def test_load_pkl(self):
'''Test whether prediction is correct.'''
assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1'
bst = load_pickle(model_path)
x, y = build_dataset()
test_x = xgb.DMatrix(x)
res = bst.predict(test_x)
assert len(res) == 10
def test_wrap_gpu_id(self):
assert os.environ['CUDA_VISIBLE_DEVICES'] == '0'
bst = load_pickle(model_path)
config = bst.save_config()
config = json.loads(config)
assert config['learner']['generic_param']['gpu_id'] == '0'
x, y = build_dataset()
test_x = xgb.DMatrix(x)
res = bst.predict(test_x)
assert len(res) == 10
def test_load_pkl(self):
'''Test whether prediction is correct.'''
assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1'
bst = load_pickle(model_path)
x, y = build_dataset()
if isinstance(bst, xgb.Booster):
test_x = xgb.DMatrix(x)
res = bst.predict(test_x)
else:
res = bst.predict(x)
assert len(res) == 10
bst.set_params(n_jobs=1) # triggers a re-configuration
res = bst.predict(x)
assert len(res) == 10