Update api

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

@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.h2o.sparkling.ml.metrics
+
+import hex.ModelMetricsBinomial.IndependentMetricBuilderBinomial
+import hex.genmodel.utils.DistributionFamily
+import org.apache.spark.sql.DataFrame
+
+object H2OBinomialMetrics extends MetricCalculation {
+
+  def calculate(
+      dataFrame: DataFrame,
+      domain: Array[String],
+      predictionProbabilitiesCol: String = "detailed_prediction.probabilities",
+      labelCol: String = "label",
+      weightColOption: Option[String] = None,
+      offsetColOption: Option[String] = None,
+      distributionFamily: String = "AUTO"): H2OBinomialMetrics = {
+    val domainFamilyEnum = DistributionFamily.valueOf(distributionFamily)
+    val getMetricBuilder = () => new IndependentMetricBuilderBinomial[_](domain, domainFamilyEnum)
+
+    val gson = getMetricGson(
+      getMetricBuilder,
+      dataFrame,
+      predictionProbabilitiesCol,
+      labelCol,
+      offsetColOption,
+      weightColOption,
+      domain)
+    val result = new H2OBinomialMetrics()
+    result.setMetrics(gson, "H2OBinomialMetrics.calculate")
+    result
+  }
+
+  def calculate(
+      dataFrame: DataFrame,
+      domain: Array[String],
+      predictionProbabilitiesCol: String,
+      labelCol: String,
+      weightCol: String,
+      offsetCol: String,
+      distributionFamily: String): Unit = {
+    calculate(
+      dataFrame,
+      domain,
+      predictionProbabilitiesCol,
+      labelCol,
+      Option(weightCol),
+      Option(offsetCol),
+      distributionFamily)
+  }
+}

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

@@ -0,0 +1,77 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.h2o.sparkling.ml.metrics
+
+import ai.h2o.sparkling.ml.metrics.H2OBinomialMetrics.getMetricGson
+import hex.ModelMetricsMultinomial.IndependentMetricBuilderMultinomial
+import hex.MultinomialAucType
+import org.apache.spark.sql.DataFrame
+
+object H2OMultinomialMetrics {
+  def calculate(
+      dataFrame: DataFrame,
+      domain: Array[String],
+      predictionProbabilitiesCol: String = "detailed_prediction.probabilities",
+      labelCol: String = "label",
+      weightColOption: Option[String] = None,
+      offsetColOption: Option[String] = None,
+      priorDistributionOption: Option[Array[Double]] = None,
+      aucType: String = "AUTO"): H2OMultinomialMetrics = {
+
+    val aucTypeEnum = MultinomialAucType.valueOf(aucType)
+    val nclasses = domain.length
+    val priorDistribution = priorDistributionOption match {
+      case Some(x) => x
+      case None => null
+    }
+    val getMetricBuilder =
+      () => new IndependentMetricBuilderMultinomial[_](nclasses, domain, aucTypeEnum, priorDistribution)
+
+    val gson = getMetricGson(
+      getMetricBuilder,
+      dataFrame,
+      predictionProbabilitiesCol,
+      labelCol,
+      offsetColOption,
+      weightColOption,
+      domain)
+    val result = new H2OMultinomialMetrics()
+    result.setMetrics(gson, "H2OMultinomialMetrics.calculate")
+    result
+  }
+
+  def calculate(
+      dataFrame: DataFrame,
+      domain: Array[String],
+      predictionProbabilitiesCol: String,
+      labelCol: String,
+      weightCol: String,
+      offsetCol: String,
+      priorDistribution: Array[Double],
+      aucType: String): H2OMultinomialMetrics = {
+    calculate(
+      dataFrame,
+      domain,
+      predictionProbabilitiesCol,
+      labelCol,
+      Option(weightCol),
+      Option(offsetCol),
+      Option(priorDistribution),
+      aucType)
+  }
+}

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

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+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package ai.h2o.sparkling.ml.metrics
+
+import ai.h2o.sparkling.ml.metrics.H2OBinomialMetrics.getMetricGson
+import hex.DistributionFactory
+import hex.ModelMetricsRegression.IndependentMetricBuilderRegression
+import hex.genmodel.utils.DistributionFamily
+import org.apache.spark.sql.DataFrame
+
+object H2ORegressionMetrics {
+
+  def calculate(
+      dataFrame: DataFrame,
+      predictionCol: String = "prediction",
+      labelCol: String = "label",
+      weightColOption: Option[String] = None,
+      offsetColOption: Option[String] = None,
+      distributionFamily: String = "AUTO"): H2ORegressionMetrics = {
+    val domainFamilyEnum = DistributionFamily.valueOf(distributionFamily)
+    val distribution= DistributionFactory.getDistribution(domainFamilyEnum)
+    val getMetricBuilder = () => new IndependentMetricBuilderRegression[_](distribution)
+
+    val gson = getMetricGson(
+      getMetricBuilder,
+      dataFrame,
+      predictionCol,
+      labelCol,
+      offsetColOption,
+      weightColOption,
+      null)
+    val result = new H2ORegressionMetrics()
+    result.setMetrics(gson, "H2ORegressionMetrics.calculate")
+    result
+  }
+
+  def calculate(
+      dataFrame: DataFrame,
+      predictionCol: String,
+      labelCol: String,
+      weightCol: String,
+      offsetCol: String,
+      distributionFamily: String): H2ORegressionMetrics = {
+    calculate(
+      dataFrame,
+      predictionCol,
+      labelCol,
+      Option(weightCol),
+      Option(offsetCol),
+      distributionFamily)
+  }
+}