Convert input to str for hypothesis note. (#9480)

tests/python-gpu/test_gpu_updaters.py

@@ -36,7 +36,7 @@ class TestGPUUpdatersMulti:
         param["tree_method"] = "gpu_hist"
         param = dataset.set_params(param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
-        note(result)
+        note(str(result))
         assert tm.non_increasing(result["train"][dataset.metric])
 
 
@@ -90,12 +90,12 @@ class TestGPUUpdaters:
     def test_sparse(self, dataset):
         param = {"tree_method": "hist", "max_bin": 64}
         hist_result = train_result(param, dataset.get_dmat(), 16)
-        note(hist_result)
+        note(str(hist_result))
         assert tm.non_increasing(hist_result["train"][dataset.metric])
 
         param = {"tree_method": "gpu_hist", "max_bin": 64}
         gpu_hist_result = train_result(param, dataset.get_dmat(), 16)
-        note(gpu_hist_result)
+        note(str(gpu_hist_result))
         assert tm.non_increasing(gpu_hist_result["train"][dataset.metric])
 
         np.testing.assert_allclose(
@@ -221,7 +221,7 @@ class TestGPUUpdaters:
             dataset.get_device_dmat(max_bin=param.get("max_bin", None)),
             num_rounds,
         )
-        note(result)
+        note(str(result))
         assert tm.non_increasing(result["train"][dataset.metric], tolerance=1e-3)
 
     @given(

tests/python/test_updaters.py

@@ -58,7 +58,7 @@ class TestTreeMethodMulti:
         param.update(hist_param)
         param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
-        note(result)
+        note(str(result))
         assert tm.non_increasing(result["train"][dataset.metric])
 
     @given(
@@ -84,7 +84,7 @@ class TestTreeMethodMulti:
         param.update(hist_param)
         param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
-        note(result)
+        note(str(result))
         assert tm.non_increasing(result["train"][dataset.metric])
 
 
@@ -125,7 +125,7 @@ class TestTreeMethod:
         param.update(hist_param)
         param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
-        note(result)
+        note(str(result))
         assert tm.non_increasing(result["train"][dataset.metric])
 
     @pytest.mark.skipif(**tm.no_sklearn())
@@ -172,7 +172,7 @@ class TestTreeMethod:
         param.update(hist_param)
         param.update(cache_param)
         result = train_result(param, dataset.get_dmat(), num_rounds)
-        note(result)
+        note(str(result))
         assert tm.non_increasing(result["train"][dataset.metric])
 
     def test_hist_categorical(self):
@@ -224,12 +224,12 @@ class TestTreeMethod:
     def test_sparse(self, dataset):
         param = {"tree_method": "hist", "max_bin": 64}
         hist_result = train_result(param, dataset.get_dmat(), 16)
-        note(hist_result)
+        note(str(hist_result))
         assert tm.non_increasing(hist_result['train'][dataset.metric])
 
         param = {"tree_method": "approx", "max_bin": 64}
         approx_result = train_result(param, dataset.get_dmat(), 16)
-        note(approx_result)
+        note(str(approx_result))
         assert tm.non_increasing(approx_result['train'][dataset.metric])
 
         np.testing.assert_allclose(

tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py

@@ -187,7 +187,7 @@ def run_gpu_hist(
         num_boost_round=num_rounds,
         evals=[(m, "train")],
     )["history"]["train"][dataset.metric]
-    note(history)
+    note(str(history))
 
     # See note on `ObjFunction::UpdateTreeLeaf`.
     update_leaf = dataset.name.endswith("-l1")

tests/test_distributed/test_with_dask/test_with_dask.py

@@ -1484,7 +1484,7 @@ class TestWithDask:
             num_boost_round=num_rounds,
             evals=[(m, "train")],
         )["history"]
-        note(history)
+        note(str(history))
         history = history["train"][dataset.metric]
 
         def is_stump():