public class FilteredClassifier extends SingleClassifierEnhancer implements Drawable, PartitionGenerator, IterativeClassifier, BatchPredictor
-F <filter specification> Full class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U Use unpruned tree.
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of instances> Set minimum number of instances per leaf. (default 2)
-R Use reduced error pruning.
-N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
-B Use binary splits only.
-S Don't perform subtree raising.
-L Do not clean up after the tree has been built.
-A Laplace smoothing for predicted probabilities.
-Q <seed> Seed for random data shuffling (default 1).
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULTBayesNet, Newick, NOT_DRAWABLE, TREE| Constructor and Description |
|---|
FilteredClassifier()
Default constructor.
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| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
batchSizeTipText()
Tool tip text for this property
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void |
buildClassifier(Instances data)
Build the classifier on the filtered data.
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double[] |
distributionForInstance(Instance instance)
Classifies a given instance after filtering.
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double[][] |
distributionsForInstances(Instances insts)
Batch scoring method.
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void |
done()
Signal end of iterating, useful for any house-keeping/cleanup (If the base
classifier supports this.)
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java.lang.String |
filterTipText()
Returns the tip text for this property
|
void |
generatePartition(Instances data)
Builds the classifier to generate a partition.
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java.lang.String |
getBatchSize()
Gets the preferred batch size from the base learner if it implements
BatchPredictor.
|
Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
|
Filter |
getFilter()
Gets the filter used.
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double[] |
getMembershipValues(Instance inst)
Computes an array that has a value for each element in the partition.
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java.lang.String[] |
getOptions()
Gets the current settings of the Classifier.
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java.lang.String |
getRevision()
Returns the revision string.
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java.lang.String |
globalInfo()
Returns a string describing this classifier
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java.lang.String |
graph()
Returns graph describing the classifier (if possible).
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int |
graphType()
Returns the type of graph this classifier represents.
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boolean |
implementsMoreEfficientBatchPrediction()
Returns true if the base classifier implements BatchPredictor and is able
to generate batch predictions efficiently
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void |
initializeClassifier(Instances data)
Initializes an iterative classifier.
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java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
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static void |
main(java.lang.String[] argv)
Main method for testing this class.
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boolean |
next()
Performs one iteration.
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int |
numElements()
Returns the number of elements in the partition.
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void |
setBatchSize(java.lang.String size)
Set the batch size to use.
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void |
setDoNotCheckForModifiedClassAttribute(boolean flag)
Use this method to determine whether classifier checks whether class attribute has been modified by filter.
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void |
setFilter(Filter filter)
Sets the filter
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void |
setOptions(java.lang.String[] options)
Parses a given list of options.
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java.lang.String |
toString()
Output a representation of this classifier
|
classifierTipText, getClassifier, postExecution, preExecution, setClassifierclassifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlacesequals, getClass, hashCode, notify, notifyAll, wait, wait, waitclassifyInstancepublic java.lang.String globalInfo()
public void setDoNotCheckForModifiedClassAttribute(boolean flag)
public int graphType()
public java.lang.String graph()
throws java.lang.Exception
public void generatePartition(Instances data) throws java.lang.Exception
generatePartition in interface PartitionGeneratorjava.lang.Exceptionpublic double[] getMembershipValues(Instance inst) throws java.lang.Exception
getMembershipValues in interface PartitionGeneratorjava.lang.Exceptionpublic int numElements()
throws java.lang.Exception
numElements in interface PartitionGeneratorjava.lang.Exceptionpublic void initializeClassifier(Instances data) throws java.lang.Exception
initializeClassifier in interface IterativeClassifierdata - the instances to be used in inductionjava.lang.Exception - if the model cannot be initializedpublic boolean next()
throws java.lang.Exception
next in interface IterativeClassifierjava.lang.Exception - if this iteration fails for unexpected reasonspublic void done()
throws java.lang.Exception
done in interface IterativeClassifierjava.lang.Exception - if cleanup failspublic java.util.Enumeration<Option> listOptions()
listOptions in interface OptionHandlerlistOptions in class SingleClassifierEnhancerpublic void setOptions(java.lang.String[] options)
throws java.lang.Exception
-F <filter specification> Full class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2"
-D If set, classifier is run in debug mode and may output additional info to the console
-W Full name of base classifier. (default: weka.classifiers.trees.J48)
Options specific to classifier weka.classifiers.trees.J48:
-U Use unpruned tree.
-C <pruning confidence> Set confidence threshold for pruning. (default 0.25)
-M <minimum number of instances> Set minimum number of instances per leaf. (default 2)
-R Use reduced error pruning.
-N <number of folds> Set number of folds for reduced error pruning. One fold is used as pruning set. (default 3)
-B Use binary splits only.
-S Don't perform subtree raising.
-L Do not clean up after the tree has been built.
-A Laplace smoothing for predicted probabilities.
-Q <seed> Seed for random data shuffling (default 1).
setOptions in interface OptionHandlersetOptions in class SingleClassifierEnhanceroptions - the list of options as an array of stringsjava.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class SingleClassifierEnhancerpublic java.lang.String filterTipText()
public void setFilter(Filter filter)
filter - the filter with all options set.public Filter getFilter()
public Capabilities getCapabilities()
getCapabilities in interface ClassifiergetCapabilities in interface CapabilitiesHandlergetCapabilities in class SingleClassifierEnhancerCapabilitiespublic void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier in interface Classifierdata - the training datajava.lang.Exception - if the classifier could not be built successfullypublic double[] distributionForInstance(Instance instance) throws java.lang.Exception
distributionForInstance in interface ClassifierdistributionForInstance in class AbstractClassifierinstance - the instance to be classifiedjava.lang.Exception - if instance could not be classified successfullypublic java.lang.String batchSizeTipText()
batchSizeTipText in class AbstractClassifierpublic void setBatchSize(java.lang.String size)
setBatchSize in interface BatchPredictorsetBatchSize in class AbstractClassifiersize - the batch size to usepublic java.lang.String getBatchSize()
getBatchSize in interface BatchPredictorgetBatchSize in class AbstractClassifierpublic double[][] distributionsForInstances(Instances insts) throws java.lang.Exception
distributionsForInstances in interface BatchPredictordistributionsForInstances in class AbstractClassifierinsts - the instances to get predictions forjava.lang.Exception - if a problem occurspublic boolean implementsMoreEfficientBatchPrediction()
implementsMoreEfficientBatchPrediction in interface BatchPredictorimplementsMoreEfficientBatchPrediction in class AbstractClassifierpublic java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class AbstractClassifierpublic static void main(java.lang.String[] argv)
argv - should contain the following arguments: -t training file [-T
test file] [-c class index]