Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Mitigate flaky tests. (#7749)

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

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

@@ -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