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

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():
-    # See demo/aft_survival/aft_survival_viz_demo.py
+@pytest.fixture(scope="module")
+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_upper = np.array([INF, INF,   20, 50, INF])
+    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_upper_bound', y_upper)
+    dmat.set_float_info("label_lower_bound", y_lower)
+    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)