org.tribuo.classification.sgd.linear

Class LinearSGDTrainer

java.lang.Object

org.tribuo.classification.sgd.linear.LinearSGDTrainer

All Implemented Interfaces:

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

Direct Known Subclasses:

LogisticRegressionTrainer

public class LinearSGDTrainer
extends Object
implements Trainer<Label>, WeightedExamples

A trainer for a linear model which uses SGD.

See:

 Bottou L.
 "Large-Scale Machine Learning with Stochastic Gradient Descent"
 Proceedings of COMPSTAT, 2010.
 

Field Summary

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor and Description
LinearSGDTrainer(LabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed) Constructs an SGD trainer for a linear model.
LinearSGDTrainer(LabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed) Sets the minibatch size to 1.
LinearSGDTrainer(LabelObjective objective, StochasticGradientOptimiser optimiser, int epochs, long seed) Sets the minibatch size to 1 and the logging interval to 1000.

Method Summary

Modifier and Type	Method and Description
int	getInvocationCount() The number of times this trainer instance has had it's train method invoked.
TrainerProvenance	getProvenance()
void	postConfig()
void	setShuffle(boolean shuffle) Turn on or off shuffling of examples.
String	toString()
Model<Label>	train(Dataset<Label> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a predictive model using the examples in the given data set.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.tribuo.Trainer

train

Constructor Detail

LinearSGDTrainer

public LinearSGDTrainer(LabelObjective objective,
                        StochasticGradientOptimiser optimiser,
                        int epochs,
                        int loggingInterval,
                        int minibatchSize,
                        long seed)

Constructs an SGD trainer for a linear model.

Parameters:

objective - The objective function to optimise.

optimiser - The gradient optimiser to use.

epochs - The number of epochs (complete passes through the training data).

loggingInterval - Log the loss after this many iterations. If -1 don't log anything.

minibatchSize - The size of any minibatches.

seed - A seed for the random number generator, used to shuffle the examples before each epoch.

LinearSGDTrainer

public LinearSGDTrainer(LabelObjective objective,
                        StochasticGradientOptimiser optimiser,
                        int epochs,
                        int loggingInterval,
                        long seed)

Sets the minibatch size to 1.

Parameters:

objective - The objective function to optimise.

optimiser - The gradient optimiser to use.

epochs - The number of epochs (complete passes through the training data).

loggingInterval - Log the loss after this many iterations. If -1 don't log anything.

seed - A seed for the random number generator, used to shuffle the examples before each epoch.

LinearSGDTrainer

public LinearSGDTrainer(LabelObjective objective,
                        StochasticGradientOptimiser optimiser,
                        int epochs,
                        long seed)

Sets the minibatch size to 1 and the logging interval to 1000.

Parameters:

objective - The objective function to optimise.

optimiser - The gradient optimiser to use.

epochs - The number of epochs (complete passes through the training data).

seed - A seed for the random number generator, used to shuffle the examples before each epoch.

Method Detail

postConfig

public void postConfig()

Specified by:

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

setShuffle

public void setShuffle(boolean shuffle)

Turn on or off shuffling of examples.

This isn't exposed in the constructor as it defaults to on. This method should only be used for debugging.

Parameters:

shuffle - If true shuffle the examples, if false leave them in their current order.

train

public Model<Label> train(Dataset<Label> examples,
                          Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)

Description copied from interface: Trainer

Trains a predictive model using the examples in the given data set.

Specified by:

train in interface Trainer<Label>

Parameters:

examples - the data set containing the examples.

runProvenance - Training run specific provenance (e.g., fold number).

Returns:

a predictive model that can be used to generate predictions for new examples.

getInvocationCount

public int getInvocationCount()

Description copied from interface: Trainer

The number of times this trainer instance has had it's train method invoked.

This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.

Specified by:

getInvocationCount in interface Trainer<Label>

Returns:

The number of train invocations.

toString

public String toString()

Overrides:

toString in class Object

getProvenance

public TrainerProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>