[DOC-JVM] Refactor JVM docs

NEWS.md

@@ -27,6 +27,8 @@ This file records the changes in xgboost library in reverse chronological order.
   - This could fix some of the previous problem which runs xgboost on multiple threads.
 * JVM Package
   - Enable xgboost4j for java and scala
+  - XGBoost distributed now runs on Flink and Spark.
+
 
 ## v0.47 (2016.01.14)
 

doc/jvm/index.md

@@ -0,0 +1,20 @@
+XGBoost JVM Package
+===================
+[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
+[![GitHub license](http://dmlc.github.io/img/apache2.svg)](../LICENSE)
+
+You have find XGBoost JVM Package!
+
+Installation
+------------
+To build XGBoost4J contains two steps.
+- First type the following command to build JNI library.
+```bash
+./create_jni.sh
+```
+- Then package the libary. you can run `mvn package` in xgboost4j folder or just use IDE(eclipse/netbeans) to open this maven project and build.
+
+Contents
+--------
+* [Java Overview Tutorial](java_intro.md)
+* [Code Examples](https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example)

doc/jvm/java_intro.md

@@ -1,23 +1,8 @@
-xgboost4j : java wrapper for xgboost
+XGBoost4J Java API
-====
+==================
+This tutorial introduces
 
-This page will introduce xgboost4j, the java wrapper for xgboost, including:
+## Data Interface
-* [Building](#build-xgboost4j)
-* [Data Interface](#data-interface)
-* [Setting Parameters](#setting-parameters)
-* [Train Model](#training-model)
-* [Prediction](#prediction)
-
-=
-#### Build xgboost4j
-* Build native library  
-first make sure you have installed jdk and `JAVA_HOME` has been setted properly, then simply run `./create_wrap.sh`.
-
-* Package xgboost4j  
-to package xgboost4j, you can run `mvn package` in xgboost4j folder or just use IDE(eclipse/netbeans) to open this maven project and build.
-
-=
-#### Data Interface
 Like the xgboost python module, xgboost4j use ```DMatrix``` to handle data, libsvm txt format file, sparse matrix in CSR/CSC format, and dense matrix is supported.
 
 * To import ```DMatrix``` :
@@ -72,7 +57,7 @@ float[] weights = new float[] {1f,2f,1f};
 dmat.setWeight(weights);
 ```
 
-#### Setting Parameters
+## Setting Parameters
 * in xgboost4j any ```Iterable<Entry<String, Object>>``` object could be used as parameters.
 
 * to set parameters, for non-multiple value params, you can simply use entrySet of an Map:
@@ -100,7 +85,7 @@ List<Entry<String, Object>> params = new ArrayList<Entry<String, Object>>() {
 };
 ```
 
-#### Training Model
+## Training Model
 With parameters and data, you are able to train a booster model.
 * Import ```Trainer``` and ```Booster``` :
 ```java
@@ -145,7 +130,7 @@ Params param = new Params() {
 Booster booster = new Booster(param, "model.bin");
 ```
 
-####Prediction
+## Prediction
 after training and loading a model, you use it to predict other data, the predict results will be a two-dimension float array (nsample, nclass) ,for predict leaf, it would be (nsample, nclass*ntrees)
 ```java
 DMatrix dtest = new DMatrix("test.svm.txt");

jvm-packages/README.md

@@ -1,33 +1,73 @@
-# xgboost4j
+# XGBoost4J: Distributed XGBoost for Scala/Java
-this is a java wrapper for xgboost 
+[![Build Status](https://travis-ci.org/dmlc/xgboost.svg?branch=master)](https://travis-ci.org/dmlc/xgboost)
+[![GitHub license](http://dmlc.github.io/img/apache2.svg)](../LICENSE)
 
-the structure of this wrapper is almost the same as the official python wrapper.
+[Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) |
+[Resources](../demo/README.md) |
+[Release Notes](../NEWS.md)
 
-core of this wrapper is two classes:
+XGBoost4J is the JVM package of xgboost. It brings all the optimizations
+and power xgboost into JVM ecosystem.
 
-* DMatrix: for handling data
+- Train XGBoost models on scala and java with easy customizations.
+- Run distributed xgboost natively on jvm frameworks such as Flink and Spark.
 
-* Booster: for train and predict
+You can find more about XGBoost on [Documentation](https://xgboost.readthedocs.org/en/latest/jvm/index.html) and [Resource Page](../demo/README.md).
 
-## usage:
+## Hello World
-  please refer to [xgboost4j.md](doc/xgboost4j.md) for more information.
+### XGBoost Scala
+```scala
+import ml.dmlc.xgboost4j.scala.DMatrix
+import ml.dmlc.xgboost4j.scala.XGBoost
 
-  besides, simple examples could be found in [xgboost4j-demo](xgboost4j-demo/README.md)
+object XGBoostScalaExample {
+  def main(args: Array[String]) {
+    // read trainining data, available at xgboost/demo/data
+    val trainData =
+      new DMatrix("/path/to/agaricus.txt.train")
+    // define parameters
+    val paramMap = List(
+      "eta" -> 0.1,
+      "max_depth" -> 2,
+      "objective" -> "binary:logistic").toMap
+    // number of iterations
+    val round = 2
+    // train the model
+    val model = XGBoost.train(paramMap, trainData, round)
+    // run prediction
+    val predTrain = model.predict(trainData)
+    // save model to the file.
+    model.saveModel("/local/path/to/model")
+  }
+}
+```
 
+### XGBoost Flink
+```scala
+import ml.dmlc.xgboost4j.scala.flink.XGBoost
+import org.apache.flink.api.scala._
+import org.apache.flink.api.scala.ExecutionEnvironment
+import org.apache.flink.ml.MLUtils
 
-## build native library
+object DistTrainWithFlink {
+  def main(args: Array[String]) {
+    val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
+    // read trainining data
+    val trainData =
+      MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
+    // define parameters
+    val paramMap = List(
+      "eta" -> 0.1,
+      "max_depth" -> 2,
+      "objective" -> "binary:logistic").toMap
+    // number of iterations
+    val round = 2
+    // train the model
+    val model = XGBoost.train(paramMap, trainData, round)
+    val predTrain = model.predict(trainData.map{x => x.vector})
+    model.saveModelToHadoop("file:///path/to/xgboost.model")
+  }
+}
+```
 
-for windows: open the xgboost.sln in "../windows" folder, you will found the xgboost4j project, you should do the following steps to build wrapper library:
+### XGBoost Spark
- * Select x64/win32 and Release in build
- * (if you have setted `JAVA_HOME` properly in windows environment variables, escape this step) right click on xgboost4j project -> choose "Properties" -> click on "C/C++" in the window -> change the "Additional Include Directories" to fit your jdk install path.
- * rebuild all
- * double click "create_wrap.bat" to set library to proper place
-
-for linux: 
- * make sure you have installed jdk and `JAVA_HOME` has been setted properly
- * run "create_wrap.sh"
-
-for osx:
- * make sure you have installed jdk
- * for single thread xgboost, simply run "create_wrap.sh"
- * for build with openMP, please refer to [build.md](../doc/build.md) to get openmp supported compiler first, and change the line "dis_omp=1" to "dis_omp=0" in "create_wrap.sh", then run "create_wrap.sh"

jvm-packages/pom.xml

@@ -5,8 +5,8 @@
     <modelVersion>4.0.0</modelVersion>
 
     <groupId>ml.dmlc</groupId>
-    <artifactId>xgboostjvm</artifactId>
+    <artifactId>xgboost-jvm</artifactId>
-    <version>0.1</version>
+    <version>0.5</version>
     <packaging>pom</packaging>
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
@@ -19,7 +19,7 @@
     </properties>
     <modules>
         <module>xgboost4j</module>
-        <module>xgboost4j-demo</module>
+        <module>xgboost4j-example</module>
         <module>xgboost4j-spark</module>
         <module>xgboost4j-flink</module>
     </modules>

jvm-packages/xgboost4j-demo/README.md

@@ -1,10 +0,0 @@
-xgboost4j examples
-====
-* [Basic walkthrough of wrappers](src/main/java/org/dmlc/xgboost4j/demo/BasicWalkThrough.java) 
-* [Cutomize loss function, and evaluation metric](src/main/java/org/dmlc/xgboost4j/demo/CustomObjective.java)
-* [Boosting from existing prediction](src/main/java/org/dmlc/xgboost4j/demo/BoostFromPrediction.java)
-* [Predicting using first n trees](src/main/java/org/dmlc/xgboost4j/demo/PredictFirstNtree.java)
-* [Generalized Linear Model](src/main/java/org/dmlc/xgboost4j/demo/GeneralizedLinearModel.java)
-* [Cross validation](src/main/java/org/dmlc/xgboost4j/demo/CrossValidation.java)
-* [Predicting leaf indices](src/main/java/org/dmlc/xgboost4j/demo/PredictLeafIndices.java)
-* [External Memory](src/main/java/org/dmlc/xgboost4j/demo/ExternalMemory.java)

jvm-packages/xgboost4j-example/README.md

@@ -0,0 +1,18 @@
+XGBoost4J Code Examples
+=======================
+
+## Java API
+* [Basic walkthrough of wrappers](src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java)
+* [Cutomize loss function, and evaluation metric](src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java)
+* [Boosting from existing prediction](src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java)
+* [Predicting using first n trees](src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java)
+* [Generalized Linear Model](src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java)
+* [Cross validation](src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java)
+* [Predicting leaf indices](src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java)
+* [External Memory](src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java)
+
+## Spark API
+* [Distributed Training with Spark](src/main/scala/ml/dmlc/xgboost4j/scala/spark/example/DistTrainWithSpark.scala)
+
+## Flink API
+* [Distributed Training with Flink](src/main/scala/ml/dmlc/xgboost4j/scala/flink/example/DistTrainWithFlink.scala)

jvm-packages/xgboost4j-example/pom.xml

@@ -5,11 +5,11 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboostjvm</artifactId>
+        <artifactId>xgboost-jvm</artifactId>
-        <version>0.1</version>
+        <version>0.5</version>
     </parent>
-    <artifactId>xgboost4j-demo</artifactId>
+    <artifactId>xgboost4j-example</artifactId>
-    <version>0.1</version>
+    <version>0.5</version>
     <packaging>jar</packaging>
     <build>
         <plugins>
@@ -26,7 +26,12 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j-spark</artifactId>
-            <version>0.1</version>
+            <version>0.5</version>
+        </dependency>
+        <dependency>
+            <groupId>ml.dmlc</groupId>
+            <artifactId>xgboost4j-flink</artifactId>
+            <version>0.5</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.io.File;
 import java.io.IOException;
@@ -24,7 +24,7 @@ import ml.dmlc.xgboost4j.java.Booster;
 import ml.dmlc.xgboost4j.java.DMatrix;
 import ml.dmlc.xgboost4j.java.XGBoost;
 import ml.dmlc.xgboost4j.java.XGBoostError;
-import ml.dmlc.xgboost4j.java.demo.util.DataLoader;
+import ml.dmlc.xgboost4j.java.example.util.DataLoader;
 
 /**
  * a simple example of java wrapper for xgboost

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.util.HashMap;
 

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.io.IOException;
 import java.util.HashMap;

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.util.ArrayList;
 import java.util.HashMap;

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.util.HashMap;
 

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.util.HashMap;
 
@@ -21,7 +21,7 @@ import ml.dmlc.xgboost4j.java.Booster;
 import ml.dmlc.xgboost4j.java.DMatrix;
 import ml.dmlc.xgboost4j.java.XGBoost;
 import ml.dmlc.xgboost4j.java.XGBoostError;
-import ml.dmlc.xgboost4j.java.demo.util.CustomEval;
+import ml.dmlc.xgboost4j.java.example.util.CustomEval;
 
 /**
  * this is an example of fit generalized linear model in xgboost

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.util.HashMap;
 
@@ -21,7 +21,7 @@ import ml.dmlc.xgboost4j.java.Booster;
 import ml.dmlc.xgboost4j.java.DMatrix;
 import ml.dmlc.xgboost4j.java.XGBoost;
 import ml.dmlc.xgboost4j.java.XGBoostError;
-import ml.dmlc.xgboost4j.java.demo.util.CustomEval;
+import ml.dmlc.xgboost4j.java.example.util.CustomEval;
 
 /**
  * predict first ntree

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo;
+package ml.dmlc.xgboost4j.java.example;
 
 import java.util.Arrays;
 import java.util.HashMap;

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/CustomEval.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo.util;
+package ml.dmlc.xgboost4j.java.example.util;
 
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;

jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/util/DataLoader.java

@@ -13,7 +13,7 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
-package ml.dmlc.xgboost4j.java.demo.util;
+package ml.dmlc.xgboost4j.java.example.util;
 
 import java.io.*;
 import java.util.ArrayList;

jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/flink/example/DistTrainWithFlink.scala

@@ -13,33 +13,29 @@
  See the License for the specific language governing permissions and
  limitations under the License.
  */
+package ml.dmlc.xgboost4j.scala.flink.example
 
-package ml.dmlc.xgboost4j.flink
+import ml.dmlc.xgboost4j.scala.flink.XGBoost
-
-import org.apache.commons.logging.Log
-import org.apache.commons.logging.LogFactory
-import org.apache.flink.api.common.functions.RichMapPartitionFunction
 import org.apache.flink.api.scala._
-import org.apache.flink.api.scala.DataSet
 import org.apache.flink.api.scala.ExecutionEnvironment
-import org.apache.flink.ml.common.LabeledVector
 import org.apache.flink.ml.MLUtils
-import org.apache.flink.util.Collector
 
-
+object DistTrainWithFlink {
-
-object Test {
-  val log = LogFactory.getLog(this.getClass)
   def main(args: Array[String]) {
     val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
-    val data = MLUtils.readLibSVM(env, "/home/tqchen/github/xgboost/demo/data/agaricus.txt.train")
+    // read trainining data
-    val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "1",
+    val trainData =
+      MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
+    // define parameters
+    val paramMap = List(
+      "eta" -> 0.1,
+      "max_depth" -> 2,
       "objective" -> "binary:logistic").toMap
+    // number of iterations
     val round = 2
-    val model = XGBoost.train(paramMap, data, round)
+    // train the model
-
+    val model = XGBoost.train(paramMap, trainData, round)
-
+    val predTrain = model.predict(trainData.map{x => x.vector})
-    log.info(model)
+    model.saveModelToHadoop("file:///path/to/xgboost.model")
   }
 }
-

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

@@ -14,7 +14,7 @@
  limitations under the License.
  */
 
-package ml.dmlc.xgboost4j.scala.spark.demo
+package ml.dmlc.xgboost4j.scala.spark.example
 
 import java.io.File
 

jvm-packages/xgboost4j-flink/pom.xml

@@ -5,11 +5,11 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboostjvm</artifactId>
+        <artifactId>xgboost-jvm</artifactId>
-        <version>0.1</version>
+        <version>0.5</version>
     </parent>
     <artifactId>xgboost4j-flink</artifactId>
-    <version>0.1</version>
+    <version>0.5</version>
     <build>
         <plugins>
             <plugin>
@@ -26,7 +26,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j</artifactId>
-            <version>0.1</version>
+            <version>0.5</version>
         </dependency>
         <dependency>
             <groupId>org.apache.commons</groupId>

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

@@ -14,7 +14,8 @@
  limitations under the License.
  */
 
-package ml.dmlc.xgboost4j.flink
+package ml.dmlc.xgboost4j.scala.flink
+
 import scala.collection.JavaConverters.asScalaIteratorConverter;
 import ml.dmlc.xgboost4j.LabeledPoint
 import ml.dmlc.xgboost4j.java.{RabitTracker, Rabit}
@@ -35,7 +36,7 @@ object XGBoost {
     *
     * @param workerEnvs
     */
-  private class MapFunction(paramMap: Map[String, AnyRef],
+  private class MapFunction(paramMap: Map[String, Any],
                             round: Int,
                             workerEnvs: java.util.Map[String, String])
     extends RichMapPartitionFunction[LabeledVector, XGBoostModel] {
@@ -69,7 +70,7 @@ object XGBoost {
     * @param modelPath The path that is accessible by hadoop filesystem API.
     * @return The loaded model
     */
-  def loadModel(modelPath: String) : XGBoostModel = {
+  def loadModelFromHadoop(modelPath: String) : XGBoostModel = {
     new XGBoostModel(
       XGBoostScala.loadModel(
         FileSystem
@@ -84,7 +85,7 @@ object XGBoost {
     * @param dtrain The training data.
     * @param round Number of rounds to train.
     */
-  def train(params: Map[String, AnyRef],
+  def train(params: Map[String, Any],
             dtrain: DataSet[LabeledVector],
             round: Int): XGBoostModel = {
     val tracker = new RabitTracker(dtrain.getExecutionEnvironment.getParallelism)

jvm-packages/xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoostModel.scala

@@ -14,7 +14,7 @@
  limitations under the License.
  */
 
-package ml.dmlc.xgboost4j.flink
+package ml.dmlc.xgboost4j.scala.flink
 
 import ml.dmlc.xgboost4j.LabeledPoint
 import ml.dmlc.xgboost4j.scala.{DMatrix, Booster}
@@ -31,7 +31,7 @@ class XGBoostModel (booster: Booster) extends Serializable {
     *
     * @param modelPath The model path as in Hadoop path.
     */
-  def saveModel(modelPath: String): Unit = {
+  def saveModelToHadoop(modelPath: String): Unit = {
     booster.saveModel(FileSystem
       .get(new Configuration)
       .create(new Path(modelPath)))

jvm-packages/xgboost4j-spark/pom.xml

@@ -5,8 +5,8 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboostjvm</artifactId>
+        <artifactId>xgboost-jvm</artifactId>
-        <version>0.1</version>
+        <version>0.5</version>
     </parent>
     <artifactId>xgboost4j-spark</artifactId>
     <build>
@@ -24,7 +24,7 @@
         <dependency>
             <groupId>ml.dmlc</groupId>
             <artifactId>xgboost4j</artifactId>
-            <version>0.1</version>
+            <version>0.5</version>
         </dependency>
         <dependency>
             <groupId>org.apache.spark</groupId>

jvm-packages/xgboost4j/pom.xml

@@ -5,11 +5,11 @@
     <modelVersion>4.0.0</modelVersion>
     <parent>
         <groupId>ml.dmlc</groupId>
-        <artifactId>xgboostjvm</artifactId>
+        <artifactId>xgboost-jvm</artifactId>
-        <version>0.1</version>
+        <version>0.5</version>
     </parent>
     <artifactId>xgboost4j</artifactId>
-    <version>0.1</version>
+    <version>0.5</version>
     <packaging>jar</packaging>
     <build>
         <plugins>

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

@@ -39,14 +39,20 @@ object XGBoost {
     */
   @throws(classOf[XGBoostError])
   def train(
-      params: Map[String, AnyRef],
+      params: Map[String, Any],
       dtrain: DMatrix,
       round: Int,
       watches: Map[String, DMatrix] = Map[String, DMatrix](),
       obj: ObjectiveTrait = null,
       eval: EvalTrait = null): Booster = {
+
+
     val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
-    val xgboostInJava = JXGBoost.train(params.asJava, dtrain.jDMatrix, round, jWatches.asJava,
+    val xgboostInJava = JXGBoost.train(
+      params.map{
+        case (key: String, value) => (key, value.toString)
+      }.toMap[String, AnyRef].asJava,
+      dtrain.jDMatrix, round, jWatches.asJava,
       obj, eval)
     new Booster(xgboostInJava)
   }
@@ -65,14 +71,17 @@ object XGBoost {
     */
   @throws(classOf[XGBoostError])
   def crossValidation(
-      params: Map[String, AnyRef],
+      params: Map[String, Any],
       data: DMatrix,
       round: Int,
       nfold: Int = 5,
       metrics: Array[String] = null,
       obj: ObjectiveTrait = null,
       eval: EvalTrait = null): Array[String] = {
-    JXGBoost.crossValidation(params.asJava, data.jDMatrix, round, nfold, metrics, obj, eval)
+    JXGBoost.crossValidation(params.map{
+      case (key: String, value) => (key, value.toString)
+    }.toMap[String, AnyRef].asJava,
+      data.jDMatrix, round, nfold, metrics, obj, eval)
   }
 
   /**