AutoWEKAClassifier

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.meta.AutoWEKAClassifier

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler
```
public class AutoWEKAClassifier
extends weka.classifiers.AbstractClassifier
implements weka.core.AdditionalMeasureProducer
```
Auto-WEKA interface for WEKA. * @author Lars Kotthoff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected weka.attributeSelection.AttributeSelection`	`as` The chosen attribute selection method.
`protected java.lang.String[]`	`attributeEvalArgs` The arguments of the chosen attribute evaluation method.
`protected java.lang.String`	`attributeEvalClass` The class of the chosen attribute evaluation.
`protected java.lang.String[]`	`attributeSearchArgs` The arguments of the chosen attribute search method.
`protected java.lang.String`	`attributeSearchClass` The class of the chosen attribute search method.
`protected ConfigurationCollection`	`cc` The best configurations.
`protected weka.classifiers.Classifier`	`classifier` The chosen classifier.
`protected java.lang.String[]`	`classifierArgs` The arguments of the chosen classifier.
`protected java.lang.String`	`classifierClass` The class of the chosen classifier.
`static java.lang.String`	`configurationHashSetPath` The path for the log with the hashcodes for the configs we have
`static java.lang.String`	`configurationInfoDirPath` The path for the directory with the configuration data and score
`static java.lang.String`	`configurationRankingPath` The path for the sorted best configurations
`protected int`	`DEFAULT_PARALLEL_RUNS` The default number of parallel threads.
`protected double`	`estimatedMetricValue` The estimated metric value of the method chosen out of the parallel runs.
`protected double[]`	`estimatedMetricValues` The estimated metric values of the chosen methods for each parallel run.
`protected weka.classifiers.Evaluation`	`eval` The evaluation for the best classifier.
`protected static java.lang.String`	`expName` The internal name of the experiment.
`protected java.lang.String`	`extraArgs` The extra arguments for Auto-WEKA.
`protected double`	`finalTrainTime` The time it took to train the final classifier.
`protected int`	`memLimit` The memory limit for running classifiers.
`protected weka.classifiers.meta.AutoWEKAClassifier.Metric`	`metric` The error metric.
`protected static java.lang.String[]`	`msExperimentPaths` The paths to the internal Auto-WEKA files.
`protected int`	`nBestConfigs` The number of best configurations to return as output.
`protected int`	`parallelRuns` The number of parallel threads.
`protected weka.classifiers.meta.AutoWEKAClassifier.Resampling`	`resampling` The internal evaluation method.
`protected java.lang.String`	`resamplingArgs` The arguments to the evaluation method.
`protected int`	`seed` The random seed.
`protected int`	`timeLimit` The time limit for running Auto-WEKA.

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

AutoWEKAClassifier()
Constructs a new AutoWEKAClassifier.

Constructors
Constructor and Description
`AutoWEKAClassifier()` Constructs a new AutoWEKAClassifier.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(weka.core.Instances is)` Find the best classifier, arguments, and attribute selection for the data.
`double`	`classifyInstance(weka.core.Instance i)` Calculates the class membership for the given test instance.
`double[]`	`distributionForInstance(weka.core.Instance i)` Calculates the class membership probabilities for the given test instance.
`java.util.Enumeration`	`enumerateMeasures()` Returns an enumeration of the additional measure names
`weka.core.Capabilities`	`getCapabilities()` Returns default capabilities of the classifier.
`double`	`getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
`int`	`getMemLimit()` Get the memory limit.
`weka.classifiers.meta.AutoWEKAClassifier.Metric`	`getMetric()` Get the metric.
`int`	`getnBestConfigs()` Get the memory limit.
`java.lang.String[]`	`getOptions()` Returns the options of the current setup.
`int`	`getParallelRuns()` Get the number of runs to do in parallel.
`int`	`getSeed()` Get the random seed.
`weka.core.TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`int`	`getTimeLimit()` Get the time limit.
`java.lang.String`	`globalInfo()` This will return a string describing the classifier.
`java.util.Enumeration<weka.core.Option>`	`listOptions()` Gets an enumeration describing the available options.
`static void`	`main(java.lang.String[] argv)` Main method for testing this class.
`double`	`measureEstimatedMetricValue()` Returns the metric value estimated during Auto-WEKA's internal evaluation.
`java.lang.String`	`memLimitTipText()` Returns the tip text for this property.
`java.lang.String`	`metricTipText()` Returns the tip text for this property.
`java.lang.String`	`nBestConfigsTipText()` Returns the tip text for this property.
`java.lang.String`	`parallelRunsTipText()` Returns the tip text for this property.
`java.lang.String`	`seedTipText()` Returns the tip text for this property.
`void`	`setLog(weka.gui.Logger log)` Set the WEKA logger.
`void`	`setMemLimit(int ml)` Set the memory limit.
`void`	`setMetric(weka.classifiers.meta.AutoWEKAClassifier.Metric m)` Set the metric.
`void`	`setnBestConfigs(int nbc)` Set the amount of configurations that will be given as output
`void`	`setOptions(java.lang.String[] options)` Set the options for the current setup.
`void`	`setParallelRuns(int n)` Set the number of parallel runs.
`void`	`setSeed(int s)` Set the random seed.
`void`	`setTimeLimit(int tl)` Set the time limit.
`java.lang.String`	`timeLimitTipText()` Returns the tip text for this property.
`java.lang.String`	`toString()` This will return a string describing the classifier.

Methods inherited from class weka.classifiers.AbstractClassifier
batchSizeTipText, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getRevision, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - configurationRankingPath
```
public static final java.lang.String configurationRankingPath
```
    The path for the sorted best configurations
  - configurationHashSetPath
```
public static final java.lang.String configurationHashSetPath
```
    The path for the log with the hashcodes for the configs we have
  - configurationInfoDirPath
```
public static final java.lang.String configurationInfoDirPath
```
    The path for the directory with the configuration data and score
  - classifier
```
protected weka.classifiers.Classifier classifier
```
    The chosen classifier.
  - as
```
protected weka.attributeSelection.AttributeSelection as
```
    The chosen attribute selection method.
  - classifierClass
```
protected java.lang.String classifierClass
```
    The class of the chosen classifier.
  - classifierArgs
```
protected java.lang.String[] classifierArgs
```
    The arguments of the chosen classifier.
  - attributeSearchClass
```
protected java.lang.String attributeSearchClass
```
    The class of the chosen attribute search method.
  - attributeSearchArgs
```
protected java.lang.String[] attributeSearchArgs
```
    The arguments of the chosen attribute search method.
  - attributeEvalClass
```
protected java.lang.String attributeEvalClass
```
    The class of the chosen attribute evaluation.
  - attributeEvalArgs
```
protected java.lang.String[] attributeEvalArgs
```
    The arguments of the chosen attribute evaluation method.
  - msExperimentPaths
```
protected static java.lang.String[] msExperimentPaths
```
    The paths to the internal Auto-WEKA files.
  - expName
```
protected static java.lang.String expName
```
    The internal name of the experiment.
  - seed
```
protected int seed
```
    The random seed.
  - timeLimit
```
protected int timeLimit
```
    The time limit for running Auto-WEKA.
  - memLimit
```
protected int memLimit
```
    The memory limit for running classifiers.
  - nBestConfigs
```
protected int nBestConfigs
```
    The number of best configurations to return as output.
  - cc
```
protected ConfigurationCollection cc
```
    The best configurations.
  - resampling
```
protected weka.classifiers.meta.AutoWEKAClassifier.Resampling resampling
```
    The internal evaluation method.
  - resamplingArgs
```
protected java.lang.String resamplingArgs
```
    The arguments to the evaluation method.
  - extraArgs
```
protected java.lang.String extraArgs
```
    The extra arguments for Auto-WEKA.
  - metric
```
protected weka.classifiers.meta.AutoWEKAClassifier.Metric metric
```
    The error metric.
  - estimatedMetricValues
```
protected double[] estimatedMetricValues
```
    The estimated metric values of the chosen methods for each parallel run.
  - estimatedMetricValue
```
protected double estimatedMetricValue
```
    The estimated metric value of the method chosen out of the parallel runs.
  - eval
```
protected weka.classifiers.Evaluation eval
```
    The evaluation for the best classifier.
  - DEFAULT_PARALLEL_RUNS
```
protected final int DEFAULT_PARALLEL_RUNS
```
    The default number of parallel threads.
    
    See Also:
    
    Constant Field Values
  - parallelRuns
```
protected int parallelRuns
```
    The number of parallel threads.
  - finalTrainTime
```
protected double finalTrainTime
```
    The time it took to train the final classifier.
- Constructor Detail
  - AutoWEKAClassifier
```
public AutoWEKAClassifier()
```
    Constructs a new AutoWEKAClassifier.
- Method Detail
  - main
```
public static void main(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - should contain command line options (see setOptions)
  - buildClassifier
```
public void buildClassifier(weka.core.Instances is)
                     throws java.lang.Exception
```
    Find the best classifier, arguments, and attribute selection for the data.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.Classifier
    
    Parameters:
    
    is - the training data to be used for selecting and tuning the classifier.
    
    Throws:
    
    java.lang.Exception - if the classifier could not be built successfully.
  - classifyInstance
```
public double classifyInstance(weka.core.Instance i)
                        throws java.lang.Exception
```
    Calculates the class membership for the given test instance.
    
    Specified by:
    
    classifyInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    classifyInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    i - the instance to be classified
    
    Returns:
    
    predicted class
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - distributionForInstance
```
public double[] distributionForInstance(weka.core.Instance i)
                                 throws java.lang.Exception
```
    Calculates the class membership probabilities for the given test instance.
    
    Specified by:
    
    distributionForInstance in interface weka.classifiers.Classifier
    
    Overrides:
    
    distributionForInstance in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    i - the instance to be classified
    
    Returns:
    
    predicted class probability distribution
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully.
  - listOptions
```
public java.util.Enumeration<weka.core.Option> listOptions()
```
    Gets an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    listOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    an enumeration of all the available options.
  - getOptions
```
public java.lang.String[] getOptions()
```
    Returns the options of the current setup.
    
    Specified by:
    
    getOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    getOptions in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the current options
  - setOptions
```
public void setOptions(java.lang.String[] options)
                throws java.lang.Exception
```
    Set the options for the current setup.
    
    Specified by:
    
    setOptions in interface weka.core.OptionHandler
    
    Overrides:
    
    setOptions in class weka.classifiers.AbstractClassifier
    
    Parameters:
    
    options - the new options
    
    Throws:
    
    java.lang.Exception
  - setSeed
```
public void setSeed(int s)
```
    Set the random seed.
    
    Parameters:
    
    s - The random seed.
  - getSeed
```
public int getSeed()
```
    Get the random seed.
    
    Returns:
    
    The random seed.
  - seedTipText
```
public java.lang.String seedTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setParallelRuns
```
public void setParallelRuns(int n)
```
    Set the number of parallel runs.
    
    Parameters:
    
    n - The number of parallel runs.
  - getParallelRuns
```
public int getParallelRuns()
```
    Get the number of runs to do in parallel.
    
    Returns:
    
    The number of parallel runs.
  - parallelRunsTipText
```
public java.lang.String parallelRunsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMetric
```
public void setMetric(weka.classifiers.meta.AutoWEKAClassifier.Metric m)
```
    Set the metric.
    
    Parameters:
    
    m - The metric.
  - getMetric
```
public weka.classifiers.meta.AutoWEKAClassifier.Metric getMetric()
```
    Get the metric.
    
    Returns:
    
    The metric.
  - metricTipText
```
public java.lang.String metricTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setTimeLimit
```
public void setTimeLimit(int tl)
```
    Set the time limit.
    
    Parameters:
    
    tl - The time limit in minutes.
  - getTimeLimit
```
public int getTimeLimit()
```
    Get the time limit.
    
    Returns:
    
    The time limit in minutes.
  - timeLimitTipText
```
public java.lang.String timeLimitTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setMemLimit
```
public void setMemLimit(int ml)
```
    Set the memory limit.
    
    Parameters:
    
    ml - The memory limit in MiB.
  - getMemLimit
```
public int getMemLimit()
```
    Get the memory limit.
    
    Returns:
    
    The memory limit in MiB.
  - memLimitTipText
```
public java.lang.String memLimitTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setnBestConfigs
```
public void setnBestConfigs(int nbc)
```
    Set the amount of configurations that will be given as output
    
    Parameters:
    
    nbc - The amount of best configurations desired by the user
  - getnBestConfigs
```
public int getnBestConfigs()
```
    Get the memory limit.
    
    Returns:
    
    The amount of best configurations that will be given as output
  - nBestConfigsTipText
```
public java.lang.String nBestConfigsTipText()
```
    Returns the tip text for this property.
    
    Returns:
    
    tip text for this property
  - setLog
```
public void setLog(weka.gui.Logger log)
```
    Set the WEKA logger. Used for providing feedback during execution.
    
    Parameters:
    
    log - The logger.
  - getCapabilities
```
public weka.core.Capabilities getCapabilities()
```
    Returns default capabilities of the classifier.
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Specified by:
    
    getCapabilities in interface weka.core.CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - getTechnicalInformation
```
public weka.core.TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Returns:
    
    the technical information about this class
  - globalInfo
```
public java.lang.String globalInfo()
```
    This will return a string describing the classifier.
    
    Returns:
    
    The string.
  - toString
```
public java.lang.String toString()
```
    This will return a string describing the classifier.
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    
    The string.
  - measureEstimatedMetricValue
```
public double measureEstimatedMetricValue()
```
    Returns the metric value estimated during Auto-WEKA's internal evaluation.
    
    Returns:
    
    The estimated metric value.
  - enumerateMeasures
```
public java.util.Enumeration enumerateMeasures()
```
    Returns an enumeration of the additional measure names
    
    Specified by:
    
    enumerateMeasures in interface weka.core.AdditionalMeasureProducer
    
    Returns:
    
    an enumeration of the measure names
  - getMeasure
```
public double getMeasure(java.lang.String additionalMeasureName)
```
    Returns the value of the named measure
    
    Specified by:
    
    getMeasure in interface weka.core.AdditionalMeasureProducer
    
    Parameters:
    
    additionalMeasureName - the name of the measure to query for its value
    
    Returns:
    
    the value of the named measure
    
    Throws:
    
    java.lang.IllegalArgumentException - if the named measure is not supported

Class AutoWEKAClassifier

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

configurationRankingPath

configurationHashSetPath

configurationInfoDirPath

classifier

as

classifierClass

classifierArgs

attributeSearchClass

attributeSearchArgs

attributeEvalClass

attributeEvalArgs

msExperimentPaths

expName

seed

timeLimit

memLimit

nBestConfigs

cc

resampling

resamplingArgs

extraArgs

metric

estimatedMetricValues

estimatedMetricValue

eval

DEFAULT_PARALLEL_RUNS

parallelRuns

finalTrainTime

Constructor Detail

AutoWEKAClassifier

Method Detail

main

buildClassifier

classifyInstance

distributionForInstance

listOptions

getOptions

setOptions

setSeed

getSeed

seedTipText

setParallelRuns

getParallelRuns

parallelRunsTipText

setMetric

getMetric

metricTipText

setTimeLimit

getTimeLimit

timeLimitTipText

setMemLimit

getMemLimit

memLimitTipText

setnBestConfigs

getnBestConfigs

nBestConfigsTipText

setLog

getCapabilities

getTechnicalInformation

globalInfo

toString

measureEstimatedMetricValue

enumerateMeasures

getMeasure