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

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Jiaming Yuan 2023-08-15 02:27:58 +08:00 committed by GitHub
parent e3f624d8e7
commit 19b59938b7
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4 changed files with 12 additions and 12 deletions
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8
tests/python-gpu/test_gpu_updaters.py
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@ -36,7 +36,7 @@ class TestGPUUpdatersMulti:
param["tree_method"] = "gpu_hist" param["tree_method"] = "gpu_hist"
param = dataset.set_params(param) param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds) result = train_result(param, dataset.get_dmat(), num_rounds)
note(result) note(str(result))
assert tm.non_increasing(result["train"][dataset.metric]) assert tm.non_increasing(result["train"][dataset.metric])
@ -90,12 +90,12 @@ class TestGPUUpdaters:
def test_sparse(self, dataset): def test_sparse(self, dataset):
param = {"tree_method": "hist", "max_bin": 64} param = {"tree_method": "hist", "max_bin": 64}
hist_result = train_result(param, dataset.get_dmat(), 16) 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]) assert tm.non_increasing(hist_result["train"][dataset.metric])
param = {"tree_method": "gpu_hist", "max_bin": 64} param = {"tree_method": "gpu_hist", "max_bin": 64}
gpu_hist_result = train_result(param, dataset.get_dmat(), 16) 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]) assert tm.non_increasing(gpu_hist_result["train"][dataset.metric])
np.testing.assert_allclose( np.testing.assert_allclose(
@ -221,7 +221,7 @@ class TestGPUUpdaters:
dataset.get_device_dmat(max_bin=param.get("max_bin", None)), dataset.get_device_dmat(max_bin=param.get("max_bin", None)),
num_rounds, num_rounds,
) )
note(result) note(str(result))
assert tm.non_increasing(result["train"][dataset.metric], tolerance=1e-3) assert tm.non_increasing(result["train"][dataset.metric], tolerance=1e-3)
@given( @given(

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

2
tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
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@ -187,7 +187,7 @@ def run_gpu_hist(
num_boost_round=num_rounds, num_boost_round=num_rounds,
evals=[(m, "train")], evals=[(m, "train")],
)["history"]["train"][dataset.metric] )["history"]["train"][dataset.metric]
note(history) note(str(history))
# See note on `ObjFunction::UpdateTreeLeaf`. # See note on `ObjFunction::UpdateTreeLeaf`.
update_leaf = dataset.name.endswith("-l1") update_leaf = dataset.name.endswith("-l1")

2
tests/test_distributed/test_with_dask/test_with_dask.py
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@ -1484,7 +1484,7 @@ class TestWithDask:
num_boost_round=num_rounds, num_boost_round=num_rounds,
evals=[(m, "train")], evals=[(m, "train")],
)["history"] )["history"]
note(history) note(str(history))
history = history["train"][dataset.metric] history = history["train"][dataset.metric]
def is_stump(): def is_stump():