[SPARK-51132][ML][BUILD] Upgrade JPMML to 1.7.1

### What changes were proposed in this pull request?

This PR aims to upgrade `JPMML` from 1.4.8 to 1.7.1. The main changes from 1.4.8 to 1.7.1 are as follows:
1. Starting from version 1.5.0, `PMML` schema version has been updated from 4.3 to 4.4, the related commit is:
jpmml/jpmml-model@7d8607a
2. Starting from version 1.6.0, `Java XML Binding` has been upgraded to `Jakarta XML Binding`,  the related commit is: jpmml/jpmml-model@d76de1c

After this PR, the exported PMML model schema version has been upgraded from 4.3(https://dmg.org/pmml/v4-3/GeneralStructure.html) to 4.4(https://dmg.org/pmml/v4-4-1/GeneralStructure.html).

### Why are the changes needed?

1. Upgrade the PMML standard to the latest 4.4 version ;
2. Upgrade `Java XML Binding` to `Jakarta XML Binding` by upgrade `JPMML`.

### Does this PR introduce _any_ user-facing change?

Yes, the exported PMML model version has been upgraded from 4.3 to 4.4, details are as follows:
1. `version` has been changed from 4.2 to 4.4;
2. `xmlns` has been changed from `http://www.dmg.org/PMML-4_3` to `http://www.dmg.org/PMML-4_4`;
3. `Application version` has been changed to the  current Spark version.

Before:
```
<PMML version="4.2" xmlns="http://www.dmg.org/PMML-4_3" xmlns:data="http://jpmml.org/jpmml-model/InlineTable">
    <Header description="k-means clustering">
        <Application name="Apache Spark MLlib" version="3.5.4"/>
        <Timestamp>2025-02-08T10:00:55</Timestamp>
    </Header>
...
```
After:
```
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<PMML version="4.4" xmlns="http://www.dmg.org/PMML-4_4" xmlns:data="http://jpmml.org/jpmml-model/InlineTable">
    <Header description="k-means clustering">
        <Application name="Apache Spark MLlib" version="4.1.0-SNAPSHOT"/>
        <Timestamp>2025-02-08T11:34:40</Timestamp>
    </Header>
...
```

**Despite this change, on the one hand, PMML version 4.4 was released as early as November 2019. On the other hand, the upgrade from 4.3 to 4.4 is backward compatible.(Reference: https://dmg.org/pmml/v4-4-1/Changes.html)**

### How was this patch tested?

1. Passed GA;
2. Manually checked the changes after the upgrade.
KMeans:
```
import org.apache.spark.ml.clustering.KMeans
val dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
val kmeans = new KMeans().setK(2).setSeed(1L)
val model = kmeans.fit(dataset)
model.write.format("pmml").save("./kmeans")
```

LinearRegression:
```
import org.apache.spark.ml.regression.LinearRegression
val dataset = spark.read.format("libsvm").load("data/mllib/sample_linear_regression_data.txt")
val lr = new LinearRegression()
val model = lr.fit(dataset)
model.write.format("pmml").save("./lr")
```
### Was this patch authored or co-authored using generative AI tooling?

No.

Closes #49854 from wayneguow/pmml.

Authored-by: Wei Guo <[email protected]>
Signed-off-by: yangjie01 <[email protected]>

LICENSE-binary

@@ -515,7 +515,9 @@ javax.xml.bind:jaxb-api    https://github.com/javaee/jaxb-v2
 Eclipse Distribution License (EDL) 1.0
 --------------------------------------
 com.sun.istack:istack-commons-runtime
+jakarta.activation:jakarta.activation-api
 jakarta.xml.bind:jakarta.xml.bind-api
+org.glassfish.jaxb:jaxb-core
 org.glassfish.jaxb:jaxb-runtime
 
 Eclipse Public License (EPL) 2.0

dev/deps/spark-deps-hadoop-3-hive-2.3

@@ -101,7 +101,7 @@ httpclient/4.5.14//httpclient-4.5.14.jar
 httpcore/4.4.16//httpcore-4.4.16.jar
 icu4j/76.1//icu4j-76.1.jar
 ini4j/0.5.4//ini4j-0.5.4.jar
-istack-commons-runtime/3.0.8//istack-commons-runtime-3.0.8.jar
+istack-commons-runtime/4.1.2//istack-commons-runtime-4.1.2.jar
 ivy/2.5.3//ivy-2.5.3.jar
 j2objc-annotations/3.0.0//j2objc-annotations-3.0.0.jar
 jackson-annotations/2.18.2//jackson-annotations-2.18.2.jar
@@ -113,21 +113,22 @@ jackson-dataformat-yaml/2.18.2//jackson-dataformat-yaml-2.18.2.jar
 jackson-datatype-jsr310/2.18.2//jackson-datatype-jsr310-2.18.2.jar
 jackson-mapper-asl/1.9.13//jackson-mapper-asl-1.9.13.jar
 jackson-module-scala_2.13/2.18.2//jackson-module-scala_2.13-2.18.2.jar
+jakarta.activation-api/2.1.3//jakarta.activation-api-2.1.3.jar
 jakarta.annotation-api/2.1.1//jakarta.annotation-api-2.1.1.jar
 jakarta.inject-api/2.0.1//jakarta.inject-api-2.0.1.jar
 jakarta.servlet-api/5.0.0//jakarta.servlet-api-5.0.0.jar
 jakarta.validation-api/3.0.2//jakarta.validation-api-3.0.2.jar
 jakarta.ws.rs-api/3.0.0//jakarta.ws.rs-api-3.0.0.jar
-jakarta.xml.bind-api/2.3.2//jakarta.xml.bind-api-2.3.2.jar
+jakarta.xml.bind-api/4.0.2//jakarta.xml.bind-api-4.0.2.jar
 janino/3.1.9//janino-3.1.9.jar
 java-diff-utils/4.15//java-diff-utils-4.15.jar
 java-xmlbuilder/1.2//java-xmlbuilder-1.2.jar
 javassist/3.30.2-GA//javassist-3.30.2-GA.jar
 javax.jdo/3.2.0-m3//javax.jdo-3.2.0-m3.jar
 javax.servlet-api/4.0.1//javax.servlet-api-4.0.1.jar
 javolution/5.5.1//javolution-5.5.1.jar
-jaxb-api/2.2.11//jaxb-api-2.2.11.jar
-jaxb-runtime/2.3.2//jaxb-runtime-2.3.2.jar
+jaxb-core/4.0.5//jaxb-core-4.0.5.jar
+jaxb-runtime/4.0.5//jaxb-runtime-4.0.5.jar
 jcl-over-slf4j/2.0.16//jcl-over-slf4j-2.0.16.jar
 jdo-api/3.0.1//jdo-api-3.0.1.jar
 jdom2/2.0.6//jdom2-2.0.6.jar

docs/ml-migration-guide.md

@@ -26,6 +26,25 @@ Note that this migration guide describes the items specific to MLlib.
 Many items of SQL migration can be applied when migrating MLlib to higher versions for DataFrame-based APIs.
 Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.html).
 
+## Upgrading from MLlib 3.5 to 4.0
+
+### Breaking changes
+{:.no_toc}
+
+There are no breaking changes.
+
+### Deprecations and changes of behavior
+{:.no_toc}
+
+**Deprecations**
+
+There are no deprecations.
+
+**Changes of behavior**
+
+* [SPARK-51132](https://issues.apache.org/jira/browse/SPARK-51132):
+  The PMML XML schema version of exported PMML format models by [PMML model export](mllib-pmml-model-export.html) has been upgraded from `PMML-4_3` to `PMML-4_4`.
+
 ## Upgrading from MLlib 2.4 to 3.0
 
 ### Breaking changes

mllib/pom.xml

@@ -34,10 +34,6 @@
   <url>https://spark.apache.org/</url>
 
   <dependencies>
-    <dependency>
-      <groupId>javax.xml.bind</groupId>
-      <artifactId>jaxb-api</artifactId>
-    </dependency>
     <dependency>
       <groupId>org.scala-lang.modules</groupId>
       <artifactId>scala-parser-combinators_${scala.binary.version}</artifactId>
@@ -144,6 +140,10 @@
       <groupId>org.glassfish.jaxb</groupId>
       <artifactId>jaxb-runtime</artifactId>
     </dependency>
+    <dependency>
+      <groupId>jakarta.xml.bind</groupId>
+      <artifactId>jakarta.xml.bind-api</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-tags_${scala.binary.version}</artifactId>

mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala

@@ -20,7 +20,7 @@ package org.apache.spark.mllib.pmml
 import java.io.{File, OutputStream, StringWriter}
 import javax.xml.transform.stream.StreamResult
 
-import org.jpmml.model.JAXBUtil
+import org.jpmml.model.JAXBSerializer
 
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Since
@@ -39,7 +39,8 @@ trait PMMLExportable {
    */
   private def toPMML(streamResult: StreamResult): Unit = {
     val pmmlModelExport = PMMLModelExportFactory.createPMMLModelExport(this)
-    JAXBUtil.marshalPMML(pmmlModelExport.getPmml(), streamResult)
+    val jaxbSerializer = new JAXBSerializer()
+    jaxbSerializer.marshalPretty(pmmlModelExport.getPmml(), streamResult)
   }
 
   /**

mllib/src/main/scala/org/apache/spark/mllib/pmml/export/BinaryClassificationPMMLModelExport.scala

@@ -19,8 +19,8 @@ package org.apache.spark.mllib.pmml.`export`
 
 import scala.{Array => SArray}
 
-import org.dmg.pmml.{DataDictionary, DataField, DataType, FieldName, MiningField,
-  MiningFunction, MiningSchema, OpType}
+import org.dmg.pmml.{DataDictionary, DataField, DataType, MiningField, MiningFunction,
+  MiningSchema, OpType}
 import org.dmg.pmml.regression.{NumericPredictor, RegressionModel, RegressionTable}
 
 import org.apache.spark.mllib.regression.GeneralizedLinearModel
@@ -44,7 +44,7 @@ private[mllib] class BinaryClassificationPMMLModelExport(
      pmml.getHeader.setDescription(description)
 
      if (model.weights.size > 0) {
-       val fields = new SArray[FieldName](model.weights.size)
+       val fields = new SArray[String](model.weights.size)
        val dataDictionary = new DataDictionary
        val miningSchema = new MiningSchema
        val regressionTableYES = new RegressionTable(model.intercept).setTargetCategory("1")
@@ -67,7 +67,7 @@ private[mllib] class BinaryClassificationPMMLModelExport(
          .addRegressionTables(regressionTableYES, regressionTableNO)
 
        for (i <- 0 until model.weights.size) {
-         fields(i) = FieldName.create("field_" + i)
+         fields(i) = "field_" + i
          dataDictionary.addDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE))
          miningSchema
            .addMiningFields(new MiningField(fields(i))
@@ -76,7 +76,7 @@ private[mllib] class BinaryClassificationPMMLModelExport(
        }
 
        // add target field
-       val targetField = FieldName.create("target")
+       val targetField = "target"
        dataDictionary
          .addDataFields(new DataField(targetField, OpType.CATEGORICAL, DataType.STRING))
        miningSchema

mllib/src/main/scala/org/apache/spark/mllib/pmml/export/GeneralizedLinearPMMLModelExport.scala

@@ -19,8 +19,8 @@ package org.apache.spark.mllib.pmml.`export`
 
 import scala.{Array => SArray}
 
-import org.dmg.pmml.{DataDictionary, DataField, DataType, FieldName, MiningField,
-  MiningFunction, MiningSchema, OpType}
+import org.dmg.pmml.{DataDictionary, DataField, DataType, MiningField, MiningFunction,
+  MiningSchema, OpType}
 import org.dmg.pmml.regression.{NumericPredictor, RegressionModel, RegressionTable}
 
 import org.apache.spark.mllib.regression.GeneralizedLinearModel
@@ -42,7 +42,7 @@ private[mllib] class GeneralizedLinearPMMLModelExport(
     pmml.getHeader.setDescription(description)
 
     if (model.weights.size > 0) {
-      val fields = new SArray[FieldName](model.weights.size)
+      val fields = new SArray[String](model.weights.size)
       val dataDictionary = new DataDictionary
       val miningSchema = new MiningSchema
       val regressionTable = new RegressionTable(model.intercept)
@@ -53,7 +53,7 @@ private[mllib] class GeneralizedLinearPMMLModelExport(
         .addRegressionTables(regressionTable)
 
       for (i <- 0 until model.weights.size) {
-        fields(i) = FieldName.create("field_" + i)
+        fields(i) = "field_" + i
         dataDictionary.addDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE))
         miningSchema
           .addMiningFields(new MiningField(fields(i))
@@ -62,7 +62,7 @@ private[mllib] class GeneralizedLinearPMMLModelExport(
       }
 
       // for completeness add target field
-      val targetField = FieldName.create("target")
+      val targetField = "target"
       dataDictionary.addDataFields(new DataField(targetField, OpType.CONTINUOUS, DataType.DOUBLE))
       miningSchema
         .addMiningFields(new MiningField(targetField)

mllib/src/main/scala/org/apache/spark/mllib/pmml/export/KMeansPMMLModelExport.scala

@@ -20,7 +20,7 @@ package org.apache.spark.mllib.pmml.`export`
 import scala.{Array => SArray}
 
 import org.dmg.pmml.{Array, CompareFunction, ComparisonMeasure, DataDictionary, DataField, DataType,
-  FieldName, MiningField, MiningFunction, MiningSchema, OpType, SquaredEuclidean}
+  MiningField, MiningFunction, MiningSchema, OpType, SquaredEuclidean}
 import org.dmg.pmml.clustering.{Cluster, ClusteringField, ClusteringModel}
 
 import org.apache.spark.mllib.clustering.KMeansModel
@@ -40,7 +40,7 @@ private[mllib] class KMeansPMMLModelExport(model: KMeansModel) extends PMMLModel
 
     if (model.clusterCenters.length > 0) {
       val clusterCenter = model.clusterCenters(0)
-      val fields = new SArray[FieldName](clusterCenter.size)
+      val fields = new SArray[String](clusterCenter.size)
       val dataDictionary = new DataDictionary
       val miningSchema = new MiningSchema
       val comparisonMeasure = new ComparisonMeasure()
@@ -55,7 +55,7 @@ private[mllib] class KMeansPMMLModelExport(model: KMeansModel) extends PMMLModel
         .setNumberOfClusters(model.clusterCenters.length)
 
       for (i <- 0 until clusterCenter.size) {
-        fields(i) = FieldName.create("field_" + i)
+        fields(i) = "field_" + i
         dataDictionary.addDataFields(new DataField(fields(i), OpType.CONTINUOUS, DataType.DOUBLE))
         miningSchema
           .addMiningFields(new MiningField(fields(i))

mllib/src/main/scala/org/apache/spark/mllib/pmml/export/PMMLModelExport.scala

@@ -23,7 +23,7 @@ import java.util.Locale
 
 import scala.beans.BeanProperty
 
-import org.dmg.pmml.{Application, Header, PMML, Timestamp}
+import org.dmg.pmml.{Application, Header, PMML, Timestamp, Version}
 
 private[mllib] trait PMMLModelExport {
 
@@ -44,6 +44,6 @@ private[mllib] trait PMMLModelExport {
     val header = new Header()
       .setApplication(app)
       .setTimestamp(timestamp)
-    new PMML("4.2", header, null)
+    new PMML(Version.PMML_4_4.getVersion(), header, null)
   }
 }

mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala

@@ -1165,7 +1165,7 @@ class LinearRegressionSuite extends MLTest with DefaultReadWriteTest with PMMLRe
       assert(fields(0).getOpType() == OpType.CONTINUOUS)
       val pmmlRegressionModel = pmml.getModels().get(0).asInstanceOf[PMMLRegressionModel]
       val pmmlPredictors = pmmlRegressionModel.getRegressionTables.get(0).getNumericPredictors
-      val pmmlWeights = pmmlPredictors.asScala.map(_.getCoefficient()).toList
+      val pmmlWeights = pmmlPredictors.asScala.map(_.getCoefficient().doubleValue()).toList
       assert(pmmlWeights(0) ~== model.coefficients(0) relTol 1E-3)
       assert(pmmlWeights(1) ~== model.coefficients(1) relTol 1E-3)
     }

mllib/src/test/scala/org/apache/spark/ml/util/PMMLUtils.scala

@@ -20,7 +20,7 @@ import java.io.ByteArrayInputStream
 import java.nio.charset.StandardCharsets
 
 import org.dmg.pmml.PMML
-import org.jpmml.model.{JAXBUtil, SAXUtil}
+import org.jpmml.model.{JAXBSerializer, SAXUtil}
 import org.jpmml.model.filters.ImportFilter
 
 /**
@@ -37,6 +37,7 @@ private[spark] object PMMLUtils {
     val transformed = SAXUtil.createFilteredSource(
       new ByteArrayInputStream(input.getBytes(StandardCharsets.UTF_8)),
       new ImportFilter())
-    JAXBUtil.unmarshalPMML(transformed)
+    val jaxbSerializer = new JAXBSerializer()
+    jaxbSerializer.unmarshal(transformed).asInstanceOf[PMML]
   }
 }

pom.xml

@@ -571,7 +571,7 @@
       <dependency>
         <groupId>org.jpmml</groupId>
         <artifactId>pmml-model</artifactId>
-        <version>1.4.8</version>
+        <version>1.7.1</version>
         <scope>provided</scope>
         <exclusions>
           <exclusion>
@@ -599,32 +599,24 @@
       <dependency>
         <groupId>org.glassfish.jaxb</groupId>
         <artifactId>jaxb-runtime</artifactId>
-        <version>2.3.2</version>
+        <version>4.0.5</version>
         <scope>compile</scope>
         <exclusions>
-          <!-- for now, we only write XML in PMML export, and these can be excluded -->
-          <exclusion>
-            <groupId>com.sun.xml.fastinfoset</groupId>
-            <artifactId>FastInfoset</artifactId>
-          </exclusion>
           <exclusion>
             <groupId>org.glassfish.jaxb</groupId>
             <artifactId>txw2</artifactId>
           </exclusion>
           <exclusion>
-            <groupId>org.jvnet.staxex</groupId>
-            <artifactId>stax-ex</artifactId>
-          </exclusion>
-          <!--
-            SPARK-27611: Exclude redundant javax.activation implementation, which
-            conflicts with the existing javax.activation:activation:1.1.1 dependency.
-            -->
-          <exclusion>
-            <groupId>jakarta.activation</groupId>
-            <artifactId>jakarta.activation-api</artifactId>
+            <groupId>org.eclipse.angus</groupId>
+            <artifactId>angus-activation</artifactId>
           </exclusion>
         </exclusions>
       </dependency>
+      <dependency>
+        <groupId>jakarta.xml.bind</groupId>
+        <artifactId>jakarta.xml.bind-api</artifactId>
+        <version>4.0.2</version>
+      </dependency>
       <dependency>
         <groupId>org.apache.commons</groupId>
         <artifactId>commons-lang3</artifactId>
@@ -1061,13 +1053,6 @@
         <groupId>org.glassfish.jersey.core</groupId>
         <artifactId>jersey-server</artifactId>
         <version>${jersey.version}</version>
-        <!-- SPARK-28765 Unused JDK11-specific dependency -->
-        <exclusions>
-          <exclusion>
-            <groupId>jakarta.xml.bind</groupId>
-            <artifactId>jakarta.xml.bind-api</artifactId>
-          </exclusion>
-        </exclusions>
       </dependency>
       <dependency>
         <groupId>org.glassfish.jersey.core</groupId>