rm WatchList class, take Iterable<Entry<String, DMatrix>> as eval param, change Params to Iterable<Entry<String, Object>>

java/doc/xgboost4j.md

@@ -73,14 +73,11 @@ dmat.setWeight(weights);
 ```
 
 #### Setting Parameters
-* A util class ```Params``` in xgboost4j is used to handle parameters.
+* in xgboost4j any ```Iterable<Entry<String, Object>>``` object could be used as parameters.
-* To import ```Params``` :
+
+* to set parameters, for non-multiple value params, you can simply use entrySet of an Map:
 ```java
-import org.dmlc.xgboost4j.util.Params;
+Map<String, Object> paramMap = new HashMap<>() {
-```
-* to set parameters :
-```java
-Params params = new Params() {
   {
     put("eta", 1.0);
     put("max_depth", 2);
@@ -89,18 +86,17 @@ Params params = new Params() {
     put("eval_metric", "logloss");
   }
 };
+Iterable<Entry<String, Object>> params = paramMap.entrySet();
 ```
-* Multiple values with same param key is handled naturally in ```Params```, e.g. :
+* for the situation that multiple values with same param key, List<Entry<String, Object>> would be a good choice, e.g. :
 ```java
-Params params = new Params() {
+List<Entry<String, Object>> params = new ArrayList<Entry<String, Object>>() {
-  {
+    {
-    put("eta", 1.0);
+        add(new SimpleEntry<String, Object>("eta", 1.0));
-    put("max_depth", 2);
+        add(new SimpleEntry<String, Object>("max_depth", 2.0));
-    put("silent", 1);
+        add(new SimpleEntry<String, Object>("silent", 1));
-    put("objective", "binary:logistic");
+        add(new SimpleEntry<String, Object>("objective", "binary:logistic"));
-    put("eval_metric", "logloss");
+    }
-    put("eval_metric", "error");
-  }
 };
 ```
 
@@ -110,7 +106,6 @@ With parameters and data, you are able to train a booster model.
 ```java
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
 ```
 
 * Training
@@ -118,9 +113,10 @@ import org.dmlc.xgboost4j.util.WatchList;
 DMatrix trainMat = new DMatrix("train.svm.txt");
 DMatrix validMat = new DMatrix("valid.svm.txt");
 //specifiy a watchList to see the performance
-WatchList watchs = new WatchList();
+//any Iterable<Entry<String, DMatrix>> object could be used as watchList
-watchs.put("train", trainMat);
+List<Entry<String, DMatrix>> watchs =  new ArrayList<>();
-watchs.put("test", testMat);
+watchs.add(new SimpleEntry<>("train", trainMat));
+watchs.add(new SimpleEntry<>("test", testMat));
 int round = 2;
 Booster booster = Trainer.train(params, trainMat, round, watchs, null, null);
 ```

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BasicWalkThrough.java

@@ -18,13 +18,19 @@ package org.dmlc.xgboost4j.demo;
 import java.io.File;
 import java.io.IOException;
 import java.io.UnsupportedEncodingException;
+import java.util.AbstractMap;
+import java.util.AbstractMap.SimpleEntry;
+import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Map.Entry;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.demo.util.DataLoader;
-import org.dmlc.xgboost4j.util.Params;
+import org.dmlc.xgboost4j.demo.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * a simple example of java wrapper for xgboost
@@ -51,8 +57,32 @@ public class BasicWalkThrough {
         DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
         DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
         
+        
         //specify parameters
-        Params param = new Params() {
+        //note: any Iterable<Entry<String, Object>> object would be used as paramters
+        //e.g.
+        //        Map<String, Object> paramMap = new HashMap<String, Object>() {
+        //            {
+        //                put("eta", 1.0);
+        //                put("max_depth", 2);
+        //                put("silent", 1);
+        //                put("objective", "binary:logistic");
+        //            }
+        //        };
+        //        Iterable<Entry<String, Object>> param = paramMap.entrySet();
+        
+        //or
+        //        List<Entry<String, Object>> param = new ArrayList<Entry<String, Object>>() {
+        //            {
+        //                add(new SimpleEntry<String, Object>("eta", 1.0));
+        //                add(new SimpleEntry<String, Object>("max_depth", 2.0));
+        //                add(new SimpleEntry<String, Object>("silent", 1));
+        //                add(new SimpleEntry<String, Object>("objective", "binary:logistic"));
+        //            }
+        //        };
+        
+        //we use a util class Params to handle parameters as example
+        Iterable<Entry<String, Object>> param = new Params() {
             {
                 put("eta", 1.0);
                 put("max_depth", 2);
@@ -61,10 +91,21 @@ public class BasicWalkThrough {
             }
         };
         
-        //specify watchList
+        
-        WatchList watchs = new WatchList();
+        
-        watchs.put("train", trainMat);
+        //specify watchList to set evaluation dmats
-        watchs.put("test", testMat);
+        //note: any Iterable<Entry<String, DMatrix>> object would be used as watchList
+        //e.g.
+        //an entrySet of Map is good
+        //        Map<String, DMatrix> watchMap = new HashMap<>();
+        //        watchMap.put("train", trainMat);
+        //        watchMap.put("test", testMat);
+        //        Iterable<Entry<String, DMatrix>> watchs = watchMap.entrySet();
+       
+        //we use a List of Entry<String, DMatrix> WatchList as example
+        List<Entry<String, DMatrix>> watchs =  new ArrayList<>();
+        watchs.add(new SimpleEntry<>("train", trainMat));
+        watchs.add(new SimpleEntry<>("test", testMat));
         
         //set round
         int round = 2;
@@ -110,9 +151,9 @@ public class BasicWalkThrough {
         trainMat2.setLabel(spData.labels);
         
         //specify watchList
-        WatchList watchs2 = new WatchList();
+        List<Entry<String, DMatrix>> watchs2 =  new ArrayList<>();
-        watchs2.put("train", trainMat2);
+        watchs2.add(new SimpleEntry<>("train", trainMat2));
-        watchs2.put("test", testMat);
+        watchs2.add(new SimpleEntry<>("test", testMat2));
         Booster booster3 = Trainer.train(param, trainMat2, round, watchs2, null, null);
         float[][] predicts3 = booster3.predict(testMat2);
         

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BoostFromPrediction.java

@@ -15,11 +15,14 @@
  */
 package org.dmlc.xgboost4j.demo;
 
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
-import org.dmlc.xgboost4j.util.Params;
+import org.dmlc.xgboost4j.demo.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * example for start from a initial base prediction
@@ -43,10 +46,10 @@ public class BoostFromPrediction {
             }
         };
         
-         //specify watchList
+        //specify watchList
-        WatchList watchs = new WatchList();
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
-        watchs.put("train", trainMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
-        watchs.put("test", testMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
         
         //train xgboost for 1 round
         Booster booster = Trainer.train(param, trainMat, 1, watchs, null, null);

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CrossValidation.java

@@ -18,7 +18,7 @@ package org.dmlc.xgboost4j.demo;
 import java.io.IOException;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.Params;
+import org.dmlc.xgboost4j.demo.util.Params;
 
 /**
  * an example of cross validation

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CustomObjective.java

@@ -15,15 +15,16 @@
  */
 package org.dmlc.xgboost4j.demo;
 
+import java.util.AbstractMap;
 import java.util.ArrayList;
 import java.util.List;
+import java.util.Map;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.IEvaluation;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.IObjective;
-import org.dmlc.xgboost4j.util.Params;
+import org.dmlc.xgboost4j.demo.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * an example user define objective and eval
@@ -140,9 +141,9 @@ public class CustomObjective {
         int round = 2;
         
         //specify watchList
-        WatchList watchs = new WatchList();
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
-        watchs.put("train", trainMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
-        watchs.put("test", testMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
         
         //user define obj and eval
         IObjective obj = new LogRegObj();

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/ExternalMemory.java

@@ -15,11 +15,14 @@
  */
 package org.dmlc.xgboost4j.demo;
 
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
-import org.dmlc.xgboost4j.util.Params;
+import org.dmlc.xgboost4j.demo.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * simple example for using external memory version
@@ -48,9 +51,9 @@ public class ExternalMemory {
         //param.put("nthread", num_real_cpu);
         
         //specify watchList
-        WatchList watchs = new WatchList();
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
-        watchs.put("train", trainMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
-        watchs.put("test", testMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
         
         //set round
         int round = 2;

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/GeneralizedLinearModel.java

@@ -15,12 +15,15 @@
  */
 package org.dmlc.xgboost4j.demo;
 
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
 import org.dmlc.xgboost4j.demo.util.CustomEval;
-import org.dmlc.xgboost4j.util.Params;
+import org.dmlc.xgboost4j.demo.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
 
 /**
  * this is an example of fit generalized linear model in xgboost
@@ -54,9 +57,9 @@ public class GeneralizedLinearModel {
         
         
         //specify watchList
-        WatchList watchs = new WatchList();
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
-        watchs.put("train", trainMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
-        watchs.put("test", testMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
         
         //train a booster
         int round = 4;

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictFirstNtree.java

@@ -15,13 +15,16 @@
  */
 package org.dmlc.xgboost4j.demo;
 
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Map;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
-import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
 
 import org.dmlc.xgboost4j.demo.util.CustomEval;
-import org.dmlc.xgboost4j.util.WatchList;
+import org.dmlc.xgboost4j.demo.util.Params;
 
 /**
  * predict first ntree
@@ -44,9 +47,9 @@ public class PredictFirstNtree {
         };
         
         //specify watchList
-        WatchList watchs = new WatchList();
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
-        watchs.put("train", trainMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
-        watchs.put("test", testMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
         
         //train a booster
         int round = 3;

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictLeafIndices.java

@@ -15,12 +15,15 @@
  */
 package org.dmlc.xgboost4j.demo;
 
+import java.util.AbstractMap;
+import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.List;
+import java.util.Map;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
-import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.util.Trainer;
-import org.dmlc.xgboost4j.util.WatchList;
+import org.dmlc.xgboost4j.demo.util.Params;
 
 /**
  * predict leaf indices
@@ -43,9 +46,9 @@ public class PredictLeafIndices {
         };
         
         //specify watchList
-        WatchList watchs = new WatchList();
+        List<Map.Entry<String, DMatrix>> watchs =  new ArrayList<>();
-        watchs.put("train", trainMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("train", trainMat));
-        watchs.put("test", testMat);
+        watchs.add(new AbstractMap.SimpleEntry<>("test", testMat));
         
         //train a booster
         int round = 3;

java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/util/Params.java

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

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/Booster.java

@@ -25,11 +25,11 @@ import java.io.UnsupportedEncodingException;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
+import java.util.Map.Entry;
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
 
 import org.dmlc.xgboost4j.util.Initializer;
-import org.dmlc.xgboost4j.util.Params;
 import org.dmlc.xgboost4j.wrapper.XgboostJNI;
 
 
@@ -58,7 +58,7 @@ public final class Booster {
      * @param params parameters
      * @param dMatrixs DMatrix array
      */
-    public Booster(Params params, DMatrix[] dMatrixs) {
+    public Booster(Iterable<Entry<String, Object>> params, DMatrix[] dMatrixs) {
         init(dMatrixs);
         setParam("seed","0");
         setParams(params);
@@ -71,7 +71,7 @@ public final class Booster {
      * @param params parameters
      * @param modelPath booster modelPath (model generated by booster.saveModel)
      */
-    public Booster(Params params, String modelPath) {
+    public Booster(Iterable<Entry<String, Object>> params, String modelPath) {
         handle = XgboostJNI.XGBoosterCreate(new long[] {});
         loadModel(modelPath);
         setParam("seed","0");
@@ -102,7 +102,7 @@ public final class Booster {
      * set parameters
      * @param params parameters key-value map 
      */
-    public void setParams(Params params) {
+    public void setParams(Iterable<Entry<String, Object>> params) {
         if(params!=null) {
             for(Map.Entry<String, Object> entry : params) {
                 setParam(entry.getKey(), entry.getValue().toString());

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/CVPack.java

@@ -15,6 +15,7 @@
  */
 package org.dmlc.xgboost4j.util;
 
+import java.util.Map;
 import org.dmlc.xgboost4j.IEvaluation;
 import org.dmlc.xgboost4j.Booster;
 import org.dmlc.xgboost4j.DMatrix;
@@ -37,7 +38,7 @@ public class CVPack {
      * @param dtest test data
      * @param params parameters
      */
-    public CVPack(DMatrix dtrain, DMatrix dtest, Params params) {
+    public CVPack(DMatrix dtrain, DMatrix dtest, Iterable<Map.Entry<String, Object>> params) {
         dmats = new DMatrix[] {dtrain, dtest};
         booster = new Booster(params, dmats);
         names = new String[] {"train", "test"};

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/Trainer.java

@@ -46,21 +46,23 @@ public class Trainer {
      * @param eval customized evaluation (set to null if not used)
      * @return trained booster
      */
-    public static Booster train(Params params, DMatrix dtrain, int round, 
+    public static Booster train(Iterable<Entry<String, Object>> params, DMatrix dtrain, int round, 
-            WatchList watchs, IObjective obj, IEvaluation eval) {
+            Iterable<Entry<String, DMatrix>> watchs, IObjective obj, IEvaluation eval) {
         
         //collect eval matrixs
-        int len = watchs.size();
+        String[] evalNames;
-        int i = 0;
+        DMatrix[] evalMats;
-        String[] evalNames = new String[len];
+        List<String> names = new ArrayList<>();
-        DMatrix[] evalMats = new DMatrix[len];
+        List<DMatrix> mats = new ArrayList<>();
         
         for(Entry<String, DMatrix> evalEntry : watchs) {
-            evalNames[i] = evalEntry.getKey();
+            names.add(evalEntry.getKey());
-            evalMats[i] = evalEntry.getValue();
+            mats.add(evalEntry.getValue());
-            i++;
         }
         
+        evalNames = names.toArray(new String[names.size()]);
+        evalMats = mats.toArray(new DMatrix[mats.size()]);
+        
         //collect all data matrixs
         DMatrix[] allMats;
         if(evalMats!=null && evalMats.length>0) {
@@ -110,7 +112,7 @@ public class Trainer {
      * @param eval customized evaluation (set to null if not used)
      * @return evaluation history
      */
-    public static String[] crossValiation(Params params, DMatrix data, int round, int nfold, String[] metrics, IObjective obj, IEvaluation eval) {
+    public static String[] crossValiation(Iterable<Entry<String, Object>> params, DMatrix data, int round, int nfold, String[] metrics, IObjective obj, IEvaluation eval) {
         CVPack[] cvPacks = makeNFold(data, nfold, params, metrics);
         String[] evalHist = new String[round];
         String[] results = new String[cvPacks.length];
@@ -147,7 +149,7 @@ public class Trainer {
      * @param evalMetrics Evaluation metrics
      * @return CV package array
      */
-    public static CVPack[] makeNFold(DMatrix data, int nfold, Params params, String[] evalMetrics) {
+    public static CVPack[] makeNFold(DMatrix data, int nfold, Iterable<Entry<String, Object>> params, String[] evalMetrics) {
         List<Integer> samples = genRandPermutationNums(0, (int) data.rowNum());
         int step = samples.size()/nfold;
         int[] testSlice = new int[step];

java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/WatchList.java

@@ -1,49 +0,0 @@
-/*
- Copyright (c) 2014 by Contributors 
-
- Licensed 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 org.dmlc.xgboost4j.util;
-
-import java.util.AbstractMap;
-import java.util.ArrayList;
-import java.util.Iterator;
-import java.util.List;
-import java.util.Map.Entry;
-import org.dmlc.xgboost4j.DMatrix;
-
-/**
- * class to handle evaluation dmatrix
- * @author hzx
- */
-public class WatchList implements Iterable<Entry<String, DMatrix> >{
-    List<Entry<String, DMatrix>> watchList = new ArrayList<>();
-    
-    /**
-     * put eval dmatrix and it's name 
-     * @param name
-     * @param dmat 
-     */
-    public void put(String name, DMatrix dmat) {
-        watchList.add(new AbstractMap.SimpleEntry<>(name, dmat));
-    }
-    
-    public int size() {
-        return watchList.size();
-    }
-
-    @Override
-    public Iterator<Entry<String, DMatrix>> iterator() {
-        return watchList.iterator();
-    }    
-}