public class RepeatedSubSamplingExperiment extends ClassifierAssessment<RepeatedSubSamplingAssessParameterSet>
myAbstractClassifier, myModel, myTempMeanResultSets, skipLastClassifiersDuringClassifierTraining
Modifier | Constructor and Description |
---|---|
|
RepeatedSubSamplingExperiment(AbstractClassifier... aCs)
Creates a new
RepeatedSubSamplingExperiment from a set of
AbstractClassifier s. |
|
RepeatedSubSamplingExperiment(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
RepeatedSubSamplingExperiment . |
protected |
RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
RepeatedSubSamplingExperiment from an array of
AbstractClassifier s and a two-dimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. |
|
RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new
RepeatedSubSamplingExperiment from a set of
TrainableStatisticalModel s. |
Modifier and Type | Method and Description |
---|---|
protected void |
evaluateClassifier(NumericalPerformanceMeasureParameterSet mp,
RepeatedSubSamplingAssessParameterSet assessPS,
DataSet[] s,
double[][] weights,
ProgressUpdater pU)
Evaluates the classifier.
|
RepeatedSubSamplingAssessParameterSet |
getAssessParameterSet()
This method returns an instance of
ClassifierAssessmentAssessParameterSet that can be used in the assess methods. |
assess, assess, assess, assess, getClassifier, getNameOfAssessment, prepareAssessment, test, train
protected RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
RepeatedSubSamplingExperiment
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
consistencyIllegalArgumentException
- if the classifiers have different lengthsWrongAlphabetException
- if the classifiers use different alphabetsCloneNotSupportedException
- if something went wrong while cloningClassDimensionException
- if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier[],
TrainableStatisticalModel[][], boolean, boolean)
public RepeatedSubSamplingExperiment(AbstractClassifier... aCs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
RepeatedSubSamplingExperiment
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 lengthsWrongAlphabetException
- if not all given classifiers are defined on the same
AlphabetContainer
CloneNotSupportedException
- if something went wrong while cloningClassDimensionException
- if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier...)
public RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
RepeatedSubSamplingExperiment
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 lengthsWrongAlphabetException
- if not all given classifiers are defined on the same
AlphabetContainer
CloneNotSupportedException
- if something went wrong while cloningClassDimensionException
- if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(boolean, TrainableStatisticalModel[][])
public RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
AbstractClassifier
s and those constructed using the given
TrainableStatisticalModel
s by a
RepeatedSubSamplingExperiment
. aCs
- contains some AbstractClassifier
that should be
assessed in addition to the AbstractClassifier
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 lengthsWrongAlphabetException
- if not all given classifiers are defined on the same
AlphabetContainer
CloneNotSupportedException
- if something went wrong while cloningClassDimensionException
- if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier[],
boolean, TrainableStatisticalModel[][])
protected void evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, RepeatedSubSamplingAssessParameterSet assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU) throws IllegalArgumentException, Exception
evaluateClassifier
in class ClassifierAssessment<RepeatedSubSamplingAssessParameterSet>
mp
- defines which performance measures are used to assess
classifierspU
- allows to abort this assessment by setting
pU.isCancelled()=true
. The last step of this
assessment is continued afterwards this assessment stops.s
- contains the data to be used for assessment. The order of the
data sets is important. s
. If
the models are trained directly, the order of given models
during initiation of this assessment object determines, which
data set will be used for training which model. In general the
first model will be trained using the first data set in
s
... . s
in order (s[0]
contains foreground data, s[1]
contains
background data)
assessPS
- contains parameters for a run of this
RepeatedSubSamplingExperiment
. Must be of type
RepeatedSubSamplingExperiment
.weights
- the (non-negative) weights for the data;
weight for each data set (first dimension) and each sequence (second dimension),
can be null
which is the same as weight 1 for all sequences in all data setsIllegalArgumentException
- if the given assessPS
is not of type
RepeatedSubSamplingExperiment
Exception
- if something went wrongpublic RepeatedSubSamplingAssessParameterSet getAssessParameterSet() throws Exception
ClassifierAssessment
ClassifierAssessmentAssessParameterSet
that can be used in the assess
methods.getAssessParameterSet
in class ClassifierAssessment<RepeatedSubSamplingAssessParameterSet>
ClassifierAssessmentAssessParameterSet
that can be used in the assess
methods.Exception
- if the parameter set could not be created properlyClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet...)
,
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, ProgressUpdater, DataSet[])
,
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, ProgressUpdater, DataSet[], double[][])