Class AdaBoostTrainer
java.lang.Object
org.tribuo.classification.ensemble.AdaBoostTrainer
- All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.config.Configurable
,com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
,Trainer<Label>
Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
Based on this paper.
If the trainer implements WeightedExamples
then it performs boosting by weighting,
otherwise it uses a weighted bootstrap sample.
See:
J. Zhu, S. Rosset, H. Zou, T. Hastie. "Multi-class Adaboost" 2006.
-
Field Summary
Modifier and TypeFieldDescriptionprotected int
protected SplittableRandom
protected long
protected int
Fields inherited from interface org.tribuo.Trainer
DEFAULT_SEED, INCREMENT_INVOCATION_COUNT
-
Constructor Summary
ConstructorDescriptionAdaBoostTrainer
(Trainer<Label> trainer, int numMembers) Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.AdaBoostTrainer
(Trainer<Label> trainer, int numMembers, long seed) Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied 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.void
setInvocationCount
(int invocationCount) Set the internal state of the trainer to the provided number of invocations of the train method.toString()
train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implementsWeightedExamples
then do boosting by weighting, otherwise do boosting by sampling.train
(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Trains a predictive model using the examples in the given data set.
-
Field Details
-
innerTrainer
-
numMembers
@Config(mandatory=true, description="The number of ensemble members to train.") protected int numMembers -
seed
@Config(mandatory=true, description="The seed for the RNG.") protected long seed -
rng
-
trainInvocationCounter
protected int trainInvocationCounter
-
-
Constructor Details
-
AdaBoostTrainer
Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds. Uses the default seed.- Parameters:
trainer
- The weak learner trainer.numMembers
- The maximum number of boosting rounds.
-
AdaBoostTrainer
Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed.- Parameters:
trainer
- The weak learner trainer.numMembers
- The maximum number of boosting rounds.seed
- The RNG seed.
-
-
Method Details
-
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
-
toString
-
train
public Model<Label> train(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance) If the trainer implementsWeightedExamples
then do boosting by weighting, otherwise do boosting by sampling.- Specified by:
train
in interfaceTrainer<Label>
- Parameters:
examples
- the data set containing the examples.runProvenance
- Training run specific provenance (e.g., fold number).- Returns:
- A
WeightedEnsembleModel
.
-
train
public Model<Label> train(Dataset<Label> examples, Map<String, com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance, int invocationCount) Description copied from interface:Trainer
Trains a predictive model using the examples in the given data set.- Specified by:
train
in interfaceTrainer<Label>
- Parameters:
examples
- the data set containing the examples.runProvenance
- Training run specific provenance (e.g., fold number).invocationCount
- The invocation counter that the trainer should be set to before training, which in most cases alters the state of the RNG inside this trainer. If the value is set toTrainer.INCREMENT_INVOCATION_COUNT
then the invocation count is not changed.- 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 interfaceTrainer<Label>
- Returns:
- The number of train invocations.
-
setInvocationCount
public void setInvocationCount(int invocationCount) Description copied from interface:Trainer
Set the internal state of the trainer to the provided number of invocations of the train method.This is used when reproducing a Tribuo-trained model by setting the state of the RNG to what it was at when Tribuo trained the original model by simulating invocations of the train method. This method should ALWAYS be overridden, and the default method is purely for compatibility.
In a future major release this default implementation will be removed.
- Specified by:
setInvocationCount
in interfaceTrainer<Label>
- Parameters:
invocationCount
- the number of invocations of the train method to simulate
-
getProvenance
- Specified by:
getProvenance
in interfacecom.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
-