[jvm-packages] Create demo and test for xgboost4j early stopping. (#7252)

jvm-packages/xgboost4j-example/README.md

@@ -10,6 +10,7 @@ XGBoost4J Code Examples
 * [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)
+* [Early Stopping](src/main/java/ml/dmlc/xgboost4j/java/example/EarlyStopping.java)
 
 ## Scala API
 

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

@@ -1,5 +1,5 @@
 /*
- Copyright (c) 2014 by Contributors
+ Copyright (c) 2014-2021 by Contributors
 
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
@@ -115,7 +115,7 @@ public class BasicWalkThrough {
     DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
 
     DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
-            DMatrix.SparseType.CSR);
+                                    DMatrix.SparseType.CSR, 127);
     trainMat2.setLabel(spData.labels);
 
     //specify watchList

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

@@ -0,0 +1,67 @@
+/*
+ Copyright (c) 2021 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 ml.dmlc.xgboost4j.java.example;
+
+import java.io.IOException;
+import java.util.HashMap;
+import java.util.LinkedHashMap;
+import java.util.Map;
+
+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.example.util.DataLoader;
+
+public class EarlyStopping {
+  public static void main(String[] args) throws IOException, XGBoostError {
+    DataLoader.CSRSparseData trainCSR =
+        DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
+    DataLoader.CSRSparseData testCSR =
+        DataLoader.loadSVMFile("../../demo/data/agaricus.txt.test");
+
+    Map<String, Object> paramMap = new HashMap<String, Object>() {
+      {
+        put("max_depth", 3);
+        put("objective", "binary:logistic");
+        put("maximize_evaluation_metrics", "false");
+      }
+    };
+
+    DMatrix trainXy = new DMatrix(trainCSR.rowHeaders, trainCSR.colIndex, trainCSR.data,
+                                  DMatrix.SparseType.CSR, 127);
+    trainXy.setLabel(trainCSR.labels);
+    DMatrix testXy = new DMatrix(testCSR.rowHeaders, testCSR.colIndex, testCSR.data,
+                                 DMatrix.SparseType.CSR, 127);
+    testXy.setLabel(testCSR.labels);
+
+    int nRounds = 128;
+    int nEarlyStoppingRounds = 4;
+
+    Map<String, DMatrix> watches = new LinkedHashMap<>();
+    watches.put("training", trainXy);
+    watches.put("test", testXy);
+
+    float[][] metrics = new float[watches.size()][nRounds];
+    Booster booster = XGBoost.train(trainXy, paramMap, nRounds,
+                                    watches, metrics, null, null, nEarlyStoppingRounds);
+
+    int bestIter = Integer.valueOf(booster.getAttr("best_iteration"));
+    float bestScore = Float.valueOf(booster.getAttr("best_score"));
+
+    System.out.printf("Best iter: %d, Best score: %f\n", bestIter, bestScore);
+  }
+}

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

@@ -16,8 +16,6 @@
 package ml.dmlc.xgboost4j.java;
 
 import java.io.*;
-import java.nio.file.Files;
-import java.nio.file.Path;
 import java.util.Arrays;
 import java.util.HashMap;
 import java.util.LinkedHashMap;
@@ -347,6 +345,34 @@ public class BoosterImplTest {
     }
   }
 
+  @Test
+  public void testEarlyStoppingAttributes() throws XGBoostError, IOException {
+    DMatrix trainMat = new DMatrix(this.train_uri);
+    DMatrix testMat = new DMatrix(this.test_uri);
+    Map<String, Object> paramMap = new HashMap<String, Object>() {
+      {
+        put("max_depth", 3);
+        put("objective", "binary:logistic");
+        put("maximize_evaluation_metrics", "false");
+      }
+    };
+    Map<String, DMatrix> watches = new LinkedHashMap<>();
+    watches.put("training", trainMat);
+    watches.put("test", testMat);
+
+    int round = 30;
+    int earlyStoppingRound = 4;
+    float[][] metrics = new float[watches.size()][round];
+
+    Booster booster = XGBoost.train(trainMat, paramMap, round,
+				    watches, metrics, null, null, earlyStoppingRound);
+
+    int bestIter = Integer.valueOf(booster.getAttr("best_iteration"));
+    float bestScore = Float.valueOf(booster.getAttr("best_score"));
+    TestCase.assertEquals(bestIter, round - 1);
+    TestCase.assertEquals(bestScore, metrics[watches.size() - 1][round - 1]);
+  }
+
   private void testWithQuantileHisto(DMatrix trainingSet, Map<String, DMatrix> watches, int round,
                                       Map<String, Object> paramMap, float threshold) throws XGBoostError {
     float[][] metrics = new float[watches.size()][round];