Updated JPMML-XGBoost dependency

See #128

pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java

@@ -23,18 +23,13 @@
 import java.io.InputStream;
 import java.util.LinkedHashMap;
 import java.util.Map;
-import java.util.function.Function;
 
 import com.google.common.io.MoreFiles;
 import ml.dmlc.xgboost4j.scala.Booster;
 import ml.dmlc.xgboost4j.scala.spark.params.GeneralParams;
 import org.apache.spark.ml.Model;
 import org.apache.spark.ml.param.shared.HasPredictionCol;
-import org.dmg.pmml.DataType;
-import org.dmg.pmml.Field;
 import org.dmg.pmml.mining.MiningModel;
-import org.jpmml.converter.ContinuousFeature;
-import org.jpmml.converter.Feature;
 import org.jpmml.converter.Schema;
 import org.jpmml.sparkml.ModelConverter;
 import org.jpmml.xgboost.HasXGBoostOptions;
@@ -47,7 +42,7 @@ private BoosterUtil(){
 	}
 
 	static
-	public <M extends Model<M> & HasPredictionCol & GeneralParams, C extends ModelConverter<M> & HasSparkMLXGBoostOptions> MiningModel encodeBooster(C converter, Booster booster, Schema schema){
+	public <M extends Model<M> & HasPredictionCol & GeneralParams, C extends ModelConverter<M>> MiningModel encodeBooster(C converter, Booster booster, Schema schema){
 		M model = converter.getModel();
 
 		Learner learner;
@@ -66,41 +61,6 @@ public <M extends Model<M> & HasPredictionCol & GeneralParams, C extends ModelCo
 			throw new RuntimeException(e);
 		}
 
-		Boolean inputFloat = (Boolean)converter.getOption(HasSparkMLXGBoostOptions.OPTION_INPUT_FLOAT, null);
-		if((Boolean.TRUE).equals(inputFloat)){
-			Function<Feature, Feature> function = new Function<Feature, Feature>(){
-
-				@Override
-				public Feature apply(Feature feature){
-
-					if(feature instanceof ContinuousFeature){
-						ContinuousFeature continuousFeature = (ContinuousFeature)feature;
-
-						DataType dataType = continuousFeature.getDataType();
-						switch(dataType){
-							case INTEGER:
-							case FLOAT:
-								break;
-							case DOUBLE:
-								{
-									Field<?> field = continuousFeature.getField();
-
-									field.setDataType(DataType.FLOAT);
-
-									return new ContinuousFeature(continuousFeature.getEncoder(), field);
-								}
-							default:
-								break;
-						}
-					}
-
-					return feature;
-				}
-			};
-
-			schema = schema.toTransformedSchema(function);
-		}
-
 		Float missing = model.getMissing();
 		if(missing.isNaN()){
 			missing = null;
@@ -113,10 +73,12 @@ public Feature apply(Feature feature){
 		options.put(HasXGBoostOptions.OPTION_PRUNE, converter.getOption(HasXGBoostOptions.OPTION_PRUNE, false));
 		options.put(HasXGBoostOptions.OPTION_NTREE_LIMIT, converter.getOption(HasXGBoostOptions.OPTION_NTREE_LIMIT, null));
 
-		Boolean numeric = (Boolean)options.get(HasXGBoostOptions.OPTION_NUMERIC);
+		Schema xgbSchema = learner.configureSchema(options, schema);
+
+		MiningModel miningModel = learner.encodeModel(options, xgbSchema);
 
-		Schema xgbSchema = learner.toXGBoostSchema(numeric, schema);
+		miningModel = learner.configureModel(options, miningModel);
 
-		return learner.encodeMiningModel(options, xgbSchema);
+		return miningModel;
 	}
 }

pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostClassificationModelConverter.java

@@ -26,7 +26,7 @@
 import org.jpmml.converter.mining.MiningModelUtil;
 import org.jpmml.sparkml.ProbabilisticClassificationModelConverter;
 
-public class XGBoostClassificationModelConverter extends ProbabilisticClassificationModelConverter<XGBoostClassificationModel> implements HasSparkMLXGBoostOptions {
+public class XGBoostClassificationModelConverter extends ProbabilisticClassificationModelConverter<XGBoostClassificationModel> {
 
 	public XGBoostClassificationModelConverter(XGBoostClassificationModel model){
 		super(model);

pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/XGBoostRegressionModelConverter.java

@@ -38,7 +38,7 @@
 import org.jpmml.sparkml.SparkMLEncoder;
 import org.jpmml.sparkml.model.HasPredictionModelOptions;
 
-public class XGBoostRegressionModelConverter extends PredictionModelConverter<XGBoostRegressionModel> implements HasSparkMLXGBoostOptions {
+public class XGBoostRegressionModelConverter extends PredictionModelConverter<XGBoostRegressionModel> {
 
 	public XGBoostRegressionModelConverter(XGBoostRegressionModel model){
 		super(model);

pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java

@@ -51,7 +51,6 @@
 import org.jpmml.sparkml.PipelineModelUtil;
 import org.jpmml.sparkml.testing.SparkMLEncoderBatch;
 import org.jpmml.sparkml.testing.SparkMLEncoderBatchTest;
-import org.jpmml.sparkml.xgboost.HasSparkMLXGBoostOptions;
 import org.jpmml.xgboost.HasXGBoostOptions;
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
@@ -78,9 +77,8 @@ public XGBoostTest getArchiveBatchTest(){
 			public List<Map<String, Object>> getOptionsMatrix(){
 				Map<String, Object> options = new LinkedHashMap<>();
 
-				options.put(HasSparkMLXGBoostOptions.OPTION_INPUT_FLOAT, new Boolean[]{false, true});
-
 				options.put(HasXGBoostOptions.OPTION_COMPACT, new Boolean[]{false, true});
+				options.put(HasXGBoostOptions.OPTION_INPUT_FLOAT, new Boolean[]{false, true});
 				options.put(HasXGBoostOptions.OPTION_PRUNE, false);
 
 				return OptionsUtil.generateOptionsMatrix(options);

pom.xml

@@ -113,7 +113,7 @@
 			<dependency>
 				<groupId>org.jpmml</groupId>
 				<artifactId>pmml-xgboost</artifactId>
-				<version>1.8.3</version>
+				<version>1.8.4</version>
 			</dependency>
 
 			<dependency>