Class LinearSGDTrainer
java.lang.Object
org.tribuo.regression.sgd.linear.LinearSGDTrainer
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable
,com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
,Trainer<Regressor>
,WeightedExamples
A trainer for a linear regression model which uses SGD.
Independently trains each output dimension, unless they are tied together in the
optimiser.
See:
Bottou L. "Large-Scale Machine Learning with Stochastic Gradient Descent" Proceedings of COMPSTAT, 2010.
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Field Summary
Fields inherited from interface org.tribuo.Trainer
DEFAULT_SEED
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Constructor Summary
ConstructorsConstructorDescriptionLinearSGDTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed) Constructs an SGD trainer for a linear model.LinearSGDTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed) Sets the minibatch size to 1.LinearSGDTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, long seed) Sets the minibatch size to 1 and the logging interval to 1000. -
Method Summary
Modifier and TypeMethodDescriptionint
The number of times this trainer instance has had it's train method invoked.void
void
setShuffle
(boolean shuffle) Turn on or off shuffling of examples.toString()
train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a predictive model using the examples in the given data set.
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Constructor Details
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LinearSGDTrainer
public LinearSGDTrainer(RegressionObjective 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.
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LinearSGDTrainer
public LinearSGDTrainer(RegressionObjective 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.
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LinearSGDTrainer
public LinearSGDTrainer(RegressionObjective 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.
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Method Details
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postConfig
- Specified by:
postConfig
in interfacecom.oracle.labs.mlrg.olcut.config.Configurable
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setShuffle
Turn on or off shuffling of examples.This isn't exposed in the constructor as it defaults to on. This method should be used for debugging.
- Parameters:
shuffle
- If true shuffle the examples, if false leave them in their current order.
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train
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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 interfaceTrainer<Regressor>
- Returns:
- The number of train invocations.
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toString
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getProvenance
- Specified by:
getProvenance
in interfacecom.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
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