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[python][tests] transfer test_save_and_load_linear to test_engine #3821

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Jan 23, 2021
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[python][tests] transfer test_save_and_load_linear to test_engine #3821

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25 changes: 0 additions & 25 deletions tests/python_package_test/test_basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,31 +110,6 @@ def test_chunked_dataset_linear():
valid_data.construct()


def test_save_and_load_linear(tmp_path):
X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(return_X_y=True), test_size=0.1,
random_state=2)
X_train = np.concatenate([np.ones((X_train.shape[0], 1)), X_train], 1)
X_train[:X_train.shape[0] // 2, 0] = 0
y_train[:X_train.shape[0] // 2] = 1
params = {'linear_tree': True}
train_data_1 = lgb.Dataset(X_train, label=y_train, params=params)
est_1 = lgb.train(params, train_data_1, num_boost_round=10, categorical_feature=[0])
pred_1 = est_1.predict(X_train)

tmp_dataset = str(tmp_path / 'temp_dataset.bin')
train_data_1.save_binary(tmp_dataset)
train_data_2 = lgb.Dataset(tmp_dataset)
est_2 = lgb.train(params, train_data_2, num_boost_round=10)
pred_2 = est_2.predict(X_train)
np.testing.assert_allclose(pred_1, pred_2)

model_file = str(tmp_path / 'model.txt')
est_2.save_model(model_file)
est_3 = lgb.Booster(model_file=model_file)
pred_3 = est_3.predict(X_train)
np.testing.assert_allclose(pred_2, pred_3)


def test_subset_group():
X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
'../../examples/lambdarank/rank.train'))
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25 changes: 25 additions & 0 deletions tests/python_package_test/test_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2566,6 +2566,31 @@ def test_linear_trees(tmp_path):
est = lgb.train(params, train_data, num_boost_round=10, categorical_feature=[0])


def test_save_and_load_linear(tmp_path):
X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(return_X_y=True), test_size=0.1,
random_state=2)
X_train = np.concatenate([np.ones((X_train.shape[0], 1)), X_train], 1)
X_train[:X_train.shape[0] // 2, 0] = 0
y_train[:X_train.shape[0] // 2] = 1
params = {'linear_tree': True}
train_data_1 = lgb.Dataset(X_train, label=y_train, params=params)
est_1 = lgb.train(params, train_data_1, num_boost_round=10, categorical_feature=[0])
pred_1 = est_1.predict(X_train)

tmp_dataset = str(tmp_path / 'temp_dataset.bin')
train_data_1.save_binary(tmp_dataset)
train_data_2 = lgb.Dataset(tmp_dataset)
est_2 = lgb.train(params, train_data_2, num_boost_round=10)
pred_2 = est_2.predict(X_train)
np.testing.assert_allclose(pred_1, pred_2)

model_file = str(tmp_path / 'model.txt')
est_2.save_model(model_file)
est_3 = lgb.Booster(model_file=model_file)
pred_3 = est_3.predict(X_train)
np.testing.assert_allclose(pred_2, pred_3)


def test_predict_with_start_iteration():
def inner_test(X, y, params, early_stopping_rounds):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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