weka.distributed.hadoop

Class WekaClassifierHadoopJob

java.lang.Object

distributed.core.DistributedJob

weka.distributed.hadoop.HadoopJob

weka.distributed.hadoop.WekaClassifierHadoopJob

All Implemented Interfaces:

java.io.Serializable, CommandlineRunnable, EnvironmentHandler, OptionHandler, ClassifierProducer

public class WekaClassifierHadoopJob
extends HadoopJob
implements ClassifierProducer, CommandlineRunnable

Hadoop job for building a classifier or regressor.

Version:

$Revision: 11162 $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com)

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class distributed.core.DistributedJob

distributed.core.DistributedJob.JobStatus

Field Summary

Fields inherited from class weka.distributed.hadoop.HadoopJob

COLT_JAR, DISTRIBUTED_WEKA_BASE_JAR, DISTRIBUTED_WEKA_HADOOP_JAR, JCOMMON_JAR, JFREECHART_JAR, LA4J_JAR, OPEN_CSV_JAR

Fields inherited from class distributed.core.DistributedJob

WEKA_ADDITIONAL_PACKAGES_KEY

Constructor Summary

Constructors

Constructor and Description
WekaClassifierHadoopJob() Constructor

Method Summary

Methods

Modifier and Type	Method and Description
java.lang.String	classAttributeTipText() Tip text for this property
java.lang.String	createRandomizedDataChunksTipText() Tip text for this property
java.lang.String	getClassAttribute() Get the name or index of the class attribute ("first" and "last" can also be used)
Classifier	getClassifier()
java.lang.String	getClassifierMapTaskOptions() Get the options for the underlying map task
boolean	getCreateRandomizedDataChunks() Get whether to create randomly shuffled (and stratified if the class is nominal) data chunks via a pre-processing pass/job.
java.lang.String	getCSVMapTaskOptions() Get the options to the header job
java.lang.String[]	getJobOptionsOnly() Get the options for this job only
java.lang.String	getMinTrainingFraction() Get the minimum training fraction.
java.lang.String	getModelFileName() Get the name only for the model file
java.lang.String	getNumInstancesPerRandomizedDataChunk() Get the number of instances that each randomly shuffled data chunk should have.
int	getNumIterations() Get the number of iterations (passes over the data) to run in the model building phase.
java.lang.String	getNumRandomizedDataChunks() Get the number of randomly shuffled data chunks to create.
java.lang.String[]	getOptions()
java.lang.String	getPathToPreconstructedFilter() Get the path to a pre-constructed filter to use to pre-process the data entering each map.
Instances	getTrainingHeader()
java.lang.String	globalInfo() Help information
java.util.Enumeration<Option>	listOptions()
static void	main(java.lang.String[] args)
java.lang.String	minTrainingFractionTipText() Tip text for this property
java.lang.String	modelFileNameTipText() Tip text for this property
java.lang.String	numInstancesPerRandomizedDataChunkTipText() Tip text for this property
java.lang.String	numIterationsTipText() Tip text for this property
java.lang.String	numRandomizedDataChunksTipText() Tip text for this property
java.lang.String	pathToPreconstructedFilterTipText() Tip text for this property
void	run(java.lang.Object toRun, java.lang.String[] args)
boolean	runJob()
void	setClassAttribute(java.lang.String c) Set the name or index of the class attribute ("first" and "last" can also be used)
void	setClassifierMapTaskOptions(java.lang.String opts) Set the options for the underlying map task
void	setCreateRandomizedDataChunks(boolean s) Set whether to create randomly shuffled (and stratified if the class is nominal) data chunks via a pre-processing pass/job.
void	setCSVMapTaskOptions(java.lang.String opts) Set the options to the header job
void	setMinTrainingFraction(java.lang.String frac) Set the minimum training fraction.
void	setModelFileName(java.lang.String m) Set the name only for the model file
void	setNumInstancesPerRandomizedDataChunk(java.lang.String insts) Set the number of instances that each randomly shuffled data chunk should have.
void	setNumIterations(int i) Set the number of iterations (passes over the data) to run in the model building phase.
void	setNumRandomizedDataChunks(java.lang.String chunks) Set the number of randomly shuffled data chunks to create.
void	setOptions(java.lang.String[] options)
void	setPathToPreconstructedFilter(java.lang.String path) Set the path to a pre-constructed filter to use to pre-process the data entering each map.
void	stopJob()

Methods inherited from class weka.distributed.hadoop.HadoopJob

additionalWekaPackagesTipText, cleanOutputDirectory, deubgTipText, getAdditionalWekaPackages, getBaseOptionsOnly, getDebug, getLoggingInterval, getMapNumber, getMapReduceJobConfig, getMapReduceNumber, getPathToWekaJar, getReduceNumber, loggingIntervalTipText, pathToWekaJarTipText, setAdditionalWekaPackages, setDebug, setLoggingInterval, setMapReduceJobConfig, setPathToWekaJar

Methods inherited from class distributed.core.DistributedJob

environmentSubstitute, getAdditionalWekaPackageNames, getJobName, getJobStatus, getLog, makeOptionsStr, parseInstance, setEnvironment, setJobDescription, setJobName, setJobStatus, setLog, setStatusMessagePrefix

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

WekaClassifierHadoopJob

public WekaClassifierHadoopJob()

Constructor

Method Detail

globalInfo

public java.lang.String globalInfo()

Help information

Returns:

help information for this job

modelFileNameTipText

public java.lang.String modelFileNameTipText()

Tip text for this property

Returns:

tip text for this property

setModelFileName

public void setModelFileName(java.lang.String m)

Set the name only for the model file

Parameters:

m - the name only (not full path) that the model should be saved to

getModelFileName

public java.lang.String getModelFileName()

Get the name only for the model file

Returns:

the name only (not full path) that the model should be saved to

numIterationsTipText

public java.lang.String numIterationsTipText()

Tip text for this property

Returns:

tip text for this property

setNumIterations

public void setNumIterations(int i)

Set the number of iterations (passes over the data) to run in the model building phase. > 1 only makes sense for incremental classifiers such as SGD that converge on a solution.

Parameters:

i - the number of iterations to run

getNumIterations

public int getNumIterations()

Get the number of iterations (passes over the data) to run in the model building phase. > 1 only makes sense for incremental classifiers such as SGD that converge on a solution.

Returns:

the number of iterations to run

classAttributeTipText

public java.lang.String classAttributeTipText()

Tip text for this property

Returns:

tip text for this property

setClassAttribute

public void setClassAttribute(java.lang.String c)

Set the name or index of the class attribute ("first" and "last" can also be used)

Parameters:

c - the name or index of the class attribute

getClassAttribute

public java.lang.String getClassAttribute()

Get the name or index of the class attribute ("first" and "last" can also be used)

Returns:

the name or index of the class attribute

setCSVMapTaskOptions

public void setCSVMapTaskOptions(java.lang.String opts)

Set the options to the header job

Parameters:

opts - options to the header job

getCSVMapTaskOptions

public java.lang.String getCSVMapTaskOptions()

Get the options to the header job

Returns:

options to the header job

setClassifierMapTaskOptions

public void setClassifierMapTaskOptions(java.lang.String opts)

Set the options for the underlying map task

Parameters:

opts - the options for the underlying map task

getClassifierMapTaskOptions

public java.lang.String getClassifierMapTaskOptions()

Get the options for the underlying map task

Returns:

the options for the underlying map task

minTrainingFractionTipText

public java.lang.String minTrainingFractionTipText()

Tip text for this property

Returns:

tip text for this property

setMinTrainingFraction

public void setMinTrainingFraction(java.lang.String frac)

Set the minimum training fraction. This is a percentage of the total number of instances seen by the map task that has seen the most data. This option is useful when not using randomly shuffled data chunks, as there may be one input split that contains significantly less data than all the others and we might want to discard the model learned on this chunk.

Parameters:

frac - the fraction of training instances below which a model should be discarded from the aggregation

getMinTrainingFraction

public java.lang.String getMinTrainingFraction()

Get the minimum training fraction. This is a percentage of the total number of instances seen by the map task that has seen the most data. This option is useful when not using randomly shuffled data chunks, as there may be one input split that contains significantly less data than all the others and we might want to discard the model learned on this chunk.

Returns:

the fraction of training instances below which a model should be discarded from the aggregation

pathToPreconstructedFilterTipText

public java.lang.String pathToPreconstructedFilterTipText()

Tip text for this property

Returns:

tip text for this property

setPathToPreconstructedFilter

public void setPathToPreconstructedFilter(java.lang.String path)

Set the path to a pre-constructed filter to use to pre-process the data entering each map. This path may be inside or outside of HDFS.

Parameters:

path - the path to a pre-constructed filter to use

getPathToPreconstructedFilter

public java.lang.String getPathToPreconstructedFilter()

Get the path to a pre-constructed filter to use to pre-process the data entering each map. This path may be inside or outside of HDFS.

Returns:

the path to a pre-constructed filter to use

createRandomizedDataChunksTipText

public java.lang.String createRandomizedDataChunksTipText()

Tip text for this property

Returns:

tip text for this property

setCreateRandomizedDataChunks

public void setCreateRandomizedDataChunks(boolean s)

Set whether to create randomly shuffled (and stratified if the class is nominal) data chunks via a pre-processing pass/job.

Parameters:

s - true if randomly shuffled data chunks are to be created for input

getCreateRandomizedDataChunks

public boolean getCreateRandomizedDataChunks()

Get whether to create randomly shuffled (and stratified if the class is nominal) data chunks via a pre-processing pass/job.

Returns:

true if randomly shuffled data chunks are to be created for input

numRandomizedDataChunksTipText

public java.lang.String numRandomizedDataChunksTipText()

Tip text for this property

Returns:

tip text for this property

setNumRandomizedDataChunks

public void setNumRandomizedDataChunks(java.lang.String chunks)

Set the number of randomly shuffled data chunks to create. Use in conjunction with createRandomizedDataChunks.

Parameters:

chunks - the number of chunks to create.

getNumRandomizedDataChunks

public java.lang.String getNumRandomizedDataChunks()

Get the number of randomly shuffled data chunks to create. Use in conjunction with createRandomizedDataChunks.

Returns:

the number of chunks to create.

numInstancesPerRandomizedDataChunkTipText

public java.lang.String numInstancesPerRandomizedDataChunkTipText()

Tip text for this property

Returns:

tip text for this property

setNumInstancesPerRandomizedDataChunk

public void setNumInstancesPerRandomizedDataChunk(java.lang.String insts)

Set the number of instances that each randomly shuffled data chunk should have. Use in conjunction with createRandomizedDataChunks.

Parameters:

insts - the number of instances that each randomly shuffled data chunk should contain

getNumInstancesPerRandomizedDataChunk

public java.lang.String getNumInstancesPerRandomizedDataChunk()

Get the number of instances that each randomly shuffled data chunk should have. Use in conjunction with createRandomizedDataChunks.

Returns:

the number of instances that each randomly shuffled data chunk should contain

listOptions

public java.util.Enumeration<Option> listOptions()

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class HadoopJob

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class HadoopJob

Throws:

java.lang.Exception

getOptions

public java.lang.String[] getOptions()

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class HadoopJob

getJobOptionsOnly

public java.lang.String[] getJobOptionsOnly()

Get the options for this job only

Returns:

the options for this job only

runJob

public boolean runJob()
               throws weka.distributed.DistributedWekaException

Specified by:

runJob in class distributed.core.DistributedJob

Throws:

weka.distributed.DistributedWekaException

getClassifier

public Classifier getClassifier()

Specified by:

getClassifier in interface ClassifierProducer

stopJob

public void stopJob()

Overrides:

stopJob in class distributed.core.DistributedJob

getTrainingHeader

public Instances getTrainingHeader()

Specified by:

getTrainingHeader in interface ClassifierProducer

main

public static void main(java.lang.String[] args)

run

public void run(java.lang.Object toRun,
       java.lang.String[] args)
         throws java.lang.IllegalArgumentException

Specified by:

run in interface CommandlineRunnable

Throws:

java.lang.IllegalArgumentException