Class SkeletalIndependentRegressionSparseTrainer<T>
java.lang.Object
org.tribuo.regression.impl.SkeletalIndependentRegressionSparseTrainer<T>
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable
,com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
,SparseTrainer<Regressor>
,Trainer<Regressor>
public abstract class SkeletalIndependentRegressionSparseTrainer<T>
extends Object
implements SparseTrainer<Regressor>
Base class for training n independent sparse models, one per dimension. Generates the SparseVectors
once to reduce allocation.
Then wraps them in an SkeletalIndependentRegressionSparseModel
to provide a Regressor
prediction.
It trains each model sequentially, and could be optimised to train in parallel.
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Field Summary
Fields inherited from interface org.tribuo.Trainer
DEFAULT_SEED
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionprotected abstract SkeletalIndependentRegressionSparseModel
createModel
(Map<String, T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass ofSkeletalIndependentRegressionModel
for this trainer.int
The number of times this trainer instance has had it's train method invoked.protected abstract String
Returns the class name of the model that this class produces.void
Used by the OLCUT configuration system, and should not be called by external code.Trains a sparse predictive model using the examples in the given data set.train
(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Trains a sparse predictive model using the examples in the given data set.protected abstract T
trainDimension
(double[] outputs, SparseVector[] features, float[] weights, SplittableRandom rng) Trains a single dimension of the possibly multiple dimensions.protected abstract boolean
useBias()
Returns true if the SparseVector should be constructed with a bias feature.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.provenance.Provenancable
getProvenance
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Constructor Details
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SkeletalIndependentRegressionSparseTrainer
protected SkeletalIndependentRegressionSparseTrainer()for olcut.
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Method Details
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postConfig
public void postConfig()Used by the OLCUT configuration system, and should not be called by external code.- Specified by:
postConfig
in interfacecom.oracle.labs.mlrg.olcut.config.Configurable
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train
Description copied from interface:SparseTrainer
Trains a sparse predictive model using the examples in the given data set. -
train
public SkeletalIndependentRegressionSparseModel train(Dataset<Regressor> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) Description copied from interface:SparseTrainer
Trains a sparse predictive model using the examples in the given data set.- Specified by:
train
in interfaceSparseTrainer<T>
- Specified by:
train
in interfaceTrainer<T>
- 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.
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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 interfaceTrainer<T>
- Returns:
- The number of train invocations.
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createModel
protected abstract SkeletalIndependentRegressionSparseModel createModel(Map<String, T> models, ModelProvenance provenance, ImmutableFeatureMap featureMap, ImmutableOutputInfo<Regressor> outputInfo) Constructs the appropriate subclass ofSkeletalIndependentRegressionModel
for this trainer.- Parameters:
models
- The models to use.provenance
- The model provenancefeatureMap
- The feature map.outputInfo
- The regression info.- Returns:
- A subclass of IndependentRegressionModel.
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trainDimension
protected abstract T trainDimension(double[] outputs, SparseVector[] features, float[] weights, SplittableRandom rng) Trains a single dimension of the possibly multiple dimensions.- Parameters:
outputs
- The regression targets for this dimension.features
- The features.weights
- The example weights.rng
- The RNG to use.- Returns:
- An object representing the model.
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useBias
protected abstract boolean useBias()Returns true if the SparseVector should be constructed with a bias feature.- Returns:
- True if the trainer needs a bias.
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getModelClassName
Returns the class name of the model that this class produces.- Returns:
- The class name of the model.
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