[jvm-packages] automatically set the max/min direction for best score (#9404)

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

@@ -23,7 +23,6 @@ import scala.util.Random
 import scala.collection.JavaConverters._
 
 import ml.dmlc.xgboost4j.java.{Communicator, IRabitTracker, XGBoostError, RabitTracker => PyRabitTracker}
-import ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams
 import ml.dmlc.xgboost4j.scala.ExternalCheckpointManager
 import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
 import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
@@ -55,9 +54,6 @@ object TrackerConf {
   def apply(): TrackerConf = TrackerConf(0L)
 }
 
-private[scala] case class XGBoostExecutionEarlyStoppingParams(numEarlyStoppingRounds: Int,
-                                                             maximizeEvalMetrics: Boolean)
-
 private[scala] case class XGBoostExecutionInputParams(trainTestRatio: Double, seed: Long)
 
 private[scala] case class XGBoostExecutionParams(
@@ -71,7 +67,7 @@ private[scala] case class XGBoostExecutionParams(
     trackerConf: TrackerConf,
     checkpointParam: Option[ExternalCheckpointParams],
     xgbInputParams: XGBoostExecutionInputParams,
-    earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
+    earlyStoppingRounds: Int,
     cacheTrainingSet: Boolean,
     device: Option[String],
     isLocal: Boolean,
@@ -146,16 +142,9 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
     val numEarlyStoppingRounds = overridedParams.getOrElse(
       "num_early_stopping_rounds", 0).asInstanceOf[Int]
     overridedParams += "num_early_stopping_rounds" -> numEarlyStoppingRounds
-    if (numEarlyStoppingRounds > 0 &&
-      !overridedParams.contains("maximize_evaluation_metrics")) {
-      if (overridedParams.getOrElse("custom_eval", null) != null) {
+    if (numEarlyStoppingRounds > 0 && overridedParams.getOrElse("custom_eval", null) != null) {
         throw new IllegalArgumentException("custom_eval does not support early stopping")
     }
-      val eval_metric = overridedParams("eval_metric").toString
-      val maximize = LearningTaskParams.evalMetricsToMaximize contains eval_metric
-      logger.info("parameter \"maximize_evaluation_metrics\" is set to " + maximize)
-      overridedParams += ("maximize_evaluation_metrics" -> maximize)
-    }
     overridedParams
   }
 
@@ -213,10 +202,6 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
 
     val earlyStoppingRounds = overridedParams.getOrElse(
       "num_early_stopping_rounds", 0).asInstanceOf[Int]
-    val maximizeEvalMetrics = overridedParams.getOrElse(
-      "maximize_evaluation_metrics", true).asInstanceOf[Boolean]
-    val xgbExecEarlyStoppingParams = XGBoostExecutionEarlyStoppingParams(earlyStoppingRounds,
-      maximizeEvalMetrics)
 
     val cacheTrainingSet = overridedParams.getOrElse("cache_training_set", false)
       .asInstanceOf[Boolean]
@@ -232,7 +217,7 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
       missing, allowNonZeroForMissing, trackerConf,
       checkpointParam,
       inputParams,
-      xgbExecEarlyStoppingParams,
+      earlyStoppingRounds,
       cacheTrainingSet,
       device,
       isLocal,
@@ -319,7 +304,7 @@ object XGBoost extends Serializable {
 
       watches = buildWatchesAndCheck(buildWatches)
 
-      val numEarlyStoppingRounds = xgbExecutionParam.earlyStoppingParams.numEarlyStoppingRounds
+      val numEarlyStoppingRounds = xgbExecutionParam.earlyStoppingRounds
       val metrics = Array.tabulate(watches.size)(_ => Array.ofDim[Float](numRounds))
       val externalCheckpointParams = xgbExecutionParam.checkpointParam
 

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

@@ -112,8 +112,4 @@ private[spark] object LearningTaskParams {
 
   val supportedObjectiveType = HashSet("regression", "classification")
 
-  val evalMetricsToMaximize = HashSet("auc", "aucpr", "ndcg", "map")
-
-  val evalMetricsToMinimize = HashSet("rmse", "rmsle", "mae", "mape", "logloss", "error", "merror",
-    "mlogloss", "gamma-deviance")
 }

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

@@ -17,6 +17,8 @@ package ml.dmlc.xgboost4j.java;
 
 import java.io.*;
 import java.util.*;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
 
 import org.apache.commons.logging.Log;
 import org.apache.commons.logging.LogFactory;
@@ -30,6 +32,11 @@ import org.apache.hadoop.fs.FileSystem;
 public class XGBoost {
   private static final Log logger = LogFactory.getLog(XGBoost.class);
 
+  public static final String[] MAXIMIZ_METRICES = {
+    "auc", "aucpr", "pre", "pre@", "map", "ndcg",
+    "auc@", "aucpr@", "map@", "ndcg@",
+  };
+
   /**
    * load model from modelPath
    *
@@ -158,7 +165,7 @@ public class XGBoost {
     //collect eval matrixs
     String[] evalNames;
     DMatrix[] evalMats;
-    float bestScore;
+    float bestScore = 1;
     int bestIteration;
     List<String> names = new ArrayList<String>();
     List<DMatrix> mats = new ArrayList<DMatrix>();
@@ -175,11 +182,7 @@ public class XGBoost {
 
     evalNames = names.toArray(new String[names.size()]);
     evalMats = mats.toArray(new DMatrix[mats.size()]);
-    if (isMaximizeEvaluation(params)) {
-      bestScore = -Float.MAX_VALUE;
-    } else {
-      bestScore = Float.MAX_VALUE;
-    }
+
     bestIteration = 0;
     metrics = metrics == null ? new float[evalNames.length][numRounds] : metrics;
 
@@ -210,6 +213,9 @@ public class XGBoost {
       checkpointIterations = new HashSet<>(ecm.getCheckpointRounds(checkpointInterval, numRounds));
     }
 
+    boolean initial_best_score_flag = false;
+    boolean max_direction = false;
+
     // begin to train
     for (int iter = booster.getVersion() / 2; iter < numRounds; iter++) {
       if (booster.getVersion() % 2 == 0) {
@@ -231,6 +237,18 @@ public class XGBoost {
         } else {
           evalInfo = booster.evalSet(evalMats, evalNames, iter, metricsOut);
         }
+
+        if (!initial_best_score_flag) {
+          if (isMaximizeEvaluation(evalInfo, evalNames, params)) {
+            max_direction = true;
+            bestScore = -Float.MAX_VALUE;
+          } else {
+            max_direction = false;
+            bestScore = Float.MAX_VALUE;
+          }
+          initial_best_score_flag = true;
+        }
+
         for (int i = 0; i < metricsOut.length; i++) {
           metrics[i][iter] = metricsOut[i];
         }
@@ -238,7 +256,7 @@ public class XGBoost {
         // If there is more than one evaluation datasets, the last one would be used
         // to determinate early stop.
         float score = metricsOut[metricsOut.length - 1];
-        if (isMaximizeEvaluation(params)) {
+        if (max_direction) {
           // Update best score if the current score is better (no update when equal)
           if (score > bestScore) {
             bestScore = score;
@@ -264,11 +282,9 @@ public class XGBoost {
           break;
         }
         if (Communicator.getRank() == 0 && shouldPrint(params, iter)) {
-          if (shouldPrint(params, iter)){
           Communicator.communicatorPrint(evalInfo + '\n');
         }
       }
-      }
       booster.saveRabitCheckpoint();
     }
     return booster;
@@ -360,16 +376,50 @@ public class XGBoost {
     return iter - bestIteration >= earlyStoppingRounds;
   }
 
-  private static boolean isMaximizeEvaluation(Map<String, Object> params) {
-    try {
-      String maximize = String.valueOf(params.get("maximize_evaluation_metrics"));
-      assert(maximize != null);
-      return Boolean.valueOf(maximize);
-    } catch (Exception ex) {
-      logger.error("maximize_evaluation_metrics has to be specified for enabling early stop," +
-              " allowed value: true/false", ex);
-      throw ex;
+  private static String getMetricNameFromlog(String evalInfo, String[] evalNames) {
+    String regexPattern = Pattern.quote(evalNames[0]) + "-(.*):";
+    Pattern pattern = Pattern.compile(regexPattern);
+    Matcher matcher = pattern.matcher(evalInfo);
+
+    String metricName = null;
+    if (matcher.find()) {
+      metricName = matcher.group(1);
+      logger.debug("Got the metric name: " + metricName);
     }
+    return metricName;
+  }
+
+  // visiable for testing
+  public static boolean isMaximizeEvaluation(String evalInfo,
+                                             String[] evalNames,
+                                             Map<String, Object> params) {
+
+    String metricName;
+
+    if (params.get("maximize_evaluation_metrics") != null) {
+      // user has forced the direction no matter what is the metric name.
+      String maximize = String.valueOf(params.get("maximize_evaluation_metrics"));
+      return Boolean.valueOf(maximize);
+    }
+
+    if (params.get("eval_metric") != null) {
+      // user has special metric name
+      metricName = String.valueOf(params.get("eval_metric"));
+    } else {
+      // infer the metric name from log
+      metricName = getMetricNameFromlog(evalInfo, evalNames);
+    }
+
+    assert metricName != null;
+
+    if (!"mape".equals(metricName)) {
+      for (String x : MAXIMIZ_METRICES) {
+        if (metricName.startsWith(x)) {
+          return true;
+        }
+      }
+    }
+    return false;
   }
 
   /**

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

@@ -0,0 +1,121 @@
+/*
+ Copyright (c) 2023 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;
+
+import junit.framework.TestCase;
+import ml.dmlc.xgboost4j.LabeledPoint;
+import org.junit.Test;
+
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Random;
+
+public class XGBoostTest {
+
+  private String composeEvalInfo(String metric, String evalName) {
+    return "[0]\t" + evalName + "-" + metric + ":" + "\ttest";
+  }
+
+  @Test
+  public void testIsMaximizeEvaluation() {
+    String[] minimum_metrics = {"mape", "logloss", "error", "others"};
+    String[] evalNames = {"set-abc"};
+
+    HashMap<String, Object> params = new HashMap<>();
+
+    // test1, infer the metric from faked log
+    for (String x : XGBoost.MAXIMIZ_METRICES) {
+      String evalInfo = composeEvalInfo(x, evalNames[0]);
+      TestCase.assertTrue(XGBoost.isMaximizeEvaluation(evalInfo, evalNames, params));
+    }
+
+    // test2, the direction for mape should be minimum
+    String evalInfo = composeEvalInfo("mape", evalNames[0]);
+    TestCase.assertFalse(XGBoost.isMaximizeEvaluation(evalInfo, evalNames, params));
+
+    // test3, force maximize_evaluation_metrics
+    params.clear();
+    params.put("maximize_evaluation_metrics", true);
+    // auc should be max,
+    evalInfo = composeEvalInfo("auc", evalNames[0]);
+    TestCase.assertTrue(XGBoost.isMaximizeEvaluation(evalInfo, evalNames, params));
+
+    params.clear();
+    params.put("maximize_evaluation_metrics", false);
+    // auc should be min,
+    evalInfo = composeEvalInfo("auc", evalNames[0]);
+    TestCase.assertFalse(XGBoost.isMaximizeEvaluation(evalInfo, evalNames, params));
+
+    // test4, set the metric manually
+    for (String x : XGBoost.MAXIMIZ_METRICES) {
+      params.clear();
+      params.put("eval_metric", x);
+      evalInfo = composeEvalInfo(x, evalNames[0]);
+      TestCase.assertTrue(XGBoost.isMaximizeEvaluation(evalInfo, evalNames, params));
+    }
+
+    // test5, set the metric manually
+    for (String x : minimum_metrics) {
+      params.clear();
+      params.put("eval_metric", x);
+      evalInfo = composeEvalInfo(x, evalNames[0]);
+      TestCase.assertFalse(XGBoost.isMaximizeEvaluation(evalInfo, evalNames, params));
+    }
+
+  }
+
+  @Test
+  public void testEarlyStop() throws XGBoostError {
+    Random random = new Random(1);
+
+    java.util.ArrayList<Float> labelall = new java.util.ArrayList<Float>();
+    int nrep = 3000;
+    java.util.List<LabeledPoint> blist = new java.util.LinkedList<LabeledPoint>();
+    for (int i = 0; i < nrep; ++i) {
+      LabeledPoint p = new LabeledPoint(
+        i % 2, 4,
+        new int[]{0, 1, 2, 3},
+        new float[]{random.nextFloat(), random.nextFloat(), random.nextFloat(), random.nextFloat()});
+      blist.add(p);
+      labelall.add(p.label());
+    }
+
+    DMatrix dmat = new DMatrix(blist.iterator(), null);
+
+    int round = 50;
+    int earlyStop = 2;
+
+    HashMap<String, Object> mapParams = new HashMap<>();
+    mapParams.put("eta", 0.1);
+    mapParams.put("objective", "binary:logistic");
+    mapParams.put("max_depth", 3);
+    mapParams.put("eval_metric", "auc");
+    mapParams.put("silent", 0);
+
+    HashMap<String, DMatrix> mapWatches = new HashMap<>();
+    mapWatches.put("selTrain-*", dmat);
+
+    try {
+      Booster booster = XGBoost.train(dmat, mapParams, round, mapWatches, null, null, null, earlyStop);
+      Map<String, String> attrs = booster.getAttrs();
+      TestCase.assertTrue(Integer.valueOf(attrs.get("best_iteration")) < round - 1);
+    } catch (Exception e) {
+      TestCase.assertFalse(false);
+    }
+
+  }
+}