Fix default metric configuration for pseudohuber.

src/learner.cc

@@ -1319,7 +1319,9 @@ class LearnerImpl : public LearnerIO {
     if (metrics_.empty() && tparam_.disable_default_eval_metric <= 0) {
       metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric(), &ctx_));
       auto config = obj_->DefaultMetricConfig();
-      metrics_.back()->LoadConfig(config);
+      if (!IsA<Null>(config)) {
+        metrics_.back()->LoadConfig(config);
+      }
       metrics_.back()->Configure({cfg_.begin(), cfg_.end()});
     }
 

src/objective/regression_obj.cu

@@ -287,6 +287,13 @@ class PseudoHuberRegression : public FitIntercept {
     }
     FromJson(in["pseudo_huber_param"], &param_);
   }
+  [[nodiscard]] Json DefaultMetricConfig() const override {
+    CHECK(param_.GetInitialised());
+    Json config{Object{}};
+    config["name"] = String{this->DefaultEvalMetric()};
+    config["pseudo_huber_param"] = ToJson(param_);
+    return config;
+  }
 };
 
 XGBOOST_REGISTER_OBJECTIVE(PseudoHuberRegression, "reg:pseudohubererror")

tests/cpp/objective/test_objective.cc

@@ -6,6 +6,7 @@
 #include <xgboost/objective.h>
 
 #include "../helpers.h"
+#include "../objective_helpers.h"
 
 TEST(Objective, UnknownFunction) {
   xgboost::ObjFunction* obj = nullptr;
@@ -43,4 +44,61 @@ TEST(Objective, PredTransform) {
     ASSERT_TRUE(predts.HostCanWrite());
   }
 }
+
+class TestDefaultObjConfig : public ::testing::TestWithParam<std::string> {
+  Context ctx_;
+
+ public:
+  void Run(std::string objective) {
+    auto Xy = MakeFmatForObjTest(objective);
+    std::unique_ptr<Learner> learner{Learner::Create({Xy})};
+    std::unique_ptr<ObjFunction> objfn{ObjFunction::Create(objective, &ctx_)};
+
+    learner->SetParam("objective", objective);
+    if (objective.find("multi") != std::string::npos) {
+      learner->SetParam("num_class", "3");
+      objfn->Configure(Args{{"num_class", "3"}});
+    } else if (objective.find("quantile") != std::string::npos) {
+      learner->SetParam("quantile_alpha", "0.5");
+      objfn->Configure(Args{{"quantile_alpha", "0.5"}});
+    } else {
+      objfn->Configure(Args{});
+    }
+    learner->Configure();
+    learner->UpdateOneIter(0, Xy);
+    learner->EvalOneIter(0, {Xy}, {"train"});
+    Json config{Object{}};
+    learner->SaveConfig(&config);
+    auto jobj = get<Object const>(config["learner"]["objective"]);
+
+    ASSERT_TRUE(jobj.find("name") != jobj.cend());
+    // FIXME(jiamingy): We should have the following check, but some legacy parameter like
+    // "pos_weight", "delta_step" in objectives are not in metrics.
+
+    // if (jobj.size() > 1) {
+    //   ASSERT_FALSE(IsA<Null>(objfn->DefaultMetricConfig()));
+    // }
+    auto mconfig = objfn->DefaultMetricConfig();
+    if (!IsA<Null>(mconfig)) {
+      // make sure metric can handle it
+      std::unique_ptr<Metric> metricfn{Metric::Create(get<String const>(mconfig["name"]), &ctx_)};
+      metricfn->LoadConfig(mconfig);
+      Json loaded(Object{});
+      metricfn->SaveConfig(&loaded);
+      metricfn->Configure(Args{});
+      ASSERT_EQ(mconfig, loaded);
+    }
+  }
+};
+
+TEST_P(TestDefaultObjConfig, Objective) {
+  std::string objective = GetParam();
+  this->Run(objective);
+}
+
+INSTANTIATE_TEST_SUITE_P(Objective, TestDefaultObjConfig,
+                         ::testing::ValuesIn(MakeObjNamesForTest()),
+                         [](const ::testing::TestParamInfo<TestDefaultObjConfig::ParamType>& info) {
+                           return ObjTestNameGenerator(info);
+                         });
 } // namespace xgboost

tests/cpp/objective_helpers.cc

@@ -0,0 +1,31 @@
+/**
+ * Copyright (c) 2023, XGBoost contributors
+ */
+#include "objective_helpers.h"
+
+#include "../../src/common/linalg_op.h"  // for begin, end
+#include "helpers.h"                     // for RandomDataGenerator
+
+namespace xgboost {
+std::shared_ptr<DMatrix> MakeFmatForObjTest(std::string const& obj) {
+  auto constexpr kRows = 10, kCols = 10;
+  auto p_fmat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
+  auto& h_upper = p_fmat->Info().labels_upper_bound_.HostVector();
+  auto& h_lower = p_fmat->Info().labels_lower_bound_.HostVector();
+  h_lower.resize(kRows);
+  h_upper.resize(kRows);
+  for (size_t i = 0; i < kRows; ++i) {
+    h_lower[i] = 1;
+    h_upper[i] = 10;
+  }
+  if (obj.find("rank:") != std::string::npos) {
+    auto h_label = p_fmat->Info().labels.HostView();
+    std::size_t k = 0;
+    for (auto& v : h_label) {
+      v = k % 2 == 0;
+      ++k;
+    }
+  }
+  return p_fmat;
+};
+}  // namespace xgboost

tests/cpp/objective_helpers.h

@@ -1,6 +1,8 @@
 /**
  * Copyright (c) 2023, XGBoost contributors
  */
+#pragma once
+
 #include <dmlc/registry.h>  // for Registry
 #include <gtest/gtest.h>
 #include <xgboost/objective.h>  // for ObjFunctionReg
@@ -29,4 +31,6 @@ inline std::string ObjTestNameGenerator(const ::testing::TestParamInfo<ParamType
   }
   return name;
 };
+
+std::shared_ptr<DMatrix> MakeFmatForObjTest(std::string const& obj);
 }  // namespace xgboost

tests/cpp/test_learner.cc

@@ -655,33 +655,11 @@ TEST_F(InitBaseScore, InitWithPredict) { this->TestInitWithPredt(); }
 TEST_F(InitBaseScore, UpdateProcess) { this->TestUpdateProcess(); }
 
 class TestColumnSplit : public ::testing::TestWithParam<std::string> {
-  static auto MakeFmat(std::string const& obj) {
-    auto constexpr kRows = 10, kCols = 10;
-    auto p_fmat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
-    auto& h_upper = p_fmat->Info().labels_upper_bound_.HostVector();
-    auto& h_lower = p_fmat->Info().labels_lower_bound_.HostVector();
-    h_lower.resize(kRows);
-    h_upper.resize(kRows);
-    for (size_t i = 0; i < kRows; ++i) {
-      h_lower[i] = 1;
-      h_upper[i] = 10;
-    }
-    if (obj.find("rank:") != std::string::npos) {
-      auto h_label = p_fmat->Info().labels.HostView();
-      std::size_t k = 0;
-      for (auto& v : h_label) {
-        v = k % 2 == 0;
-        ++k;
-      }
-    }
-    return p_fmat;
-  };
-
   void TestBaseScore(std::string objective, float expected_base_score, Json expected_model) {
     auto const world_size = collective::GetWorldSize();
     auto const rank = collective::GetRank();
 
-    auto p_fmat = MakeFmat(objective);
+    auto p_fmat = MakeFmatForObjTest(objective);
     std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
     std::unique_ptr<Learner> learner{Learner::Create({sliced})};
     learner->SetParam("tree_method", "approx");
@@ -705,7 +683,7 @@ class TestColumnSplit : public ::testing::TestWithParam<std::string> {
 
  public:
   void Run(std::string objective) {
-    auto p_fmat = MakeFmat(objective);
+    auto p_fmat = MakeFmatForObjTest(objective);
     std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
     learner->SetParam("tree_method", "approx");
     learner->SetParam("objective", objective);