[GH-5725] Expose UUID for MOJO2

ml/src/test/scala/ai/h2o/sparkling/ml/models/H2OMOJOPipelineModelTestSuite.scala

@@ -113,6 +113,10 @@ class H2OMOJOPipelineModelTestSuite extends FunSuite with SparkTestContext with
     }
   }
 
+  test("get UUID") {
+    prostateMojoPipeline.getUuid() shouldEqual "b448504b-1877-435f-9b09-d911a3388bd7"
+  }
+
   test("Testing dataset is missing one of the feature columns") {
     val schema = prostateTestData.drop("AGE").schema
     val rdd = sc.parallelize(Seq(Row("1", "0", "1", "2", "1", "1.4", "0", "6")))

py-scoring/src/ai/h2o/sparkling/ml/models/H2OMOJOPipelineModel.py

@@ -48,3 +48,6 @@ def getWithPredictionInterval(self):
 
     def getScoringBulkSize(self):
         return self._java_obj.getScoringBulkSize()
+
+    def getUuid(self):
+        return self._java_obj.getUuid()

py-scoring/tests/unit/with_runtime_spark/test_mojo_pipeline.py

@@ -164,3 +164,4 @@ def testMojoPipelinePredictionInterval(spark):
     assert predictionDF.select("secret_Pressure3pm").distinct().count() == expectedCount
     assert predictionDF.select("`secret_Pressure3pm.lower`").distinct().count() == expectedCount
     assert predictionDF.select("`secret_Pressure3pm.upper`").distinct().count() == expectedCount
+    assert mojo.getUuid() == "test_regression_accuracy3_e5169_d71b"

r/src/R/ai/h2o/sparkling/ml/models/H2OMOJOPipelineModel.R

@@ -33,5 +33,8 @@ H2OMOJOPipelineModel <- setRefClass("H2OMOJOPipelineModel", contains = ("H2OAlgo
   },
   getScoringBulkSize = function() {
     invoke(.self$jmojo, "getScoringBulkSize")
+  },
+  getUuid = function() {
+    invoke(.self$jmojo, "getUuid")
   }
 ))

r/src/tests/testthat/testMojoPipeline.R

@@ -103,6 +103,7 @@ test_that("test MOJO predicition intervals", {
 
   flattenedContributions <- tidyr::unnest_wider(data = mojoOutput, col = "prediction")
   expect_equal(length(colnames(flattenedContributions)), length(colnames(dataset)) + 3)
+  expect_equal(mojo$getUuid(), "test_regression_accuracy3_e5169_d71b")
 })
 
 spark_disconnect(sc)

scoring/src/main/scala/ai/h2o/sparkling/ml/models/H2OMOJOPipelineModel.scala

@@ -21,7 +21,7 @@ import ai.h2o.mojos.runtime.MojoPipeline
 import ai.h2o.mojos.runtime.api.{MojoPipelineService, PipelineConfig}
 import ai.h2o.mojos.runtime.frame.MojoColumn.Type
 import ai.h2o.mojos.runtime.frame.MojoFrame
-import ai.h2o.sparkling.ml.params.{H2OAlgorithmMOJOParams, H2OBaseMOJOParams, HasFeatureTypesOnMOJO}
+import ai.h2o.sparkling.ml.params.{H2OAlgorithmMOJOParams, H2OBaseMOJOParams, HasFeatureTypesOnMOJO, ParameterConstructorMethods}
 import ai.h2o.sparkling.sql.catalyst.encoders.RowEncoder
 import com.google.common.collect.Iterators
 import org.apache.spark.annotation.DeveloperApi
@@ -40,10 +40,13 @@ class H2OMOJOPipelineModel(override val uid: String)
   with H2OMOJOWritable
   with H2OAlgorithmMOJOParams
   with H2OBaseMOJOParams
+  with ParameterConstructorMethods
   with HasFeatureTypesOnMOJO {
 
   H2OMOJOPipelineCache.startCleanupThread()
 
+  protected final val uuid = stringParam(name = "UUID", doc = "MOJO2 UUID")
+
   // private parameter used to store MOJO output columns
   protected final val outputSubCols: StringArrayParam =
     new StringArrayParam(this, "outputSubCols", "Names of sub-columns under the output column")
@@ -89,6 +92,8 @@ class H2OMOJOPipelineModel(override val uid: String)
     "scoringBulkSize",
     "A number of records passed at once to the underlying mojo2 runtime library.  Supported only by DriverlessAI MOJO models.")
 
+  def getUuid(): String = $ { uuid }
+
   def getOutputSubCols(): Array[String] = $ { outputSubCols }
 
   def getContributionsCol(): String = "contributions"
@@ -341,6 +346,7 @@ object H2OMOJOPipelineModel extends H2OMOJOReadable[H2OMOJOPipelineModel] with H
     val featureTypesMap = featureCols.zip(featureTypeNames).toMap
     val outputCols = pipelineMojo.getOutputMeta.getColumns.asScala
     model.set(model.featureTypes, featureTypesMap)
+    model.set(model.uuid, pipelineMojo.getUuid)
     model.set(model.outputSubCols, outputCols.map(_.getColumnName).toArray)
     model.set(model.outputSubTypes, outputCols.map(_.getColumnType.toString).toArray)
   }