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FieldDescriptionDeprecated when regression was made multidimensional by default. Use
RegressionFactory.UNKNOWN_REGRESSOR
instead.
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MethodDescriptionAs it's replaced with
CRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap)
which is more flexible.As it's replaced withCRFModel.convertToVector(org.tribuo.sequence.SequenceExample<T>, org.tribuo.ImmutableFeatureMap)
which is more flexible.In 4.1, to migrate to the new nameLabelObjective.lossAndGradient(java.lang.Integer, org.tribuo.math.la.SGDVector)
.org.tribuo.classification.sgd.Util.shuffleInPlace(SparseVector[], int[], double[], SplittableRandom) Deprecated to unify the names across LibLinear, LibSVM and XGBoost.As the URL argument must always be valid. To wrap an in-memory booster useXGBoostExternalModel.createXGBoostModel(OutputFactory, Map, Map, XGBoostOutputConverter, Booster, Map)
.useResponseProcessor.getFieldNames()
and support multiple values instead. Gets the field name this ResponseProcessor uses.useResponseProcessor.process(List)
and support multiple values instead. Returns Optional.empty() if it failed to process out a response.Response processors should be immutable; downstream objects assume that they are Set the field name this ResponseProcessor uses.In a future release this API will change, in the meantime this is the correct way to get a row processor with clean state.When using regexMappingProcessors, RowProcessor is stateful in a way that can sometimes make it fail the second time it is used. Concretely:
RowProcessor rp; Dataset ds1 = new MutableDataset(new CSVDataSource(csvfile1, rp)); Dataset ds2 = new MutableDataset(new CSVDataSource(csvfile2, rp)); // this may fail due to state in rp
This method returns a RowProcessor with clean state and the same configuration as this row processor.In 4.1 to move to the new name, lossAndGradient.in 4.1 as it's unnecessary.
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ConstructorDescription
CSVDataSource
. This provenance is kept so older models can still load correctly.