[SW-2559] Expose Scoring History and Variable Importances on H2OMOJOM…

…odel (#2525)

* [SW-2559] Expose Scoring History and Variable Importances on H2OMOJOModel

* Deprecate variable importances on H2ODeepLearning

* Rename variable importances to feature importances

* Extend documentation

* Fix deserialization problem

* spotless apply

* Fix python tests and deprecation on DeepLearningMOJOModel

* fix python tests

* fix R tests

* typo

* fix R tests

* spotless apply

* null handling

(cherry picked from commit 3fab572)

# Conflicts:
#	api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/AlgorithmConfigurations.scala

api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/AlgorithmConfigurations.scala

@@ -99,13 +99,22 @@ trait AlgorithmConfigurations {
 
     val noDeprecation = Seq.empty
 
+    val dlDeprecations = Seq(
+      DeprecatedField(
+        "variable_importances",
+        "HasDeprecatedVariableImportances",
+        "variableImportances",
+        "3.36",
+        Some("calculateFeatureImportances"),
+        Some("HasDeprecatedVariableImportancesOnMOJO")))
+
     val algorithmParameters = Seq[(String, Class[_], Class[_], Seq[ExplicitField], Seq[DeprecatedField])](
       ("H2OXGBoostParams", classOf[XGBParamsV3], classOf[XGBoostParameters], xgboostFields, noDeprecation),
       ("H2OGBMParams", classOf[GBMV3.GBMParametersV3], classOf[GBMParameters], gbmFields, noDeprecation),
       ("H2ODRFParams", classOf[DRFV3.DRFParametersV3], classOf[DRFParameters], drfFields, noDeprecation),
       ("H2OGLMParams", classOf[GLMV3.GLMParametersV3], classOf[GLMParameters], glmFields, glmDeprecations),
       ("H2OGAMParams", classOf[GAMV3.GAMParametersV3], classOf[GAMParameters], gamFields, noDeprecation),
-      ("H2ODeepLearningParams", classOf[DLParamsV3], classOf[DeepLearningParameters], dlFields, noDeprecation),
+      ("H2ODeepLearningParams", classOf[DLParamsV3], classOf[DeepLearningParameters], dlFields, dlDeprecations),
       ("H2OKMeansParams", classOf[KMeansParamsV3], classOf[KMeansParameters], kmeansFields, kmeansDeprecations),
       ("H2OCoxPHParams", classOf[CoxPHParametersV3], classOf[CoxPHParameters], coxPHFields, noDeprecation),
       ("H2OIsolationForestParams", classOf[IFParamsV3], classOf[IsolationForestParameters], ifFields, noDeprecation))

api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/ParameterNameConverter.scala

@@ -29,7 +29,8 @@ object ParameterNameConverter {
     "colsample_bynode" -> "colSampleByNode",
     "rand_family" -> "randomFamily",
     "rand_link" -> "randomLink",
-    "calibration_frame" -> "calibrationDataFrame")
+    "calibration_frame" -> "calibrationDataFrame",
+    "variable_importances" -> "calculateFeatureImportances")
 
   val conversionRules: Map[String, String] = Map("Column" -> "Col")
 

api-generation/src/main/scala/ai/h2o/sparkling/api/generation/common/ParameterSubstitutionContext.scala

@@ -40,7 +40,13 @@ case class ExplicitField(
     sparkName: Option[String] = None,
     mojoImplementation: Option[String] = None)
 
-case class DeprecatedField(h2oName: String, implementation: String, sparkName: String, version: String)
+case class DeprecatedField(
+    h2oName: String,
+    implementation: String,
+    sparkName: String,
+    version: String,
+    replacement: Option[String] = None,
+    mojoImplementation: Option[String] = None)
 
 object DefaultValueSource extends Enumeration {
   type DefaultValueSource = Value

api-generation/src/main/scala/ai/h2o/sparkling/api/generation/python/AlgorithmTemplateBase.scala

@@ -41,7 +41,13 @@ trait AlgorithmTemplateBase extends PythonEntityTemplate {
         .map { param =>
           val version = param.version
           val name = param.sparkName
-          s"""        if '$name' in kwargs:
+          val valuePropagation = param.replacement match {
+            case Some(replacement) =>
+              s"""\n            if '$replacement' not in kwargs:
+                 |                kwargs['$replacement'] = kwargs['$name']""".stripMargin
+            case None => ""
+          }
+          s"""        if '$name' in kwargs:$valuePropagation
              |            del kwargs['$name']
              |            warn("The parameter '$name' is deprecated and will be removed in the version $version.")""".stripMargin
         }

api-generation/src/main/scala/ai/h2o/sparkling/api/generation/python/MOJOModelTemplate.scala

@@ -33,7 +33,8 @@ object MOJOModelTemplate
     val entityName = algorithmSubstitutionContext.entityName
     val namespace = algorithmSubstitutionContext.namespace
     val algorithmType = algorithmSubstitutionContext.algorithmType.replace("Algorithm", "MOJOModelParams")
-    val explicitFields = parameterSubstitutionContext.explicitFields.flatMap(_.mojoImplementation)
+    val explicitFields = parameterSubstitutionContext.explicitFields.flatMap(_.mojoImplementation) ++
+      parameterSubstitutionContext.deprecatedFields.flatMap(_.mojoImplementation)
     val parents = Seq(algorithmType) ++ explicitFields
 
     val imports = Seq(

api-generation/src/main/scala/ai/h2o/sparkling/api/generation/scala/MOJOModelTemplate.scala

@@ -34,7 +34,8 @@ object MOJOModelTemplate
       .filter(parameter =>
         !IgnoredParameters.ignoredInMOJOs(algorithmSubstitutionContext.entityName).contains(parameter.h2oName))
 
-    val explicitFieldImplementations = parameterSubstitutionContext.explicitFields.flatMap(_.mojoImplementation)
+    val explicitFieldImplementations = parameterSubstitutionContext.explicitFields.flatMap(_.mojoImplementation) ++
+      parameterSubstitutionContext.deprecatedFields.flatMap(_.mojoImplementation)
 
     val imports = Seq(
       "ai.h2o.sparkling.ml.params.ParameterConstructorMethods",

booklet/src/sections/productionazing.tex

@@ -198,6 +198,22 @@ \subsubsection{Obtaining Model Category}
 
 The method \texttt{getModelCategory} can be used to get the model category (such as \texttt{binomial}, \texttt{multinomial} etc).
 
+\subsubsection{Obtaining Feature Types}
+
+The method \texttt{getFeatureTypes} returns a map/dictionary from a feature name to a corresponding feature type
+[\texttt{enum} (categorical), \texttt{numeric}, \texttt{string}, etc.]. These pieces helps to understand how individual
+columns of the training dataset were treated during the model training.
+
+\subsubsection{Obtaining Feature Importances}
+
+The method \texttt{getFeatureImportances} returns a data frame describing importance of each feature. The importance is expressed
+by several numbers (Relative Importance, Scaled Importance and Percentage).
+
+\subsubsection{Obtaining Scoring History}
+
+The method \texttt{getScoringHistory} returns a data frame describing how the model evolved during the training process
+according to a certain training and validation metrics.
+
 \subsubsection{Obtaining Training Params}
 
 The method \texttt{getTrainingParams} can be used to get map containing all training parameters used in the H2O. It is a map

doc/src/site/sphinx/deployment/load_mojo.rst

@@ -341,6 +341,20 @@ The method ``getFeatureTypes`` returns a map/dictionary from a feature name to a
 [``enum`` (categorical), ``numeric``, ``string``, etc.]. These pieces helps to understand how individual columns of
 the training dataset were treated during the model training.
 
+Obtaining Feature Importances
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The method ``getFeatureImportances`` returns a data frame describing importance of each feature. The importance is expressed
+by several numbers (Relative Importance, Scaled Importance and Percentage). `H2O-3 documentation
+<https://h2o-release.s3.amazonaws.com/h2o/rel-SUBST_H2O_RELEASE_NAME/SUBST_H2O_BUILD_NUMBER/docs-website/h2o-docs/variable-importance.html>`__
+describes how the numbers are calculated.
+
+
+Obtaining Scoring History
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The method ``getScoringHistory`` returns a data frame describing how the model evolved during the training process according to
+a certain training and validation metrics.
 
 Obtaining Metrics
 ^^^^^^^^^^^^^^^^^

doc/src/site/sphinx/migration_guide.rst

@@ -9,6 +9,12 @@ From 3.34 to 3.36
 - The methods ``getWithDetailedPredictionCol`` and ``setWithDetailedPredictionCol`` on all SW Algorithms and
   MOJO models were removed without replacement.
 
+- The parameter ``variableImportances`` of ``H2ODeepLearning`` has been replaced with ``calculateFeatureImportances`` as
+  well as the methods ``getVariableImportances`` and ``setVariableImportances`` on ``H2ODeepLearning`` have been replaced
+  with ``getCalculateFeatureImportances`` and ``setCalculateFeatureImportances``.
+
+- The method ``getVariableImportances`` of ``H2ODeepLearningMOJOModel`` has been replaced with ``getCalculateFeatureImportances``.
+
 From 3.32.1 to 3.34
 -------------------
 

ml/src/main/scala/ai/h2o/sparkling/ml/params/HasDeprecatedVariableImportances.scala

@@ -0,0 +1,34 @@
+/*
+ * 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.params
+
+import ai.h2o.sparkling.macros.DeprecatedMethod
+import org.apache.spark.expose.Logging
+
+trait HasDeprecatedVariableImportances extends Logging {
+
+  def getCalculateFeatureImportances(): Boolean
+
+  @DeprecatedMethod("getCalculateFeatureImportances", "3.36")
+  def getVariableImportances(): Boolean = getCalculateFeatureImportances()
+
+  def setCalculateFeatureImportances(value: Boolean): this.type
+
+  @DeprecatedMethod("setCalculateFeatureImportances", "3.36")
+  def setVariableImportances(value: Boolean): this.type = setCalculateFeatureImportances(value)
+}

py-scoring/src/ai/h2o/sparkling/ml/params/H2OMOJOModelParams.py

@@ -46,6 +46,12 @@ def getTrainingParams(self):
     def getModelCategory(self):
         return self._java_obj.getModelCategory()
 
+    def getScoringHistory(self):
+        return H2OTypeConverters.scalaToPythonDataFrame(self._java_obj.getScoringHistory())
+
+    def getFeatureImportances(self):
+        return H2OTypeConverters.scalaToPythonDataFrame(self._java_obj.getFeatureImportances())
+
 
 class HasOffsetCol:
 

py-scoring/src/ai/h2o/sparkling/ml/params/H2OTypeConverters.py

@@ -19,7 +19,7 @@
 from pyspark.ml.linalg import DenseVector, DenseMatrix
 from pyspark.ml.param import TypeConverters
 from pyspark.ml.util import _jvm
-from pyspark.sql import DataFrame
+from pyspark.sql import DataFrame, SparkSession
 
 
 class H2OTypeConverters(object):
@@ -500,3 +500,13 @@ def scalaArrayToPythonArray(array):
             return [v for v in array]
         else:
             raise TypeError("Invalid type.")
+
+    @staticmethod
+    def scalaToPythonDataFrame(jdf):
+        if jdf is None:
+            return None
+        elif isinstance(jdf, JavaObject):
+            sqlContext = SparkSession.builder.getOrCreate()._wrapped
+            return DataFrame(jdf, sqlContext)
+        else:
+            raise TypeError("Invalid type.")

py-scoring/src/ai/h2o/sparkling/ml/params/HasDeprecatedVariableImportances.py

@@ -0,0 +1,32 @@
+#
+# 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.
+#
+
+from pyspark.ml.param import *
+import warnings
+
+class HasDeprecatedVariableImportances(Params):
+
+    def getVariableImportances(self):
+        warnings.warn("The method 'getVariableImportances' is deprecated and will be removed in the version 3.36." +
+                      "The replacement is 'getCalculateFeatureImportances'.")
+        return self.getCalculateFeatureImportances()
+
+    def setVariableImportances(self, value):
+        warnings.warn("The method 'setVariableImportances' is deprecated and will be removed in the version 3.36." +
+                      "The replacement is 'setCalculateFeatureImportances'.")
+        self.setCalculateFeatureImportances(value)
+        return self

py-scoring/src/ai/h2o/sparkling/ml/params/HasDeprecatedVariableImportancesOnMOJO.py

@@ -0,0 +1,26 @@
+#
+# 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.
+#
+
+from pyspark.ml.param import *
+import warnings
+
+class HasDeprecatedVariableImportancesOnMOJO(Params):
+
+    def getVariableImportances(self):
+        warnings.warn("The method 'getVariableImportances' is deprecated and will be removed in the version 3.36." +
+                      "The replacement is 'getCalculateFeatureImportances'.")
+        return self.getCalculateFeatureImportances()

py/tests/unit/with_runtime_sparkling/test_mojo.py

@@ -80,6 +80,33 @@ def testFeatureTypes(gbmModel):
     assert len(types) == 9
 
 
+def testScoringHistory(gbmModel):
+    scoringHistoryDF = gbmModel.getScoringHistory()
+    assert scoringHistoryDF.count() > 0
+    assert len(scoringHistoryDF.columns) > 0
+
+
+def testFeatureImportances(gbmModel):
+    featureImportancesDF = gbmModel.getFeatureImportances()
+    assert featureImportancesDF.select("Variable").collect().sort() == gbmModel.getFeaturesCols().sort()
+    assert len(featureImportancesDF.columns) == 4
+
+
+def testFeatureImportancesAndScoringHistoryAreSameAfterSerde(gbmModel):
+    expectedScoringHistoryDF = gbmModel.getScoringHistory()
+    expectedFeatureImportancesDF = gbmModel.getFeatureImportances()
+
+    filePath = "file://" + os.path.abspath("build/scoringHistoryAndFeatureImportancesSerde")
+    gbmModel.write().overwrite().save(filePath)
+    loadedModel = H2OMOJOModel.load(filePath)
+
+    loadedScoringHistoryDF = loadedModel.getScoringHistory()
+    loadedFeatureImportancesDF = loadedModel.getFeatureImportances()
+
+    unit_test_utils.assert_data_frames_are_identical(expectedScoringHistoryDF, loadedScoringHistoryDF)
+    unit_test_utils.assert_data_frames_are_identical(expectedFeatureImportancesDF, loadedFeatureImportancesDF)
+
+
 def getCurrentMetrics():
     metrics = gbmModel.getCurrentMetrics()
     assert metrics == gbmModel.getTrainingMetrics()

r/src/R/ai/h2o/sparkling/ml/models/H2OMOJOModel.R

@@ -39,11 +39,9 @@ H2OMOJOModel <- setRefClass("H2OMOJOModel", contains = ("H2OMOJOModelBase"), met
   getModelDetails = function() {
     invoke(.self$jmojo, "getModelDetails")
   },
-
   getDomainValues = function() {
     invoke(.self$jmojo, "getDomainValues")
   },
-
   getTrainingMetrics = function() {
     invoke(.self$jmojo, "getTrainingMetrics")
   },
@@ -61,6 +59,14 @@ H2OMOJOModel <- setRefClass("H2OMOJOModel", contains = ("H2OMOJOModelBase"), met
   },
   getModelCategory = function() {
     invoke(.self$jmojo, "getModelCategory")
+  },
+  getScoringHistory = function() {
+    outputFrame <- invoke(.self$jmojo, "getScoringHistory")
+    sdf_register(outputFrame)
+  },
+  getFeatureImportances = function() {
+    outputFrame <- invoke(.self$jmojo, "getFeatureImportances")
+    sdf_register(outputFrame)
   }
 ))
 

r/src/tests/testthat/testMojo.R

@@ -69,6 +69,27 @@ test_that("test getDomainValues", {
   expect_true(is.null(domainValues[["ID"]]))
 })
 
+test_that("test getScoringHistory", {
+  model <- H2OMOJOModel.createFromMojo(paste0("file://", normalizePath("../../../../../ml/src/test/resources/binom_model_prostate.mojo")))
+  scoringHistory <- model$getScoringHistory()
+
+  numberOfRecordsFrame <- dplyr::tally(scoringHistory)
+  count <- as.double(dplyr::collect(numberOfRecordsFrame)[[1]])
+
+  expect_true(count > 0)
+})
+
+test_that("test getFeatureImportances", {
+  model <- H2OMOJOModel.createFromMojo(paste0("file://", normalizePath("../../../../../ml/src/test/resources/binom_model_prostate.mojo")))
+  featureImportances <- model$getFeatureImportances()
+  expectedCount <- length(model$getFeaturesCols())
+
+  numberOfRecordsFrame <- dplyr::tally(featureImportances)
+  count <- as.double(dplyr::collect(numberOfRecordsFrame)[[1]])
+
+  expect_equal(count, expectedCount)
+})
+
 test_that("test training params", {
   model <- H2OMOJOModel.createFromMojo(paste0("file://", normalizePath("../../../../../ml/src/test/resources/binom_model_prostate.mojo")))
   params <- model$getTrainingParams()