Class ElasticNetCDTrainer
java.lang.Object
org.tribuo.regression.slm.ElasticNetCDTrainer
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable
,com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
,SparseTrainer<Regressor>
,Trainer<Regressor>
An ElasticNet trainer that uses co-ordinate descent. Modelled after scikit-learn's sparse matrix implementation.
Each output dimension is trained independently.
See:
Friedman J, Hastie T, Tibshirani R. "Regularization Paths for Generalized Linear Models via Coordinate Descent" Journal of Statistical Software, 2010
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Field Summary
Fields inherited from interface org.tribuo.Trainer
DEFAULT_SEED
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Constructor Summary
ConstructorsConstructorDescriptionElasticNetCDTrainer
(double alpha, double l1Ratio) ElasticNetCDTrainer
(double alpha, double l1Ratio, double tolerance, int maxIterations, boolean randomise, long seed) ElasticNetCDTrainer
(double alpha, double l1Ratio, long seed) -
Method Summary
Modifier and TypeMethodDescriptionint
The number of times this trainer instance has had it's train method invoked.void
Used by the OLCUT configuration system, and should not be called by external code.toString()
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.Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
Methods inherited from interface org.tribuo.SparseTrainer
train
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Constructor Details
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ElasticNetCDTrainer
public ElasticNetCDTrainer(double alpha, double l1Ratio) -
ElasticNetCDTrainer
public ElasticNetCDTrainer(double alpha, double l1Ratio, long seed) -
ElasticNetCDTrainer
public ElasticNetCDTrainer(double alpha, double l1Ratio, double tolerance, int maxIterations, boolean randomise, long seed)
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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
public SparseModel<Regressor> 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<Regressor>
- Specified by:
train
in interfaceTrainer<Regressor>
- 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<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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