debug.

Author: trivialfis

tests/python/test_quantile_dmatrix.py

@@ -110,9 +110,9 @@ def test_with_iterator(self, sparsity: float) -> None:
 
     @pytest.mark.parametrize("sparsity", [0.0, 0.1, 0.5, 0.8, 0.9])
     def test_training(self, sparsity: float) -> None:
-        n_samples_per_batch = 317
-        n_features = 8
-        n_batches = 7
+        n_samples_per_batch = 16
+        n_features = 2
+        n_batches = 2
         if sparsity == 0.0:
             it = IteratorForTest(
                 *make_batches(n_samples_per_batch, n_features, n_batches, False), None
@@ -127,27 +127,17 @@ def test_training(self, sparsity: float) -> None:
 
         parameters = {"tree_method": "hist", "max_bin": 256}
         Xy_it = xgb.QuantileDMatrix(it, max_bin=parameters["max_bin"])
-        from_it = xgb.train(parameters, Xy_it)
+        from_it = xgb.train(parameters, Xy_it, num_boost_round=1)
 
         X, y, w = it.as_arrays()
         w_it = Xy_it.get_weight()
         np.testing.assert_allclose(w_it, w)
 
         Xy_arr = xgb.DMatrix(X, y, weight=w)
-        from_arr = xgb.train(parameters, Xy_arr)
+        from_arr = xgb.train(parameters, Xy_arr, num_boost_round=1)
 
         np.testing.assert_allclose(from_arr.predict(Xy_it), from_it.predict(Xy_arr))
 
-        y -= y.min()
-        y += 0.01
-        Xy = xgb.QuantileDMatrix(X, y, weight=w)
-        with pytest.raises(ValueError, match=r"Only.*hist.*"):
-            parameters = {
-                "tree_method": "approx",
-                "max_bin": 256,
-                "objective": "reg:gamma",
-            }
-            xgb.train(parameters, Xy)
 
     def run_ref_dmatrix(self, rng: Any, tree_method: str, enable_cat: bool) -> None:
         n_samples, n_features = 2048, 17