def test_local_evaluate_wrong():
data_node = DataNode()
data_node.self_private_test_data = 0
eval = data_node.local_evaluate('id')
assert eval is None
def test_set_params():
random_array = np.random.rand(30)
model_mock = Mock()
data_node = DataNode()
data_node.model = model_mock
data_node.set_model_params(random_array)
model_mock.set_model_params.assert_not_called()
copy_mock.set_model_params.assert_called_once_with(copy_mock)
def test_predict():
random_array = np.random.rand(30)
model_mock = Mock()
data_node = DataNode()
data_node.model = model_mock
data_node.predict(random_array)
model_mock.predict.assert_not_called()
copy_mock.predict.assert_called_once_with(random_array)
def test_evaluate():
data = np.random.rand(60).reshape((15, 4))
labels = np.random.randint(0, 2, 15)
data_node = DataNode()
data_node._model = Mock()
data_node.evaluate(data, labels)
data_node._model.evaluate.assert_called_once_with(data, labels)
def test_performance():
data_node = DataNode()
data_node._model = Mock()
data_node._model.performance.return_value = 0
data = np.random.rand(25).reshape((5, 5))
labels = np.random.randint(0, 2, 5)
res = data_node.performance(data, labels)
data_node._model.performance.assert_called_once_with(data, labels)
assert res == 0
def test_train_model_wrong_data():
random_array = np.random.rand(30)
data_node = DataNode()
model_mock = Mock()
data_node.model = model_mock
data_node.set_private_data("random_array", random_array)
with pytest.raises(ValueError):
data_node.train_model("random_array")
def test_query_private_data():
random_array = np.random.rand(30)
data_node = DataNode()
data_node.set_private_data("random_array", random_array)
data_node.configure_data_access("random_array", UnprotectedAccess())
data = data_node.query("random_array")
for i in range(len(random_array)):
assert data[i] == random_array[i]
def test_train_model_data():
random_array = np.random.rand(30)
random_array_labels = np.random.rand(30)
labeled_data = LabeledData(random_array, random_array_labels)
data_node = DataNode()
model_mock = Mock()
data_node.model = model_mock
data_node.set_private_data("random_array", labeled_data)
data_node.train_model("random_array")
model_mock.train.assert_not_called()
copy_mock.train.assert_called_once()
def test_query_model_params():
random_array = np.random.rand(30)
data_node = DataNode()
model_mock = Mock()
model_mock.get_model_params.return_value = random_array
data_node.model = model_mock
data_node.configure_model_params_access(UnprotectedAccess())
model_params = data_node.query_model_params()
for i in range(len(random_array)):
assert model_params[i] == random_array[i]
def test_local_evaluate():
data_key = 'EMNIST'
data_node = DataNode()
data_node._private_test_data = Mock()
data = Mock()
data.data = np.random.rand(60)
data.label = np.random.randint(0, 2, 60)
data_node._private_test_data.get.return_value = data
data_node._model = Mock()
data_node._model.evaluate.return_value = 0
data_node.self_private_test_data = 1
eval = data_node.local_evaluate(data_key)
assert eval == 0
data_node._private_test_data.get.assert_called_once_with(data_key)
data_node._model.evaluate.assert_called_once_with(data.data, data.label)
def test_get_model():
model_mock = Mock()
data_node = DataNode()
data_node.model = model_mock
assert data_node.model is None
def test_private_test_data():
random_array = np.random.rand(30)
data_node = DataNode()
data_node.set_private_test_data("random_array_test", random_array)
data_node.private_test_data