def test_predict_should_predict_in_test_mode():
tape_fit = TapeCallbackFunction()
tape_transform = TapeCallbackFunction()
p = Pipeline([
TestOnlyWrapper(
CallbackWrapper(MultiplyByN(2), tape_transform, tape_fit)),
TrainOnlyWrapper(
CallbackWrapper(MultiplyByN(4), tape_transform, tape_fit))
])
outputs = p.predict(np.array([1, 1]))
assert np.array_equal(outputs, np.array([2, 2]))
def test_predict_should_transform_with_initial_is_train_mode_after_predict():
tape_fit = TapeCallbackFunction()
tape_transform = TapeCallbackFunction()
p = Pipeline([
TestOnlyWrapper(
CallbackWrapper(MultiplyByN(2), tape_transform, tape_fit)),
TrainOnlyWrapper(
CallbackWrapper(MultiplyByN(4), tape_transform, tape_fit))
])
p.predict(np.array([1, 1]))
outputs = p.transform(np.array([1, 1]))
assert np.array_equal(outputs, np.array([4, 4]))
def test_handle_predict_should_predict_in_test_mode():
tape_fit = TapeCallbackFunction()
tape_transform = TapeCallbackFunction()
p = Pipeline([
TestOnlyWrapper(
CallbackWrapper(MultiplyByN(2), tape_transform, tape_fit)),
TrainOnlyWrapper(
CallbackWrapper(MultiplyByN(4), tape_transform, tape_fit))
])
data_container = p.handle_predict(data_container=DataContainer(
data_inputs=np.array([1, 1]), expected_outputs=np.array([1, 1])),
context=ExecutionContext())
assert np.array_equal(data_container.data_inputs, np.array([2, 2]))
DATA_INPUTS = np.array(range(5))
EXPECTED_OUTPUTS = np.array(range(5, 10))
EXPECTED_PROCESSED_OUTPUTS = np.array([5.0, 6.0, 7.0, 8.0, 9.0])
tape_transform_preprocessing = TapeCallbackFunction()
tape_fit_preprocessing = TapeCallbackFunction()
tape_transform_postprocessing = TapeCallbackFunction()
tape_fit_postprocessing = TapeCallbackFunction()
tape_inverse_transform_preprocessing = TapeCallbackFunction()
@pytest.mark.parametrize('test_case', [
NeuraxleTestCase(
pipeline=Pipeline([
ReversiblePreprocessingWrapper(
preprocessing_step=CallbackWrapper(MultiplyByN(2), tape_transform_preprocessing, tape_fit_postprocessing, tape_inverse_transform_preprocessing),
postprocessing_step=CallbackWrapper(AddN(10), tape_transform_postprocessing, tape_fit_postprocessing)
)]
),
callbacks=[tape_transform_preprocessing, tape_fit_preprocessing, tape_transform_postprocessing, tape_fit_postprocessing, tape_inverse_transform_preprocessing],
expected_callbacks_data=[
[DATA_INPUTS],
[],
[DATA_INPUTS * 2],
[],
[(DATA_INPUTS * 2) + 10]
],
data_inputs=DATA_INPUTS,
expected_processed_outputs=EXPECTED_PROCESSED_OUTPUTS,
execution_mode=ExecutionMode.TRANSFORM
),