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Drop saving binary format for memory snapshot. (#6513)

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This commit is contained in:
Jiaming Yuan
2020-12-17 00:14:57 +08:00
committed by GitHub
parent 0e97d97d50
commit c5876277a8
5 changed files with 23 additions and 103 deletions
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8
tests/python/test_pickling.py
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@@ -42,17 +42,9 @@ class TestPickling:
if os.path.exists(filename):
os.remove(filename)
def test_model_pickling_binary(self):
params = {
'nthread': 1,
'tree_method': 'hist'
}
self.run_model_pickling(params)
def test_model_pickling_json(self):
params = {
'nthread': 1,
'tree_method': 'hist',
'enable_experimental_json_serialization': True
}
self.run_model_pickling(params)

28
tests/python/test_training_continuation.py
View File

@@ -9,7 +9,7 @@ rng = np.random.RandomState(1337)
class TestTrainingContinuation:
num_parallel_tree = 3
def generate_parameters(self, use_json):
def generate_parameters(self):
xgb_params_01_binary = {
'nthread': 1,
}
@@ -24,13 +24,6 @@ class TestTrainingContinuation:
'num_class': 5,
'num_parallel_tree': self.num_parallel_tree
}
if use_json:
xgb_params_01_binary[
'enable_experimental_json_serialization'] = True
xgb_params_02_binary[
'enable_experimental_json_serialization'] = True
xgb_params_03_binary[
'enable_experimental_json_serialization'] = True
return [
xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary
@@ -136,31 +129,16 @@ class TestTrainingContinuation:
ntree_limit=gbdt_05.best_ntree_limit)
np.testing.assert_almost_equal(res1, res2)
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_binary(self):
params = self.generate_parameters(False)
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_json(self):
params = self.generate_parameters(True)
for p in params:
p['enable_experimental_json_serialization'] = True
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_updaters_binary(self):
updaters = 'grow_colmaker,prune,refresh'
params = self.generate_parameters(False)
for p in params:
p['updater'] = updaters
params = self.generate_parameters()
self.run_training_continuation(params[0], params[1], params[2])
@pytest.mark.skipif(**tm.no_sklearn())
def test_training_continuation_updaters_json(self):
# Picked up from R tests.
updaters = 'grow_colmaker,prune,refresh'
params = self.generate_parameters(True)
params = self.generate_parameters()
for p in params:
p['updater'] = updaters
self.run_training_continuation(params[0], params[1], params[2])