Send default configuration from metric to objective. (#8760)

include/xgboost/objective.h

@@ -55,6 +55,11 @@ class ObjFunction : public Configurable {
 
   /*! \return the default evaluation metric for the objective */
   virtual const char* DefaultEvalMetric() const = 0;
+  /**
+   * \brief Return the configuration for the default metric.
+   */
+  virtual Json DefaultMetricConfig() const { return Json{Null{}}; }
+
   // the following functions are optional, most of time default implementation is good enough
   /*!
    * \brief transform prediction values, this is only called when Prediction is called

src/learner.cc

@@ -520,6 +520,7 @@ class LearnerConfiguration : public Learner {
 
     auto const& objective_fn = learner_parameters.at("objective");
     if (!obj_) {
+      CHECK_EQ(get<String const>(objective_fn["name"]), tparam_.objective);
       obj_.reset(ObjFunction::Create(tparam_.objective, &ctx_));
     }
     obj_->LoadConfig(objective_fn);
@@ -1311,8 +1312,10 @@ class LearnerImpl : public LearnerIO {
     std::ostringstream os;
     os.precision(std::numeric_limits<double>::max_digits10);
     os << '[' << iter << ']' << std::setiosflags(std::ios::fixed);
-    if (metrics_.size() == 0 && tparam_.disable_default_eval_metric <= 0) {
+    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);
       metrics_.back()->Configure({cfg_.begin(), cfg_.end()});
     }
 

src/objective/aft_obj.cu

@@ -134,6 +134,12 @@ class AFTObj : public ObjFunction {
   void LoadConfig(Json const& in) override {
     FromJson(in["aft_loss_param"], &param_);
   }
+  Json DefaultMetricConfig() const override {
+    Json config{Object{}};
+    config["name"] = String{this->DefaultEvalMetric()};
+    config["aft_loss_param"] = ToJson(param_);
+    return config;
+  }
 
  private:
   AFTParam param_;

tests/python/test_survival.py

@@ -1,5 +1,6 @@
 import json
 import os
+from typing import Optional, Tuple
 
 import numpy as np
 import pytest
@@ -10,16 +11,56 @@ from xgboost import testing as tm
 dpath = tm.data_dir(__file__)
 
 
-def test_aft_survival_toy_data():
+@pytest.fixture(scope="module")
-    # See demo/aft_survival/aft_survival_viz_demo.py
+def toy_data() -> Tuple[xgb.DMatrix, np.ndarray, np.ndarray]:
     X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
     INF = np.inf
-    y_lower = np.array([ 10,  15, -INF, 30, 100])
+    y_lower = np.array([10, 15, -INF, 30, 100])
     y_upper = np.array([INF, INF, 20, 50, INF])
 
     dmat = xgb.DMatrix(X)
-    dmat.set_float_info('label_lower_bound', y_lower)
+    dmat.set_float_info("label_lower_bound", y_lower)
-    dmat.set_float_info('label_upper_bound', y_upper)
+    dmat.set_float_info("label_upper_bound", y_upper)
+    return dmat, y_lower, y_upper
+
+
+def test_default_metric(toy_data: Tuple[xgb.DMatrix, np.ndarray, np.ndarray]) -> None:
+    Xy, y_lower, y_upper = toy_data
+
+    def run(evals: Optional[list]) -> None:
+        # test with or without actual evaluation.
+        booster = xgb.train(
+            {"objective": "survival:aft", "aft_loss_distribution": "extreme"},
+            Xy,
+            num_boost_round=1,
+            evals=evals,
+        )
+        config = json.loads(booster.save_config())
+        metrics = config["learner"]["metrics"]
+        assert len(metrics) == 1
+        assert metrics[0]["aft_loss_param"]["aft_loss_distribution"] == "extreme"
+
+        booster = xgb.train(
+            {"objective": "survival:aft"},
+            Xy,
+            num_boost_round=1,
+            evals=evals,
+        )
+        config = json.loads(booster.save_config())
+        metrics = config["learner"]["metrics"]
+        assert len(metrics) == 1
+        assert metrics[0]["aft_loss_param"]["aft_loss_distribution"] == "normal"
+
+    run([(Xy, "Train")])
+    run(None)
+
+
+def test_aft_survival_toy_data(
+    toy_data: Tuple[xgb.DMatrix, np.ndarray, np.ndarray]
+) -> None:
+    # See demo/aft_survival/aft_survival_viz_demo.py
+    X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
+    dmat, y_lower, y_upper = toy_data
 
     # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
     #              the corresponding predicted label (y_pred)