def test_input_and_output_transformer_wrapper_should_not_return_a_different_amount_of_data_inputs_and_expected_outputs(
):
with pytest.raises(AssertionError):
p = InputAndOutputTransformerWrapper(ChangeLenDataInputs())
data_inputs, expected_outputs = _create_data_source((10, 10))
p.handle_transform(
DataContainer(data_inputs=data_inputs,
expected_outputs=expected_outputs),
ExecutionContext())
def test_input_and_output_transformer_wrapper_should_fit_with_data_inputs_and_expected_outputs_as_data_inputs(
):
tape = TapeCallbackFunction()
p = InputAndOutputTransformerWrapper(FitCallbackStep(tape))
data_inputs, expected_outputs = _create_data_source((10, 10))
p.fit(data_inputs, expected_outputs)
assert np.array_equal(tape.data[0][0][0], data_inputs)
assert np.array_equal(tape.data[0][0][1], expected_outputs)
def test_input_and_output_transformer_wrapper_should_raise_an_assertion_error_if_current_ids_have_not_been_resampled_correctly(
):
with pytest.raises(AssertionError) as e:
p = InputAndOutputTransformerWrapper(
ChangeLenDataInputsAndExpectedOutputs())
data_inputs, expected_outputs = _create_data_source((10, 10))
p.handle_transform(
DataContainer(data_inputs=data_inputs,
expected_outputs=expected_outputs),
ExecutionContext())
def test_data_doubler():
p = InputAndOutputTransformerWrapper(DoubleData())
data_inputs, expected_outputs = _create_data_source((10, 10))
out = p.handle_transform(
DataContainer(data_inputs=data_inputs,
expected_outputs=expected_outputs), ExecutionContext())
doubled_length = len(out.data_inputs)
assert doubled_length == 2 * len(data_inputs)
assert doubled_length == len(out.expected_outputs)
assert doubled_length == len(out.current_ids)
def test_input_and_output_transformer_wrapper_should_transform_with_data_inputs_and_expected_outputs(
):
p = InputAndOutputTransformerWrapper(MultiplyByNInputAndOutput(2))
data_inputs, expected_outputs = _create_data_source((10, 10))
data_container = p.handle_transform(
DataContainer(data_inputs=data_inputs,
expected_outputs=expected_outputs), ExecutionContext())
assert np.array_equal(data_container.data_inputs, data_inputs * 2)
assert np.array_equal(data_container.expected_outputs,
expected_outputs * 2)
def test_fit_transform_input_and_output_transformer_wrapper_with_hashlib_md5_value_hasher():
step = InputAndOutputTransformerWrapper(WindowTimeSeriesForOutputTransformerWrapper()) \
.set_hashers([HashlibMd5ValueHasher()])
step, data_container = step.handle_fit_transform(
data_container=DataContainer(
data_inputs=np.array(list(range(10))),
expected_outputs=np.array(list(range(10)))
),
context=ExecutionContext()
)
assert np.array_equal(data_container.data_inputs, np.array(list(range(0, 5))))
assert np.array_equal(data_container.expected_outputs, np.array(list(range(5, 10))))
def test_input_and_output_transformer_wrapper_should_fit_transform_with_data_inputs_and_expected_outputs(
):
tape = TapeCallbackFunction()
p = InputAndOutputTransformerWrapper(
Pipeline([MultiplyByNInputAndOutput(2),
FitCallbackStep(tape)]))
data_inputs, expected_outputs = _create_data_source((10, 10))
p, data_container = p.handle_fit_transform(
DataContainer(data_inputs=data_inputs,
expected_outputs=expected_outputs), ExecutionContext())
assert np.array_equal(data_container.data_inputs, data_inputs * 2)
assert np.array_equal(data_container.expected_outputs,
expected_outputs * 2)
assert np.array_equal(tape.data[0][0][0], data_inputs * 2)
assert np.array_equal(tape.data[0][0][1], expected_outputs * 2)