org.tribuo.anomaly.liblinear

Class LibLinearAnomalyTrainer

java.lang.Object

org.tribuo.common.liblinear.LibLinearTrainer<Event>

org.tribuo.anomaly.liblinear.LibLinearAnomalyTrainer

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>, Trainer<Event>

public class LibLinearAnomalyTrainer
extends LibLinearTrainer<Event>

A Trainer which wraps a liblinear-java anomaly detection trainer using a one-class SVM.

Note the train method is synchronized on LibLinearTrainer.class due to a global RNG in liblinear-java. This is insufficient to ensure reproducibility if liblinear-java is used directly in the same JVM as Tribuo, but avoids locking on classes Tribuo does not control.

See:

 Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ.
 "LIBLINEAR: A library for Large Linear Anomaly"
 Journal of Machine Learning Research, 2008.
 

and for the original algorithm:

 Cortes C, Vapnik V.
 "Support-Vector Networks"
 Machine Learning, 1995.
 

Field Summary

Fields inherited from class org.tribuo.common.liblinear.LibLinearTrainer

cost, epsilon, libLinearParams, maxIterations, terminationCriterion, trainerType

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor and Description
LibLinearAnomalyTrainer() Creates a trainer using the default values (type:ONECLASS_SVM, cost:1, maxIterations:1000, terminationCriterion:0.1, nu:0.5).
LibLinearAnomalyTrainer(double nu) Creates a trainer using the default values (type:ONECLASS_SVM, cost:1, maxIterations:1000, terminationCriterion:0.1) and the specified nu.
LibLinearAnomalyTrainer(LinearAnomalyType trainerType, double cost, double terminationCriterion, double nu) Creates a trainer for a LibLinearAnomalyModel.
LibLinearAnomalyTrainer(LinearAnomalyType trainerType, double cost, int maxIterations, double terminationCriterion, double nu) Creates a trainer for a LibLinear model

Method Summary

Modifier and Type	Method and Description
protected LibLinearModel<Event>	createModel(ModelProvenance provenance, ImmutableFeatureMap featureIDMap, ImmutableOutputInfo<Event> outputIDInfo, List<de.bwaldvogel.liblinear.Model> models) Construct the appropriate subtype of LibLinearModel for the prediction task.
protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]>	extractData(Dataset<Event> data, ImmutableOutputInfo<Event> outputInfo, ImmutableFeatureMap featureMap) Extracts the features and Outputs in LibLinear's format.
void	postConfig() Used by the OLCUT configuration system, and should not be called by external code.
protected de.bwaldvogel.liblinear.Parameter	setupParameters(ImmutableOutputInfo<Event> labelIDMap) Constructs the parameters.
protected List<de.bwaldvogel.liblinear.Model>	trainModels(de.bwaldvogel.liblinear.Parameter curParams, int numFeatures, de.bwaldvogel.liblinear.FeatureNode[][] features, double[][] outputs) Train all the liblinear instances necessary for this dataset.

Methods inherited from class org.tribuo.common.liblinear.LibLinearTrainer

exampleToNodes, getInvocationCount, getProvenance, toString, train, train

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

LibLinearAnomalyTrainer

public LibLinearAnomalyTrainer()

Creates a trainer using the default values (type:ONECLASS_SVM, cost:1, maxIterations:1000, terminationCriterion:0.1, nu:0.5).

LibLinearAnomalyTrainer

public LibLinearAnomalyTrainer(double nu)

Creates a trainer using the default values (type:ONECLASS_SVM, cost:1, maxIterations:1000, terminationCriterion:0.1) and the specified nu.

Parameters:

nu - The nu parameter in the one-class SVM.

LibLinearAnomalyTrainer

public LibLinearAnomalyTrainer(LinearAnomalyType trainerType,
                               double cost,
                               double terminationCriterion,
                               double nu)

Creates a trainer for a LibLinearAnomalyModel. Sets maxIterations to 1000.

Parameters:

trainerType - Loss function and optimisation method combination.

cost - Cost penalty for each incorrectly classified training point.

terminationCriterion - How close does the optimisation function need to be before terminating that subproblem (usually set to 0.1).

nu - The nu parameter in the one-class SVM.

LibLinearAnomalyTrainer

public LibLinearAnomalyTrainer(LinearAnomalyType trainerType,
                               double cost,
                               int maxIterations,
                               double terminationCriterion,
                               double nu)

Creates a trainer for a LibLinear model

Parameters:

trainerType - Loss function and optimisation method combination.

cost - Cost penalty for each incorrectly classified training point.

maxIterations - The maximum number of dataset iterations.

terminationCriterion - How close does the optimisation function need to be before terminating that subproblem (usually set to 0.1).

nu - The nu parameter in the one-class SVM.

Method Detail

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

Overrides:

postConfig in class LibLinearTrainer<Event>

setupParameters

protected de.bwaldvogel.liblinear.Parameter setupParameters(ImmutableOutputInfo<Event> labelIDMap)

Description copied from class: LibLinearTrainer

Constructs the parameters. Most of the time this is a no-op, but classification overrides it to incorporate label weights if they exist.

Overrides:

setupParameters in class LibLinearTrainer<Event>

Parameters:

labelIDMap - The output info.

Returns:

The Parameters to use for training.

trainModels

protected List<de.bwaldvogel.liblinear.Model> trainModels(de.bwaldvogel.liblinear.Parameter curParams,
                                                          int numFeatures,
                                                          de.bwaldvogel.liblinear.FeatureNode[][] features,
                                                          double[][] outputs)

Description copied from class: LibLinearTrainer

Train all the liblinear instances necessary for this dataset.

Specified by:

trainModels in class LibLinearTrainer<Event>

Parameters:

curParams - The LibLinear parameters.

numFeatures - The number of features in this dataset.

features - The features themselves.

outputs - The outputs.

Returns:

A list of liblinear models.

createModel

protected LibLinearModel<Event> createModel(ModelProvenance provenance,
                                            ImmutableFeatureMap featureIDMap,
                                            ImmutableOutputInfo<Event> outputIDInfo,
                                            List<de.bwaldvogel.liblinear.Model> models)

Description copied from class: LibLinearTrainer

Construct the appropriate subtype of LibLinearModel for the prediction task.

Specified by:

createModel in class LibLinearTrainer<Event>

Parameters:

provenance - The model provenance.

featureIDMap - The feature id map.

outputIDInfo - The output id info.

models - The list of linear models.

Returns:

An implementation of LibLinearModel.

extractData

protected com.oracle.labs.mlrg.olcut.util.Pair<de.bwaldvogel.liblinear.FeatureNode[][],double[][]> extractData(Dataset<Event> data,
                                                                                                               ImmutableOutputInfo<Event> outputInfo,
                                                                                                               ImmutableFeatureMap featureMap)

Description copied from class: LibLinearTrainer

Extracts the features and Outputs in LibLinear's format.

Specified by:

extractData in class LibLinearTrainer<Event>

Parameters:

data - The input data.

outputInfo - The output info.

featureMap - The feature info.

Returns:

The features and outputs.