Turn warning messages into Python warnings. (#9387)

python-package/xgboost/core.py

@@ -153,7 +153,11 @@ def _expect(expectations: Sequence[Type], got: Type) -> str:
 
 def _log_callback(msg: bytes) -> None:
     """Redirect logs from native library into Python console"""
-    print(py_str(msg))
+    smsg = py_str(msg)
+    if smsg.find("WARNING:") != -1:
+        warnings.warn(smsg, UserWarning)
+        return
+    print(smsg)
 
 
 def _get_log_callback_func() -> Callable:

tests/python-gpu/load_pickle.py

@@ -61,9 +61,7 @@ class TestLoadPickle:
         rng = np.random.RandomState(1994)
         X = rng.randn(10, 10)
         y = rng.randn(10)
-        with tm.captured_output() as (out, err):
+        with pytest.warns(UserWarning, match="No visible GPU is found"):
             # Test no thrust exception is thrown
-            with pytest.raises(xgb.core.XGBoostError):
+            with pytest.raises(xgb.core.XGBoostError, match="have at least one device"):
                 xgb.train({"tree_method": "gpu_hist"}, xgb.DMatrix(X, y))
-
-            assert out.getvalue().find("No visible GPU is found") != -1

tests/python/test_with_sklearn.py

@@ -3,6 +3,7 @@ import os
 import pickle
 import random
 import tempfile
+import warnings
 from typing import Callable, Optional
 
 import numpy as np
@@ -1091,25 +1092,22 @@ def test_constraint_parameters():
     )
 
 
+@pytest.mark.filterwarnings("error")
 def test_parameter_validation():
-    reg = xgb.XGBRegressor(foo='bar', verbosity=1)
+    reg = xgb.XGBRegressor(foo="bar", verbosity=1)
     X = np.random.randn(10, 10)
     y = np.random.randn(10)
-    with tm.captured_output() as (out, err):
+    with pytest.warns(Warning, match="foo"):
         reg.fit(X, y)
-        output = out.getvalue().strip()
 
-    assert output.find('foo') != -1
-
-    reg = xgb.XGBRegressor(n_estimators=2, missing=3,
-                           importance_type='gain', verbosity=1)
+    reg = xgb.XGBRegressor(
+        n_estimators=2, missing=3, importance_type="gain", verbosity=1
+    )
     X = np.random.randn(10, 10)
     y = np.random.randn(10)
-    with tm.captured_output() as (out, err):
-        reg.fit(X, y)
-        output = out.getvalue().strip()
 
-    assert len(output) == 0
+    with warnings.catch_warnings():
+        reg.fit(X, y)
 
 
 def test_deprecate_position_arg():