[jvm-packages] Expose prediction feature contribution on the Java side (#2441)

* Exposed prediction feature contribution on the Java side

* was not supplying the newly added argument

* Exposed from Scala-side as well

* formatting (keep declaration in one line unless exceeding 100 chars)

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

@@ -131,7 +131,7 @@ public class Booster implements Serializable, KryoSerializable {
    * @throws XGBoostError native error
    */
   public void update(DMatrix dtrain, IObjective obj) throws XGBoostError {
-    float[][] predicts = this.predict(dtrain, true, 0, false);
+    float[][] predicts = this.predict(dtrain, true, 0, false, false);
     List<float[]> gradients = obj.getGradient(predicts, dtrain);
     boost(dtrain, gradients.get(0), gradients.get(1));
   }
@@ -219,12 +219,14 @@ public class Booster implements Serializable, KryoSerializable {
    * @param outputMargin output margin
    * @param treeLimit    limit number of trees, 0 means all trees.
    * @param predLeaf     prediction minimum to keep leafs
+   * @param predContribs prediction feature contributions
    * @return predict results
    */
   private synchronized float[][] predict(DMatrix data,
                                          boolean outputMargin,
                                          int treeLimit,
-                                         boolean predLeaf) throws XGBoostError {
+                                         boolean predLeaf,
+                                         boolean predContribs) throws XGBoostError {
     int optionMask = 0;
     if (outputMargin) {
       optionMask = 1;
@@ -232,6 +234,9 @@ public class Booster implements Serializable, KryoSerializable {
     if (predLeaf) {
       optionMask = 2;
     }
+    if (predContribs) {
+      optionMask = 4;
+    }
     float[][] rawPredicts = new float[1][];
     XGBoostJNI.checkCall(XGBoostJNI.XGBoosterPredict(handle, data.getHandle(), optionMask,
             treeLimit, rawPredicts));
@@ -256,7 +261,19 @@ public class Booster implements Serializable, KryoSerializable {
    * @throws XGBoostError
    */
   public float[][] predictLeaf(DMatrix data, int treeLimit) throws XGBoostError {
-    return this.predict(data, false, treeLimit, true);
+    return this.predict(data, false, treeLimit, true, false);
+  }
+
+  /**
+   * Output feature contributions toward predictions of given data
+   *
+   * @param data The input data.
+   * @param treeLimit Number of trees to include, 0 means all trees.
+   * @return The feature contributions and bias.
+   * @throws XGBoostError
+   */
+  public float[][] predictContrib(DMatrix data, int treeLimit) throws XGBoostError {
+    return this.predict(data, false, treeLimit, true, true);
   }
 
   /**
@@ -267,7 +284,7 @@ public class Booster implements Serializable, KryoSerializable {
    * @throws XGBoostError native error
    */
   public float[][] predict(DMatrix data) throws XGBoostError {
-    return this.predict(data, false, 0, false);
+    return this.predict(data, false, 0, false, false);
   }
 
   /**
@@ -278,7 +295,7 @@ public class Booster implements Serializable, KryoSerializable {
    * @return predict results
    */
   public float[][] predict(DMatrix data, boolean outputMargin) throws XGBoostError {
-    return this.predict(data, outputMargin, 0, false);
+    return this.predict(data, outputMargin, 0, false, false);
   }
 
   /**
@@ -290,7 +307,7 @@ public class Booster implements Serializable, KryoSerializable {
    * @return predict results
    */
   public float[][] predict(DMatrix data, boolean outputMargin, int treeLimit) throws XGBoostError {
-    return this.predict(data, outputMargin, treeLimit, false);
+    return this.predict(data, outputMargin, treeLimit, false, false);
   }
 
   /**

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

@@ -135,11 +135,23 @@ class Booster private[xgboost4j](private var booster: JBooster)
    * @throws XGBoostError native error
    */
   @throws(classOf[XGBoostError])
-  def predictLeaf(data: DMatrix, treeLimit: Int = 0)
+  def predictLeaf(data: DMatrix, treeLimit: Int = 0) : Array[Array[Float]] = {
-    : Array[Array[Float]] = {
     booster.predictLeaf(data.jDMatrix, treeLimit)
   }
 
+  /**
+    * Output feature contributions toward predictions of given data
+    *
+    * @param data      dmatrix storing the input
+    * @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
+    * @return The feature contributions and bias.
+    * @throws XGBoostError native error
+    */
+  @throws(classOf[XGBoostError])
+  def predictContrib(data: DMatrix, treeLimit: Int = 0) : Array[Array[Float]] = {
+    booster.predictContrib(data.jDMatrix, treeLimit)
+  }
+
   /**
    * save model to modelPath
    *