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Mitigate flaky tests. (#7749)

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* Skip non-increasing test with external memory when subsample is used.
* Increase bin numbers for boost from prediction test. This mitigates the effect of
  non-deterministic partitioning.
This commit is contained in:
Jiaming Yuan
2022-03-28 21:20:50 +08:00
committed by GitHub
parent 39c5616af2
commit 8b3ecfca25
9 changed files with 50 additions and 46 deletions
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2
tests/python-gpu/test_gpu_data_iterator.py
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@@ -22,7 +22,7 @@ def test_gpu_single_batch() -> None:
strategies.integers(0, 13),
strategies.booleans(),
)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_gpu_data_iterator(
n_samples_per_batch: int, n_features: int, n_batches: int, subsample: bool
) -> None:

4
tests/python-gpu/test_gpu_linear.py
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@@ -30,7 +30,7 @@ def train_result(param, dmat, num_rounds):
class TestGPULinear:
@given(parameter_strategy, strategies.integers(10, 50),
tm.dataset_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_gpu_coordinate(self, param, num_rounds, dataset):
assume(len(dataset.y) > 0)
param['updater'] = 'gpu_coord_descent'
@@ -45,7 +45,7 @@ class TestGPULinear:
@given(parameter_strategy, strategies.integers(10, 50),
tm.dataset_strategy, strategies.floats(1e-5, 1.0),
strategies.floats(1e-5, 1.0))
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_gpu_coordinate_regularised(self, param, num_rounds, dataset, alpha, lambd):
assume(len(dataset.y) > 0)
param['updater'] = 'gpu_coord_descent'

10
tests/python-gpu/test_gpu_prediction.py
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@@ -247,7 +247,7 @@ class TestGPUPredict:
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_shap(self, num_rounds, dataset, param):
param.update({"predictor": "gpu_predictor", "gpu_id": 0})
param = dataset.set_params(param)
@@ -261,7 +261,7 @@ class TestGPUPredict:
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None, max_examples=20)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_shap_interactions(self, num_rounds, dataset, param):
param.update({"predictor": "gpu_predictor", "gpu_id": 0})
param = dataset.set_params(param)
@@ -312,14 +312,14 @@ class TestGPUPredict:
np.testing.assert_equal(cpu_leaf, gpu_leaf)
@given(predict_parameter_strategy, tm.dataset_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_predict_leaf_gbtree(self, param, dataset):
param['booster'] = 'gbtree'
param['tree_method'] = 'gpu_hist'
self.run_predict_leaf_booster(param, 10, dataset)
@given(predict_parameter_strategy, tm.dataset_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_predict_leaf_dart(self, param, dataset):
param['booster'] = 'dart'
param['tree_method'] = 'gpu_hist'
@@ -330,7 +330,7 @@ class TestGPUPredict:
@given(df=data_frames([column('x0', elements=strategies.integers(min_value=0, max_value=3)),
column('x1', elements=strategies.integers(min_value=0, max_value=5))],
index=range_indexes(min_size=20, max_size=50)))
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_predict_categorical_split(self, df):
from sklearn.metrics import mean_squared_error

10
tests/python-gpu/test_gpu_updaters.py
View File

@@ -46,7 +46,7 @@ class TestGPUUpdaters:
cputest = test_up.TestTreeMethod()
@given(parameter_strategy, strategies.integers(1, 20), tm.dataset_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_gpu_hist(self, param, num_rounds, dataset):
param["tree_method"] = "gpu_hist"
param = dataset.set_params(param)
@@ -56,7 +56,7 @@ class TestGPUUpdaters:
@given(strategies.integers(10, 400), strategies.integers(3, 8),
strategies.integers(1, 2), strategies.integers(4, 7))
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
@pytest.mark.skipif(**tm.no_pandas())
def test_categorical(self, rows, cols, rounds, cats):
self.cputest.run_categorical_basic(rows, cols, rounds, cats, "gpu_hist")
@@ -76,7 +76,7 @@ class TestGPUUpdaters:
@pytest.mark.skipif(**tm.no_cupy())
@given(parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_gpu_hist_device_dmatrix(self, param, num_rounds, dataset):
# We cannot handle empty dataset yet
assume(len(dataset.y) > 0)
@@ -88,7 +88,7 @@ class TestGPUUpdaters:
@given(parameter_strategy, strategies.integers(1, 20),
tm.dataset_strategy)
@settings(deadline=None)
@settings(deadline=None, print_blob=True)
def test_external_memory(self, param, num_rounds, dataset):
# We cannot handle empty dataset yet
assume(len(dataset.y) > 0)
@@ -127,7 +127,7 @@ class TestGPUUpdaters:
@pytest.mark.mgpu
@given(tm.dataset_strategy, strategies.integers(0, 10))
@settings(deadline=None, max_examples=10)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_specified_gpu_id_gpu_update(self, dataset, gpu_id):
param = {'tree_method': 'gpu_hist', 'gpu_id': gpu_id}
param = dataset.set_params(param)

6
tests/python-gpu/test_gpu_with_dask.py
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@@ -27,7 +27,7 @@ from test_with_dask import run_empty_dmatrix_reg # noqa
from test_with_dask import run_empty_dmatrix_auc # noqa
from test_with_dask import run_auc # noqa
from test_with_dask import run_boost_from_prediction # noqa
from test_with_dask import run_boost_from_prediction_multi_clasas # noqa
from test_with_dask import run_boost_from_prediction_multi_class # noqa
from test_with_dask import run_dask_classifier # noqa
from test_with_dask import run_empty_dmatrix_cls # noqa
from test_with_dask import _get_client_workers # noqa
@@ -216,7 +216,7 @@ def test_boost_from_prediction(local_cuda_cluster: LocalCUDACluster) -> None:
X_, y_ = load_digits(return_X_y=True)
X = dd.from_array(X_, chunksize=100).map_partitions(cudf.from_pandas)
y = dd.from_array(y_, chunksize=100).map_partitions(cudf.from_pandas)
run_boost_from_prediction_multi_clasas(X, y, "gpu_hist", client)
run_boost_from_prediction_multi_class(X, y, "gpu_hist", client)
class TestDistributedGPU:
@@ -231,7 +231,7 @@ class TestDistributedGPU:
num_rounds=strategies.integers(1, 20),
dataset=tm.dataset_strategy,
)
@settings(deadline=duration(seconds=120), suppress_health_check=suppress)
@settings(deadline=duration(seconds=120), suppress_health_check=suppress, print_blob=True)
@pytest.mark.skipif(**tm.no_cupy())
@pytest.mark.parametrize(
"local_cuda_cluster", [{"n_workers": 2}], indirect=["local_cuda_cluster"]