Use original builders instead

extensions/src/main/scala/hex/MetricsCalculationTypeExtensions.java

@@ -6,11 +6,11 @@
 
 public class MetricsCalculationTypeExtensions implements TypeMapExtension {
   public static final String[] MODEL_BUILDER_CLASSES = {
-    ModelMetrics.IndependentMetricBuilder.class.getName(),
-    ModelMetricsSupervised.IndependentMetricBuilderSupervised.class.getName(),
-    ModelMetricsBinomial.IndependentMetricBuilderBinomial.class.getName(),
+    ModelMetrics.MetricBuilder.class.getName(),
+    ModelMetricsSupervised.MetricBuilderSupervised.class.getName(),
+    ModelMetricsBinomial.MetricBuilderBinomial.class.getName(),
     AUC2.AUCBuilder.class.getName(),
-    ModelMetricsRegression.IndependentMetricBuilderRegression.class.getName(),
+    ModelMetricsRegression.MetricBuilderRegression.class.getName(),
     Distribution.class.getName(),
     GaussianDistribution.class.getName(),
     BernoulliDistribution.class.getName(),
@@ -33,7 +33,7 @@ public class MetricsCalculationTypeExtensions implements TypeMapExtension {
     OlogitFunction.class.getName(),
     OloglogFunction.class.getName(),
     OprobitFunction.class.getName(),
-    ModelMetricsMultinomial.IndependentMetricBuilderMultinomial.class.getName()
+    ModelMetricsMultinomial.MetricBuilderMultinomial.class.getName()
   };
 
   public static final String[] SCHEMA_CLASSES = {

scoring/src/main/scala/ai/h2o/sparkling/ml/metrics/H2OBinomialMetrics.scala

@@ -17,8 +17,7 @@
 
 package ai.h2o.sparkling.ml.metrics
 
-import hex.ModelMetricsBinomial.IndependentMetricBuilderBinomial
-import hex.genmodel.utils.DistributionFamily
+import hex.ModelMetricsBinomial.MetricBuilderBinomial
 import org.apache.spark.{ExposeUtils, ml, mllib}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.types._
@@ -59,7 +58,7 @@ object H2OBinomialMetrics extends MetricCalculation {
       weightColOption: Option[String] = None,
       offsetColOption: Option[String] = None): H2OBinomialMetrics = {
     validateDataFrameForMetricCalculation(dataFrame, predictionCol, labelCol, offsetColOption, weightColOption)
-    val getMetricBuilder = () => new IndependentMetricBuilderBinomial(domain, DistributionFamily.bernoulli)
+    val getMetricBuilder = () => new MetricBuilderBinomial(domain)
     val castedLabelDF = dataFrame.withColumn(labelCol, col(labelCol) cast StringType)
 
     val gson =

scoring/src/main/scala/ai/h2o/sparkling/ml/metrics/H2OMultinomialMetrics.scala

@@ -17,7 +17,7 @@
 
 package ai.h2o.sparkling.ml.metrics
 
-import hex.ModelMetricsMultinomial.IndependentMetricBuilderMultinomial
+import hex.ModelMetricsMultinomial.MetricBuilderMultinomial
 import hex.MultinomialAucType
 import org.apache.spark.{ExposeUtils, ml, mllib}
 import org.apache.spark.ml.util.Identifiable
@@ -67,7 +67,7 @@ object H2OMultinomialMetrics extends MetricCalculation {
     val aucTypeEnum = MultinomialAucType.valueOf(aucType)
     val nclasses = domain.length
     val getMetricBuilder =
-      () => new IndependentMetricBuilderMultinomial(nclasses, domain, aucTypeEnum, null)
+      () => new MetricBuilderMultinomial(nclasses, domain, aucTypeEnum)
     val castedLabelDF = dataFrame.withColumn(labelCol, col(labelCol) cast StringType)
 
     val gson =

scoring/src/main/scala/ai/h2o/sparkling/ml/metrics/H2ORegressionMetrics.scala

@@ -18,7 +18,7 @@
 package ai.h2o.sparkling.ml.metrics
 
 import hex.DistributionFactory
-import hex.ModelMetricsRegression.IndependentMetricBuilderRegression
+import hex.ModelMetricsRegression.MetricBuilderRegression
 import hex.genmodel.utils.DistributionFamily
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.functions.col
@@ -54,7 +54,7 @@ object H2ORegressionMetrics extends MetricCalculation {
       offsetColOption: Option[String] = None): H2ORegressionMetrics = {
     validateDataFrameForMetricCalculation(dataFrame, predictionCol, labelCol, offsetColOption, weightColOption)
     val getMetricBuilder =
-      () => new IndependentMetricBuilderRegression(DistributionFactory.getDistribution(DistributionFamily.AUTO))
+      () => new MetricBuilderRegression(DistributionFactory.getDistribution(DistributionFamily.AUTO))
     val castedLabelDF = dataFrame.withColumn(labelCol, col(labelCol) cast DoubleType)
     val gson =
       getMetricGson(getMetricBuilder, castedLabelDF, predictionCol, labelCol, offsetColOption, weightColOption, null)

scoring/src/main/scala/ai/h2o/sparkling/ml/metrics/MetricCalculation.scala

@@ -19,7 +19,7 @@ package ai.h2o.sparkling.ml.metrics
 
 import com.google.gson.{GsonBuilder, JsonObject}
 import hex._
-import hex.ModelMetrics.IndependentMetricBuilder
+import hex.ModelMetrics.MetricBuilder
 import org.apache.spark.sql.{DataFrame, Row}
 import water.api.{Schema, SchemaServer}
 import water.api.schemas3._
@@ -91,7 +91,7 @@ trait MetricCalculation {
   protected def getActualValue(dataType: DataType, domain: Array[String], row: Row): Double
 
   protected def getMetricGson(
-      createMetricBuilder: () => IndependentMetricBuilder[_],
+      createMetricBuilder: () => MetricBuilder[_],
       dataFrame: DataFrame,
       predictionCol: String,
       labelCol: String,
@@ -108,22 +108,22 @@ trait MetricCalculation {
     val predictionType = flatDF.schema.fields(0).dataType
     val actualType = flatDF.schema.fields(1).dataType
     val filledMetricsBuilder = flatDF.rdd
-      .mapPartitions[IndependentMetricBuilder[_]] { rows =>
+      .mapPartitions[MetricBuilder[_]] { rows =>
         val metricBuilder = createMetricBuilder()
         while (rows.hasNext) {
           val row = rows.next()
           val prediction = getPredictionValues(predictionType, domain, row)
           val actualValue: Double = getActualValue(actualType, domain, row)
           val weight = row.getDouble(2)
           val offset = row.getDouble(3)
-          metricBuilder.perRow(prediction, Array(actualValue), weight, offset)
+          metricBuilder.perRow(prediction, Array(actualValue.toFloat), weight, offset, null)
         }
         Iterator.single(metricBuilder)
       }
       .reduce((f, s) => { f.reduce(s); f })
 
     filledMetricsBuilder.postGlobal()
-    val metrics = filledMetricsBuilder.makeModelMetrics()
+    val metrics = filledMetricsBuilder.makeModelMetrics(null, null, null, null)
     val schema = metricsToSchema(metrics)
     val json = schema.toJsonString
     new GsonBuilder().create().fromJson(json, classOf[JsonObject])