public class IndependentMultiLabelTrainer extends Object implements Trainer<MultiLabel>
Model
s, each of which predicts a single Label
.
Then wraps it up in an IndependentMultiLabelModel
to provide a MultiLabel
prediction.
It trains each model sequentially, and could be optimised to train in parallel.
DEFAULT_SEED
Constructor and Description |
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IndependentMultiLabelTrainer(Trainer<Label> innerTrainer) |
Modifier and Type | Method and Description |
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int |
getInvocationCount()
The number of times this trainer instance has had it's train method invoked.
|
TrainerProvenance |
getProvenance() |
String |
toString() |
Model<MultiLabel> |
train(Dataset<MultiLabel> examples,
Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
Trains a predictive model using the examples in the given data set.
|
public Model<MultiLabel> train(Dataset<MultiLabel> examples, Map<String,com.oracle.labs.mlrg.olcut.provenance.Provenance> runProvenance)
Trainer
train
in interface Trainer<MultiLabel>
examples
- the data set containing the examples.runProvenance
- Training run specific provenance (e.g., fold number).public int getInvocationCount()
Trainer
This is used to determine how many times the trainer's RNG has been accessed to ensure replicability in the random number stream.
getInvocationCount
in interface Trainer<MultiLabel>
public TrainerProvenance getProvenance()
getProvenance
in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<TrainerProvenance>
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