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_predictor_type_is_gpu(self): '''When CUDA_VISIBLE_DEVICES is not specified, keep using `gpu_predictor`''' assert 'CUDA_VISIBLE_DEVICES' not in os.environ.keys() bst = load_pickle(model_path) config = bst.save_config() config = json.loads(config) assert config['learner']['gradient_booster']['gbtree_train_param'][ 'predictor'] == 'gpu_predictor'
def test_predictor_type_is_auto(self): '''Under invalid CUDA_VISIBLE_DEVICES, predictor should be set to auto''' assert os.environ['CUDA_VISIBLE_DEVICES'] == '-1' bst = load_pickle(model_path) config = bst.save_config() config = json.loads(config) assert config['learner']['gradient_booster']['gbtree_train_param'][ 'predictor'] == 'auto'
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