try

jvm-packages/dev/build.sh

@@ -17,5 +17,5 @@ rm /usr/bin/python
 ln -s /opt/rh/python27/root/usr/bin/python /usr/bin/python
 
 # build xgboost
-cd /xgboost/jvm-packages;mvn package
+cd /xgboost/jvm-packages;ulimit -c unlimited;mvn package
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/LogRegObj.scala

@@ -0,0 +1,84 @@
+/*
+ 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 ml.dmlc.xgboost4j.scala.spark
+
+import scala.collection.mutable.ListBuffer
+
+import ml.dmlc.xgboost4j.java.XGBoostError
+import ml.dmlc.xgboost4j.scala.{DMatrix, ObjectiveTrait}
+import org.apache.commons.logging.{Log, LogFactory}
+
+/**
+ * loglikelihoode loss obj function
+ */
+class LogRegObj(x: Int) extends ObjectiveTrait {
+  private val logger: Log = LogFactory.getLog(classOf[LogRegObj])
+  /**
+   * user define objective function, return gradient and second order gradient
+   *
+   * @param predicts untransformed margin predicts
+   * @param dtrain   training data
+   * @return List with two float array, correspond to first order grad and second order grad
+   */
+  override def getGradient(predicts: Array[Array[Float]], dtrain: DMatrix)
+  : List[Array[Float]] = {
+    val nrow = predicts.length
+    val gradients = new ListBuffer[Array[Float]]
+    var labels: Array[Float] = null
+    try {
+      labels = dtrain.getLabel
+    } catch {
+      case e: XGBoostError =>
+        logger.error(e)
+        null
+      case _: Throwable =>
+        null
+    }
+    val grad = new Array[Float](nrow)
+    val hess = new Array[Float](nrow)
+    val transPredicts = transform(predicts)
+
+    for (i <- 0 until nrow) {
+      val predict = transPredicts(i)(0)
+      grad(i) = predict - labels(i)
+      hess(i) = predict * (1 - predict)
+    }
+    gradients += grad
+    gradients += hess
+    gradients.toList
+  }
+
+  /**
+   * simple sigmoid func
+   *
+   * @param input
+   * @return Note: this func is not concern about numerical stability, only used as example
+   */
+  def sigmoid(input: Float): Float = {
+    (1 / (1 + Math.exp(-input))).toFloat
+  }
+
+  def transform(predicts: Array[Array[Float]]): Array[Array[Float]] = {
+    val nrow = predicts.length
+    val transPredicts = Array.fill[Float](nrow, 1)(0)
+    for (i <- 0 until nrow) {
+      transPredicts(i)(0) = sigmoid(predicts(i)(0))
+    }
+    transPredicts
+  }
+
+}

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/PersistenceSuite.scala

@@ -19,14 +19,22 @@ package ml.dmlc.xgboost4j.scala.spark
 import java.io.{File, FileNotFoundException}
 import java.util.Arrays
 
-import ml.dmlc.xgboost4j.scala.DMatrix
+import scala.collection.mutable.ListBuffer
 
+import ml.dmlc.xgboost4j.scala.{DMatrix, ObjectiveTrait}
 import scala.util.Random
+
+import ml.dmlc.xgboost4j.java.XGBoostError
+import ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter
+import org.apache.commons.logging.{Log, LogFactory}
+
 import org.apache.spark.ml.feature._
 import org.apache.spark.ml.{Pipeline, PipelineModel}
 import org.apache.spark.network.util.JavaUtils
 import org.scalatest.{BeforeAndAfterAll, FunSuite}
 
+import org.apache.spark.SparkContext
+
 class PersistenceSuite extends FunSuite with PerTest with BeforeAndAfterAll {
 
   private var tempDir: File = _
@@ -162,5 +170,25 @@ class PersistenceSuite extends FunSuite with PerTest with BeforeAndAfterAll {
     assert(xgbModel.getNumRound === xgbModel2.getNumRound)
     assert(xgbModel.getRawPredictionCol === xgbModel2.getRawPredictionCol)
   }
+
+  test("test persistence of XGBoostClassificationModel with customizedObj and" +
+    " customizedEval") {
+    val r = new Random(0)
+    // maybe move to shared context, but requires session to import implicits
+    val df = ss.createDataFrame(Seq.fill(100)(r.nextInt(2)).map(i => (i, i))).
+      toDF("feature", "label")
+
+    val assembler = new VectorAssembler()
+      .setInputCols(df.columns.filter(!_.contains("label")))
+      .setOutputCol("features")
+    val transformedDF = assembler.transform(df)
+
+    val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "binary:logistic", "num_round" -> "10", "num_workers" -> numWorkers)
+    val xgb = new XGBoostClassifier(paramMap).setCustomObj(new LogRegObj(1)).
+      setObjectiveType("regression")
+    val xgbModel = xgb.fit(transformedDF)
+    DefaultXGBoostParamsWriter.getMetadataToSave(xgbModel, SparkContext.getOrCreate(), None, None)
+  }
 }
 

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifierSuite.scala

@@ -16,7 +16,7 @@
 
 package ml.dmlc.xgboost4j.scala.spark
 
-import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost => ScalaXGBoost}
+import ml.dmlc.xgboost4j.scala.{DMatrix, ObjectiveTrait, XGBoost => ScalaXGBoost}
 
 import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.ParamMap

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala

@@ -99,7 +99,6 @@ class XGBoostGeneralSuite extends FunSuite with PerTest {
     assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
   }
 
-
   test("training with Scala-implemented Rabit tracker") {
     val eval = new EvalError()
     val training = buildDataFrame(Classification.train)
@@ -111,7 +110,31 @@ class XGBoostGeneralSuite extends FunSuite with PerTest {
     assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
   }
 
-  test("test with fast histo with monotone_constraints") {
+  test("test with fast histo with monotone_constraints (lossguide)") {
+    val eval = new EvalError()
+    val training = buildDataFrame(Classification.train)
+    val testDM = new DMatrix(Classification.test.iterator)
+    val paramMap = Map("eta" -> "1",
+      "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "lossguide",
+      "num_round" -> 5, "num_workers" -> numWorkers, "monotone_constraints" -> "(1, 0)")
+    val model = new XGBoostClassifier(paramMap).fit(training)
+    assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
+  }
+
+  test("test with fast histo with interaction_constraints (lossguide)") {
+    val eval = new EvalError()
+    val training = buildDataFrame(Classification.train)
+    val testDM = new DMatrix(Classification.test.iterator)
+    val paramMap = Map("eta" -> "1",
+      "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "binary:logistic", "tree_method" -> "hist", "grow_policy" -> "lossguide",
+      "num_round" -> 5, "num_workers" -> numWorkers, "interaction_constraints" -> "[[1,2],[2,3,4]]")
+    val model = new XGBoostClassifier(paramMap).fit(training)
+    assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
+  }
+
+  test("test with fast histo with monotone_constraints (depthwise)") {
     val eval = new EvalError()
     val training = buildDataFrame(Classification.train)
     val testDM = new DMatrix(Classification.test.iterator)
@@ -123,7 +146,7 @@ class XGBoostGeneralSuite extends FunSuite with PerTest {
     assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
   }
 
-  test("test with fast histo with interaction_constraints") {
+  test("test with fast histo with interaction_constraints (depthwise)") {
     val eval = new EvalError()
     val training = buildDataFrame(Classification.train)
     val testDM = new DMatrix(Classification.test.iterator)

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

@@ -83,7 +83,7 @@ private Booster trainBooster(DMatrix trainMat, DMatrix testMat) throws XGBoostEr
     //train a boost model
     return XGBoost.train(trainMat, paramMap, round, watches, null, null);
   }
-
+  
   @Test
   public void testBoosterBasic() throws XGBoostError, IOException {
 
@@ -536,15 +536,12 @@ public void testGetFeatureImportanceTotalCover() throws XGBoostError {
   @Test
   public void testFastHistoDepthwiseMaxDepth() throws XGBoostError {
     DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
-    DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
-    // testBoosterWithFastHistogram(trainMat, testMat);
     Map<String, Object> paramMap = new HashMap<String, Object>() {
       {
         put("max_depth", 3);
         put("silent", 1);
         put("objective", "binary:logistic");
         put("tree_method", "hist");
-        put("max_depth", 2);
         put("grow_policy", "depthwise");
         put("eval_metric", "auc");
       }

src/common/column_matrix.h

@@ -71,7 +71,6 @@ class ColumnMatrix {
                 double  sparse_threshold) {
     const auto nfeature = static_cast<bst_uint>(gmat.cut.row_ptr.size() - 1);
     const size_t nrow = gmat.row_ptr.size() - 1;
-
     // identify type of each column
     feature_counts_.resize(nfeature);
     type_.resize(nfeature);
@@ -131,7 +130,6 @@ class ColumnMatrix {
         // max() indicates missing values
       }
     }
-
     // loop over all rows and fill column entries
     // num_nonzeros[fid] = how many nonzeros have this feature accumulated so far?
     std::vector<size_t> num_nonzeros;

src/common/hist_util.cc

@@ -1,8 +1,6 @@
 /*!
- * Copyright 2017-2018 by Contributors
+ * Copyright 2019 by Contributors
  * \file hist_util.h
- * \brief Utilities to store histograms
- * \author Philip Cho, Tianqi Chen
  */
 #include <rabit/rabit.h>
 #include <dmlc/omp.h>
@@ -57,15 +55,13 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
           SparsePage::Inst inst = batch[i];
           for (auto& ins : inst) {
             if (ins.index >= begin && ins.index < end) {
-              sketchs[ins.index].Push(ins.fvalue,
-                                      weights.size() > 0 ? weights[ridx] : 1.0f);
+              sketchs[ins.index].Push(ins.fvalue, weights.size() > 0 ? weights[ridx] : 1.0f);
             }
           }
         }
       }
     }
   }
-
   Init(&sketchs, max_num_bins);
 }
 
@@ -135,7 +131,6 @@ uint32_t HistCutMatrix::GetBinIdx(const Entry& e) {
 
 void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
   cut.Init(p_fmat, max_num_bins);
-
   const int nthread = omp_get_max_threads();
   const uint32_t nbins = cut.row_ptr.back();
   hit_count.resize(nbins, 0);
@@ -148,7 +143,6 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
       row_ptr.push_back(batch[i].size() + row_ptr.back());
     }
     index.resize(row_ptr.back());
-
     CHECK_GT(cut.cut.size(), 0U);
     CHECK_EQ(cut.row_ptr.back(), cut.cut.size());
 
@@ -159,11 +153,10 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
       size_t ibegin = row_ptr[rbegin + i];
       size_t iend = row_ptr[rbegin + i + 1];
       SparsePage::Inst inst = batch[i];
-
       CHECK_EQ(ibegin + inst.size(), iend);
+
       for (bst_uint j = 0; j < inst.size(); ++j) {
         uint32_t idx = cut.GetBinIdx(inst[j]);
-
         index[ibegin + j] = idx;
         ++hit_count_tloc_[tid * nbins + idx];
       }

src/common/hist_util.h

@@ -26,7 +26,7 @@ struct HistCutMatrix {
   std::vector<bst_float> min_val;
   /*! \brief the cut field */
   std::vector<bst_float> cut;
-  uint32_t GetBinIdx(const Entry &e);
+  uint32_t GetBinIdx(const Entry& e);
 
   using WXQSketch = common::WXQuantileSketch<bst_float, bst_float>;
 

src/common/random.h

@@ -103,10 +103,8 @@ class ColumnSampler {
     std::shuffle(new_features.begin(), new_features.end(), common::GlobalRandom());
     new_features.resize(n);
     std::sort(new_features.begin(), new_features.end());
-
     // ensure that new_features are the same across ranks
     rabit::Broadcast(&new_features, 0);
-
     return p_new_features;
   }
 

src/tree/split_evaluator.cc

@@ -412,7 +412,6 @@ class InteractionConstraint final : public SplitEvaluator {
     if (!CheckInteractionConstraint(featureid, nodeid)) {
       return -std::numeric_limits<bst_float>::infinity();
     }
-
     // Otherwise, get score from inner evaluator
     bst_float score = inner_->ComputeSplitScore(
       nodeid, featureid, left_stats, right_stats, left_weight, right_weight);

src/tree/updater_histmaker.cc

@@ -364,8 +364,7 @@ class CQHistMaker: public HistMaker {
                          &thread_stats_, &node_stats_);
       for (int const nid : this->qexpand_) {
         const int wid = this->node2workindex_[nid];
-        this->wspace_.hset[0][fset.size() + wid * (fset.size() + 1)]
-                .data[0] = node_stats_[nid];
+        this->wspace_.hset[0][fset.size() + wid * (fset.size() + 1)].data[0] = node_stats_[nid];
       }
     };
     // sync the histogram