Class TransformTrainer<T extends Output<T>>

java.lang.Object

org.tribuo.transform.TransformTrainer<T>

All Implemented Interfaces:

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

public final class TransformTrainer<T extends Output<T>>
extends Object
implements Trainer<T>

A Trainer which encapsulates another trainer plus a TransformationMap object to apply to each Dataset before training each Model.

Transformations only operate on observed values. To operate on implicit zeros then first call MutableDataset.densify() on the datasets.

Field Summary

Fields inherited from interface org.tribuo.Trainer

DEFAULT_SEED

Constructor Summary

Constructors

Constructor	Description
TransformTrainer(Trainer<T> innerTrainer, TransformationMap transformations)	Creates a trainer which transforms the data before training, and stores the transformers along with the trained model in a TransformedModel.
TransformTrainer(Trainer<T> innerTrainer, TransformationMap transformations, boolean densify)	Creates a trainer which transforms the data before training, and stores the transformers along with the trained model in a TransformedModel.

Method Summary

Modifier and Type	Method	Description
int	getInvocationCount()	The number of times this trainer instance has had it's train method invoked.
TrainerProvenance	getProvenance()
TransformedModel<T>	train(Dataset<T> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)	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, toString, wait, wait, wait

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Configurable

postConfig

Methods inherited from interface org.tribuo.Trainer

train

Constructor Details

TransformTrainer

public TransformTrainer(Trainer<T> innerTrainer,
 TransformationMap transformations)

Creates a trainer which transforms the data before training, and stores the transformers along with the trained model in a TransformedModel.

This constructor makes a trainer which keeps the data sparse.

Parameters:

innerTrainer - The trainer to use.

transformations - The transformations to apply to the data first.

TransformTrainer

public TransformTrainer(Trainer<T> innerTrainer,
 TransformationMap transformations,
 boolean densify)

Creates a trainer which transforms the data before training, and stores the transformers along with the trained model in a TransformedModel.

Parameters:

innerTrainer - The trainer to use.

transformations - The transformations to apply to the data first.

densify - Densify the dataset (and any predict time data) before training/prediction.

Method Details

train

public TransformedModel<T> train(Dataset<T> examples,
 Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> instanceProvenance)

Description copied from interface: Trainer

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

Specified by:

train in interface Trainer<T extends Output<T>>

Parameters:

examples - the data set containing the examples.

instanceProvenance - 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<T extends Output<T>>

Returns:

The number of train invocations.

getProvenance

public TrainerProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<T extends Output<T>>