Set feature_names and feature_types in jvm-packages (#9364)

* 1. Add parameters to set feature names and feature types
2. Save feature names and feature types to native json model

* Change serialization and deserialization format to ubj.

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala

@@ -74,7 +74,9 @@ private[scala] case class XGBoostExecutionParams(
     earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
     cacheTrainingSet: Boolean,
     treeMethod: Option[String],
-    isLocal: Boolean) {
+    isLocal: Boolean,
+    featureNames: Option[Array[String]],
+    featureTypes: Option[Array[String]]) {
 
   private var rawParamMap: Map[String, Any] = _
 
@@ -213,6 +215,13 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
     val cacheTrainingSet = overridedParams.getOrElse("cache_training_set", false)
       .asInstanceOf[Boolean]
 
+    val featureNames = if (overridedParams.contains("feature_names")) {
+      Some(overridedParams("feature_names").asInstanceOf[Array[String]])
+    } else None
+    val featureTypes = if (overridedParams.contains("feature_types")){
+      Some(overridedParams("feature_types").asInstanceOf[Array[String]])
+    } else None
+
     val xgbExecParam = XGBoostExecutionParams(nWorkers, round, useExternalMemory, obj, eval,
       missing, allowNonZeroForMissing, trackerConf,
       checkpointParam,
@@ -220,7 +229,10 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
       xgbExecEarlyStoppingParams,
       cacheTrainingSet,
       treeMethod,
-      isLocal)
+      isLocal,
+      featureNames,
+      featureTypes
+    )
     xgbExecParam.setRawParamMap(overridedParams)
     xgbExecParam
   }
@@ -531,6 +543,16 @@ private object Watches {
     if (trainMargin.isDefined) trainMatrix.setBaseMargin(trainMargin.get)
     if (testMargin.isDefined) testMatrix.setBaseMargin(testMargin.get)
 
+    if (xgbExecutionParams.featureNames.isDefined) {
+      trainMatrix.setFeatureNames(xgbExecutionParams.featureNames.get)
+      testMatrix.setFeatureNames(xgbExecutionParams.featureNames.get)
+    }
+
+    if (xgbExecutionParams.featureTypes.isDefined) {
+      trainMatrix.setFeatureTypes(xgbExecutionParams.featureTypes.get)
+      testMatrix.setFeatureTypes(xgbExecutionParams.featureTypes.get)
+    }
+
     new Watches(Array(trainMatrix, testMatrix), Array("train", "test"), cacheDirName)
   }
 
@@ -643,6 +665,15 @@ private object Watches {
     if (trainMargin.isDefined) trainMatrix.setBaseMargin(trainMargin.get)
     if (testMargin.isDefined) testMatrix.setBaseMargin(testMargin.get)
 
+    if (xgbExecutionParams.featureNames.isDefined) {
+      trainMatrix.setFeatureNames(xgbExecutionParams.featureNames.get)
+      testMatrix.setFeatureNames(xgbExecutionParams.featureNames.get)
+    }
+    if (xgbExecutionParams.featureTypes.isDefined) {
+      trainMatrix.setFeatureTypes(xgbExecutionParams.featureTypes.get)
+      testMatrix.setFeatureTypes(xgbExecutionParams.featureTypes.get)
+    }
+
     new Watches(Array(trainMatrix, testMatrix), Array("train", "test"), cacheDirName)
   }
 }

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala

@@ -139,6 +139,12 @@ class XGBoostClassifier (
   def setSinglePrecisionHistogram(value: Boolean): this.type =
     set(singlePrecisionHistogram, value)
 
+  def setFeatureNames(value: Array[String]): this.type =
+    set(featureNames, value)
+
+  def setFeatureTypes(value: Array[String]): this.type =
+    set(featureTypes, value)
+
   // called at the start of fit/train when 'eval_metric' is not defined
   private def setupDefaultEvalMetric(): String = {
     require(isDefined(objective), "Users must set \'objective\' via xgboostParams.")

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala

@@ -141,6 +141,12 @@ class XGBoostRegressor (
   def setSinglePrecisionHistogram(value: Boolean): this.type =
     set(singlePrecisionHistogram, value)
 
+  def setFeatureNames(value: Array[String]): this.type =
+    set(featureNames, value)
+
+  def setFeatureTypes(value: Array[String]): this.type =
+    set(featureTypes, value)
+
   // called at the start of fit/train when 'eval_metric' is not defined
   private def setupDefaultEvalMetric(): String = {
     require(isDefined(objective), "Users must set \'objective\' via xgboostParams.")

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala

@@ -177,6 +177,21 @@ private[spark] trait GeneralParams extends Params {
 
   final def getSeed: Long = $(seed)
 
+  /** Feature's name, it will be set to DMatrix and Booster, and in the final native json model.
+   * In native code, the parameter name is feature_name.
+   * */
+  final val featureNames = new StringArrayParam(this, "feature_names",
+  "an array of feature names")
+
+  final def getFeatureNames: Array[String] = $(featureNames)
+
+  /** Feature types, q is numeric and c is categorical.
+   * In native code, the parameter name is feature_type
+   * */
+  final val featureTypes = new StringArrayParam(this, "feature_types",
+  "an array of feature types")
+
+  final def getFeatureTypes: Array[String] = $(featureTypes)
 }
 
 trait HasLeafPredictionCol extends Params {

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifierSuite.scala

@@ -27,6 +27,8 @@ import org.apache.commons.io.IOUtils
 
 import org.apache.spark.Partitioner
 import org.apache.spark.ml.feature.VectorAssembler
+import org.json4s.{DefaultFormats, Formats}
+import org.json4s.jackson.parseJson
 
 class XGBoostClassifierSuite extends AnyFunSuite with PerTest with TmpFolderPerSuite {
 
@@ -453,4 +455,26 @@ class XGBoostClassifierSuite extends AnyFunSuite with PerTest with TmpFolderPerS
     assert(!compareTwoFiles(new File(modelJsonPath, "data/XGBoostClassificationModel").getPath,
       nativeUbjModelPath))
   }
+
+  test("native json model file should store feature_name and feature_type") {
+    val featureNames = (1 to 33).map(idx => s"feature_${idx}").toArray
+    val featureTypes = (1 to 33).map(idx => "q").toArray
+    val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "multi:softprob", "num_class" -> "6", "num_round" -> 5,
+      "num_workers" -> numWorkers, "tree_method" -> treeMethod
+    )
+    val trainingDF = buildDataFrame(MultiClassification.train)
+    val xgb = new XGBoostClassifier(paramMap)
+      .setFeatureNames(featureNames)
+      .setFeatureTypes(featureTypes)
+    val model = xgb.fit(trainingDF)
+    val modelStr = new String(model._booster.toByteArray("json"))
+    System.out.println(modelStr)
+    val jsonModel = parseJson(modelStr)
+    implicit val formats: Formats = DefaultFormats
+    val featureNamesInModel = (jsonModel \ "learner" \ "feature_names").extract[List[String]]
+    val featureTypesInModel = (jsonModel \ "learner" \ "feature_types").extract[List[String]]
+    assert(featureNamesInModel.length == 33)
+    assert(featureTypesInModel.length == 33)
+  }
 }

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java

@@ -162,6 +162,51 @@ public class Booster implements Serializable, KryoSerializable {
     }
   }
 
+  /**
+   * Get feature names from the Booster.
+   * @return
+   * @throws XGBoostError
+   */
+  public final String[] getFeatureNames() throws XGBoostError {
+    int numFeature = (int) getNumFeature();
+    String[] out = new String[numFeature];
+    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterGetStrFeatureInfo(handle, "feature_name", out));
+    return out;
+  }
+
+  /**
+   * Set feature names to the Booster.
+   *
+   * @param featureNames
+   * @throws XGBoostError
+   */
+  public void setFeatureNames(String[] featureNames) throws XGBoostError {
+    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterSetStrFeatureInfo(
+        handle, "feature_name", featureNames));
+  }
+
+  /**
+   * Get feature types from the Booster.
+   * @return
+   * @throws XGBoostError
+   */
+  public final String[] getFeatureTypes() throws XGBoostError {
+    int numFeature = (int) getNumFeature();
+    String[] out = new String[numFeature];
+    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterGetStrFeatureInfo(handle, "feature_type", out));
+    return out;
+  }
+
+  /**
+   * Set feature types to the Booster.
+   * @param featureTypes
+   * @throws XGBoostError
+   */
+  public void setFeatureTypes(String[] featureTypes) throws XGBoostError {
+    XGBoostJNI.checkCall(XGBoostJNI.XGBoosterSetStrFeatureInfo(
+        handle, "feature_type", featureTypes));
+  }
+
   /**
    * Update the booster for one iteration.
    *
@@ -744,7 +789,7 @@ public class Booster implements Serializable, KryoSerializable {
   private void writeObject(java.io.ObjectOutputStream out) throws IOException {
     try {
       out.writeInt(version);
-      out.writeObject(this.toByteArray());
+      out.writeObject(this.toByteArray("ubj"));
     } catch (XGBoostError ex) {
       ex.printStackTrace();
       logger.error(ex.getMessage());
@@ -780,7 +825,7 @@ public class Booster implements Serializable, KryoSerializable {
   @Override
   public void write(Kryo kryo, Output output) {
     try {
-      byte[] serObj = this.toByteArray();
+      byte[] serObj = this.toByteArray("ubj");
       int serObjSize = serObj.length;
       output.writeInt(serObjSize);
       output.writeInt(version);

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java

@@ -198,6 +198,8 @@ public class XGBoost {
     if (booster == null) {
       // Start training on a new booster
       booster = new Booster(params, allMats);
+      booster.setFeatureNames(dtrain.getFeatureNames());
+      booster.setFeatureTypes(dtrain.getFeatureTypes());
       booster.loadRabitCheckpoint();
     } else {
       // Start training on an existing booster

jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoostJNI.java

@@ -164,4 +164,8 @@ class XGBoostJNI {
   public final static native int XGDMatrixCreateFromArrayInterfaceColumns(
     String featureJson, float missing, int nthread, long[] out);
 
+  public final static native int XGBoosterSetStrFeatureInfo(long handle, String field, String[] features);
+
+  public final static native int XGBoosterGetStrFeatureInfo(long handle, String field, String[] out);
+
 }

jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/DMatrix.scala

@@ -205,6 +205,26 @@ class DMatrix private[scala](private[scala] val jDMatrix: JDMatrix) {
     jDMatrix.setBaseMargin(column)
   }
 
+  /**
+   * set feature names
+   * @param values feature names
+   * @throws ml.dmlc.xgboost4j.java.XGBoostError
+   */
+  @throws(classOf[XGBoostError])
+  def setFeatureNames(values: Array[String]): Unit = {
+    jDMatrix.setFeatureNames(values)
+  }
+
+  /**
+   * set feature types
+   * @param values feature types
+   * @throws ml.dmlc.xgboost4j.java.XGBoostError
+   */
+  @throws(classOf[XGBoostError])
+  def setFeatureTypes(values: Array[String]): Unit = {
+    jDMatrix.setFeatureTypes(values)
+  }
+
   /**
    * Get group sizes of DMatrix (used for ranking)
    */
@@ -243,6 +263,26 @@ class DMatrix private[scala](private[scala] val jDMatrix: JDMatrix) {
     jDMatrix.getBaseMargin
   }
 
+  /**
+   * get feature names
+   * @throws ml.dmlc.xgboost4j.java.XGBoostError
+   * @return
+   */
+  @throws(classOf[XGBoostError])
+  def getFeatureNames: Array[String] = {
+    jDMatrix.getFeatureNames
+  }
+
+  /**
+   * get feature types
+   * @throws ml.dmlc.xgboost4j.java.XGBoostError
+   * @return
+   */
+  @throws(classOf[XGBoostError])
+  def getFeatureTypes: Array[String] = {
+    jDMatrix.getFeatureTypes
+  }
+
   /**
    * Slice the DMatrix and return a new DMatrix that only contains `rowIndex`.
    *

jvm-packages/xgboost4j/src/native/xgboost4j.cpp

@@ -1148,3 +1148,68 @@ JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixGetStrFea
   if (field) jenv->ReleaseStringUTFChars(jfield, field);
   return ret;
 }
+
+/*
+ * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
+ * Method:    XGBoosterSetStrFeatureInfo
+ * Signature: (JLjava/lang/String;[Ljava/lang/String;])I
+ */
+JNIEXPORT jint JNICALL
+Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSetStrFeatureInfo(
+    JNIEnv *jenv, jclass jclz, jlong jhandle, jstring jfield,
+    jobjectArray jfeatures) {
+  BoosterHandle handle = (BoosterHandle)jhandle;
+
+  const char *field = jenv->GetStringUTFChars(jfield, 0);
+
+  bst_ulong feature_num = (bst_ulong)jenv->GetArrayLength(jfeatures);
+
+  std::vector<std::string> features;
+  std::vector<char const*> features_char;
+
+  for (bst_ulong i = 0; i < feature_num; ++i) {
+    jstring jfeature = (jstring)jenv->GetObjectArrayElement(jfeatures, i);
+    const char *s = jenv->GetStringUTFChars(jfeature, 0);
+    features.push_back(std::string(s, jenv->GetStringLength(jfeature)));
+    if (s != nullptr) jenv->ReleaseStringUTFChars(jfeature, s);
+  }
+
+  for (size_t i = 0; i < features.size(); ++i) {
+    features_char.push_back(features[i].c_str());
+  }
+
+  int ret = XGBoosterSetStrFeatureInfo(
+      handle, field, dmlc::BeginPtr(features_char), feature_num);
+  JVM_CHECK_CALL(ret);
+  return ret;
+}
+
+/*
+ * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
+ * Method:    XGBoosterSetGtrFeatureInfo
+ * Signature: (JLjava/lang/String;[Ljava/lang/String;])I
+ */
+JNIEXPORT jint JNICALL
+Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetStrFeatureInfo(
+    JNIEnv *jenv, jclass jclz, jlong jhandle, jstring jfield,
+    jobjectArray jout) {
+  BoosterHandle handle = (BoosterHandle)jhandle;
+
+  const char *field = jenv->GetStringUTFChars(jfield, 0);
+
+  bst_ulong feature_num = (bst_ulong)jenv->GetArrayLength(jout);
+
+  const char **features;
+  std::vector<char *> features_char;
+
+  int ret = XGBoosterGetStrFeatureInfo(handle, field, &feature_num,
+                                       (const char ***)&features);
+  JVM_CHECK_CALL(ret);
+
+  for (bst_ulong i = 0; i < feature_num; i++) {
+    jstring jfeature = jenv->NewStringUTF(features[i]);
+    jenv->SetObjectArrayElement(jout, i, jfeature);
+  }
+
+  return ret;
+}

jvm-packages/xgboost4j/src/native/xgboost4j.h

@@ -383,6 +383,24 @@ JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGQuantileDMatrixC
 JNIEXPORT jint JNICALL Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGDMatrixCreateFromArrayInterfaceColumns
   (JNIEnv *, jclass, jstring, jfloat, jint, jlongArray);
 
+/*
+ * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
+ * Method:    XGBoosterSetStrFeatureInfo
+ * Signature: (JLjava/lang/String;[Ljava/lang/String;])I
+ */
+JNIEXPORT jint JNICALL
+Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterSetStrFeatureInfo
+  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
+
+/*
+ * Class:     ml_dmlc_xgboost4j_java_XGBoostJNI
+ * Method:    XGBoosterGetStrFeatureInfo
+ * Signature: (JLjava/lang/String;[Ljava/lang/String;])I
+ */
+JNIEXPORT jint JNICALL
+Java_ml_dmlc_xgboost4j_java_XGBoostJNI_XGBoosterGetStrFeatureInfo
+  (JNIEnv *, jclass, jlong, jstring, jobjectArray);
+
 #ifdef __cplusplus
 }
 #endif

jvm-packages/xgboost4j/src/test/java/ml/dmlc/xgboost4j/java/BoosterImplTest.java

@@ -16,10 +16,7 @@
 package ml.dmlc.xgboost4j.java;
 
 import java.io.*;
-import java.util.Arrays;
+import java.util.*;
-import java.util.HashMap;
-import java.util.LinkedHashMap;
-import java.util.Map;
 
 import junit.framework.TestCase;
 import org.junit.Test;
@@ -122,6 +119,40 @@ public class BoosterImplTest {
     TestCase.assertTrue(eval.eval(predicts2, testMat) < 0.1f);
   }
 
+  @Test
+  public void saveLoadModelWithFeaturesWithPath() throws XGBoostError, IOException {
+    DMatrix trainMat = new DMatrix(this.train_uri);
+    DMatrix testMat = new DMatrix(this.test_uri);
+    IEvaluation eval = new EvalError();
+
+    String[] featureNames = new String[126];
+    String[] featureTypes = new String[126];
+    for(int i = 0; i < 126; i++) {
+      featureNames[i] = "test_feature_name_" + i;
+      featureTypes[i] = "q";
+    }
+    trainMat.setFeatureNames(featureNames);
+    testMat.setFeatureNames(featureNames);
+    trainMat.setFeatureTypes(featureTypes);
+    testMat.setFeatureTypes(featureTypes);
+
+    Booster booster = trainBooster(trainMat, testMat);
+    // save and load, only json format save and load feature_name and feature_type
+    File temp = File.createTempFile("temp", ".json");
+    temp.deleteOnExit();
+    booster.saveModel(temp.getAbsolutePath());
+
+    String modelString = new String(booster.toByteArray("json"));
+    System.out.println(modelString);
+
+    Booster bst2 = XGBoost.loadModel(temp.getAbsolutePath());
+    assert (Arrays.equals(bst2.toByteArray("ubj"), booster.toByteArray("ubj")));
+    assert (Arrays.equals(bst2.toByteArray("json"), booster.toByteArray("json")));
+    assert (Arrays.equals(bst2.toByteArray("deprecated"), booster.toByteArray("deprecated")));
+    float[][] predicts2 = bst2.predict(testMat, true, 0);
+    TestCase.assertTrue(eval.eval(predicts2, testMat) < 0.1f);
+  }
+
   @Test
   public void saveLoadModelWithStream() throws XGBoostError, IOException {
     DMatrix trainMat = new DMatrix(this.train_uri);