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[CI] Refactor tests to reduce CI time. (#8312)

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This commit is contained in:
Rory Mitchell
2022-10-12 11:32:06 +02:00
committed by GitHub
parent 39afdac3be
commit ce0382dcb0
19 changed files with 95 additions and 122 deletions
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11
tests/python-gpu/test_gpu_prediction.py
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@@ -32,6 +32,7 @@ predict_parameter_strategy = strategies.fixed_dictionaries({
'num_parallel_tree': strategies.sampled_from([1, 4]),
})
pytestmark = pytest.mark.timeout(20)
class TestGPUPredict:
def test_predict(self):
@@ -264,7 +265,7 @@ class TestGPUPredict:
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_shap(self, num_rounds, dataset, param):
if dataset.name.endswith("-l1"): # not supported by the exact tree method
return
@@ -280,7 +281,7 @@ class TestGPUPredict:
@given(strategies.integers(1, 10),
tm.dataset_strategy, shap_parameter_strategy)
@settings(deadline=None, max_examples=20, print_blob=True)
@settings(deadline=None, max_examples=10, print_blob=True)
def test_shap_interactions(self, num_rounds, dataset, param):
if dataset.name.endswith("-l1"): # not supported by the exact tree method
return
@@ -333,14 +334,14 @@ class TestGPUPredict:
np.testing.assert_equal(cpu_leaf, gpu_leaf)
@given(predict_parameter_strategy, tm.dataset_strategy)
@settings(deadline=None, print_blob=True)
@settings(deadline=None, max_examples=20, 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, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_predict_leaf_dart(self, param, dataset):
param['booster'] = 'dart'
param['tree_method'] = 'gpu_hist'
@@ -351,7 +352,7 @@ 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, print_blob=True)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_predict_categorical_split(self, df):
from sklearn.metrics import mean_squared_error