment
from an array of
AbstractClassifier
s and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. If
buildClassifiersByCrossProduct
is true
, the
cross-product of all TrainableStatisticalModel
s in aMs
is built to
obtain these classifiers.
aCs
- the predefined classifiersaMs
- the TrainableStatisticalModel
s that are used to build additional
classifiersbuildClassifiersByCrossProduct
- 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. 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
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
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier[],
TrainableStatisticalModel[][], boolean, boolean)
public Sampled_RepeatedHoldOutExperiment(AbstractClassifier... aCs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
Sampled_RepeatedHoldOutExperiment
from a set of
AbstractClassifier
s.
aCs
- contains the classifiers to be assessed,assess( ... )
.s
in order (s[0]
contains foreground data, s[1]
contains
background data)
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
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier...)
public Sampled_RepeatedHoldOutExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
Sampled_RepeatedHoldOutExperiment
from a set of
TrainableStatisticalModel
s. The argument buildClassifiersByCrossProduct
determines how these TrainableStatisticalModel
s are combined to classifiers.
buildClassifiersByCrossProduct
- 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
- aMs[i]
) contains the
models according to class i
.s
... . s
in order (s[0]
contains foreground data, s[1]
contains
background data)
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
classifierClassifierAssessment.ClassifierAssessment(boolean, TrainableStatisticalModel[][])
public Sampled_RepeatedHoldOutExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
AbstractClassifier
s and those constructed using the given
TrainableStatisticalModel
s by a
Sampled_RepeatedHoldOutExperiment
.
aCs
- contains some AbstractClassifier
that should be
assessed in addition to the AbstractClassifier
s
constructed using the given
TrainableStatisticalModel
sbuildClassifiersByCrossProduct
- 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
- aMs[i]
) contains the
models according to class i
.s
... . s
in order (s[0]
contains foreground data, s[1]
contains
background data)
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
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier[],
boolean, TrainableStatisticalModel[][])
Method Detail |
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protected void evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, ClassifierAssessmentAssessParameterSet assessPS, DataSet[] s, ProgressUpdater pU) throws IllegalArgumentException, Exception
ClassifierAssessment
train()
to train classifiers/models using
train data test()
to cause evaluation (test) of trained
classifiers
evaluateClassifier
in class ClassifierAssessment
mp
- defines which performance measures are used to assess
classifiersassessPS
- 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. pU.setMax()
= number of iterations of the assessment loop
pU.setValue()
=iteration+1;
train()
;
test()
;
pU.isCancelled()
))
IllegalArgumentException
- if the given ClassifierAssessmentAssessParameterSet
is of wrong type
Exception
- that occurred during training or using classifiers/models
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