Package hex.genmodel.easy

The easy prediction API for generated POJO and MOJO models.

See: Description

Interface Summary

Interface	Description
CategoricalEncoder

Class Summary

Class	Description
BinaryColumnMapper
BinaryDomainMapConstructor
BinaryEncoder
EasyPredictModelWrapper	An easy-to-use prediction wrapper for generated models.
EasyPredictModelWrapper.Config	Configuration builder for instantiating a Wrapper.
EasyPredictModelWrapper.ErrorConsumer	Observer interface with methods corresponding to errors during the prediction.
EigenEncoder
EigenEncoderColumnMapper
EigenEncoderDomainMapConstructor
EnumEncoder
EnumEncoderColumnMapper
EnumEncoderDomainMapConstructor
EnumLimitedEncoder
EnumLimitedEncoderColumnMapper
EnumLimitedEncoderDomainMapConstructor
LabelEncoder
LabelEncoderDomainMapConstructor
OneHotEncoder
OneHotEncoderColumnMapper
OneHotEncoderDomainMapConstructor
RowData	Column name to column value mapping for a new row (aka data point, observation, sample) to predict.
RowToRawDataConverter	This class is intended to transform a RowData instance - for which we want to get prediction to - into a raw array

Package hex.genmodel.easy Description

The easy prediction API for generated POJO and MOJO models. Use as follows:

1. Instantiate an EasyPredictModelWrapper
2. Create a new row of data
3. Call one of the predict methods

Here is an example:

   
   // Step 1.
   modelClassName = "your_pojo_model_downloaded_from_h2o";
   GenModel rawModel;
   rawModel = (GenModel) Class.forName(modelClassName).newInstance();
   EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);
   //
   // By default, unknown categorical levels throw PredictUnknownCategoricalLevelException.
   // Optionally configure the wrapper to treat unknown categorical levels as N/A instead:
   //
   //     EasyPredictModelWrapper model = new EasyPredictModelWrapper(
   //                                         new EasyPredictModelWrapper.Config()
   //                                             .setModel(rawModel)
   //                                             .setConvertUnknownCategoricalLevelsToNa(true));

   // Step 2.
   RowData row = new RowData();
   row.put(new String("CategoricalColumnName"), new String("LevelName"));
   row.put(new String("NumericColumnName1"), new String("42.0"));
   row.put(new String("NumericColumnName2"), Double.valueOf(42.0));

   // Step 3.
   BinomialModelPrediction p = model.predictBinomial(row);