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Remove experimental_json_serialization from tests. (#6640)

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This commit is contained in:
Jiaming Yuan
2021-01-27 17:44:49 +08:00
committed by GitHub
parent 8968ca7c0a
commit bc08e0c9d1
5 changed files with 9 additions and 22 deletions
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7
tests/python-gpu/test_gpu_prediction.py
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@@ -283,9 +283,10 @@ class TestGPUPredict:
y = (x0 * 10 - 20) + (x1 - 2)
dtrain = xgb.DMatrix(df, label=y, enable_categorical=True)
params = {'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor',
'enable_experimental_json_serialization': True,
'max_depth': 3, 'learning_rate': 1.0, 'base_score': 0.0, 'eval_metric': 'rmse'}
params = {
'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor',
'max_depth': 3, 'learning_rate': 1.0, 'base_score': 0.0, 'eval_metric': 'rmse'
}
eval_history = {}
bst = xgb.train(params, dtrain, num_boost_round=5, evals=[(dtrain, 'train')],

11
tests/python-gpu/test_gpu_training_continuation.py
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@@ -6,15 +6,14 @@ rng = np.random.RandomState(1994)
class TestGPUTrainingContinuation:
def run_training_continuation(self, use_json):
def test_training_continuation(self):
kRows = 64
kCols = 32
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
dtrain = xgb.DMatrix(X, y)
params = {'tree_method': 'gpu_hist', 'max_depth': '2',
'gamma': '0.1', 'alpha': '0.01',
'enable_experimental_json_serialization': use_json}
'gamma': '0.1', 'alpha': '0.01'}
bst_0 = xgb.train(params, dtrain, num_boost_round=64)
dump_0 = bst_0.get_dump(dump_format='json')
@@ -48,9 +47,3 @@ class TestGPUTrainingContinuation:
obj_0 = json.loads(dump_0[i])
obj_1 = json.loads(dump_1[i])
recursive_compare(obj_0, obj_1)
def test_gpu_training_continuation_binary(self):
self.run_training_continuation(False)
def test_gpu_training_continuation_json(self):
self.run_training_continuation(True)

4
tests/python-gpu/test_gpu_updaters.py
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@@ -63,9 +63,7 @@ class TestGPUUpdaters:
by_etl_results = {}
by_builtin_results = {}
parameters = {'tree_method': 'gpu_hist',
'predictor': 'gpu_predictor',
'enable_experimental_json_serialization': True}
parameters = {'tree_method': 'gpu_hist', 'predictor': 'gpu_predictor'}
m = xgb.DMatrix(onehot, label, enable_categorical=True)
xgb.train(parameters, m,