def test_step_cloner_should_set_steps_hyperparams_space():
p = StepClonerForEachDataInput(SomeStep())
p.fit_transform([[0, 0]])
p.set_hyperparams_space(
HyperparameterSpace({
META_STEP_HP: RAND_INT_STEP_CLONER,
SOME_STEP_HP: RAND_INT_SOME_STEP
}))
assert isinstance(p.steps[0].hyperparams_space, HyperparameterSpace)
assert p.steps[0].hyperparams_space[SOME_STEP_HP_KEY] == RAND_INT_SOME_STEP
def test_step_cloner_should_set_steps_hyperparams():
p = StepClonerForEachDataInput(SomeStep())
p.fit_transform([[0, 0]])
p.set_hyperparams(
HyperparameterSamples({
META_STEP_HP: META_STEP_HP_VALUE,
SOME_STEP_HP: SOME_STEP_HP_VALUE
}))
assert isinstance(p.hyperparams, HyperparameterSamples)
assert isinstance(p.steps[0].hyperparams, HyperparameterSamples)
assert p.steps[0].get_hyperparams()[SOME_STEP_HP_KEY] == SOME_STEP_HP_VALUE
def test_step_cloner_should_fit_transform():
# Given
tape = TapeCallbackFunction()
p = StepClonerForEachDataInput(
Pipeline([FitCallbackStep(tape), MultiplyByN(2)]))
data_inputs = _create_data((2, 2))
expected_outputs = _create_data((2, 2))
# When
p, processed_outputs = p.fit_transform(data_inputs, expected_outputs)
# Then
assert isinstance(p.steps[0], Pipeline)
assert np.array_equal(p.steps[0][0].callback_function.data[0][0],
data_inputs[0])
assert np.array_equal(p.steps[0][0].callback_function.data[0][1],
expected_outputs[0])
assert isinstance(p.steps[1], Pipeline)
assert np.array_equal(p.steps[1][0].callback_function.data[0][0],
data_inputs[1])
assert np.array_equal(p.steps[1][0].callback_function.data[0][1],
expected_outputs[1])
assert np.array_equal(processed_outputs, data_inputs * 2)
def test_should_inverse_transform():
step_cloner = StepClonerForEachDataInput(SomeStepInverseTransform())
step_cloner, processed_outputs = step_cloner.fit_transform([0])
step_cloner = step_cloner.reverse()
processed_outputs = step_cloner.inverse_transform(processed_outputs)
assert processed_outputs == ['inverse_transform']
def test_should_fit_transform():
some_step = SomeStepInverseTransform()
step_cloner = StepClonerForEachDataInput(some_step)
step_cloner, processed_outputs = step_cloner.fit_transform([0])
assert isinstance(step_cloner.steps[0], SomeStepInverseTransform)
assert processed_outputs == ['fit_transform']
def test_step_cloner_should_save_sub_steps(tmpdir):
tape = TapeCallbackFunction()
p = StepClonerForEachDataInput(Pipeline(
[FitCallbackStep(tape), MultiplyByN(2)]),
cache_folder_when_no_handle=tmpdir)
data_inputs = _create_data((2, 2))
expected_outputs = _create_data((2, 2))
p, processed_outputs = p.fit_transform(data_inputs, expected_outputs)
p.save(ExecutionContext(tmpdir), full_dump=True)
saved_paths = [
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[0]/FitCallbackStep/FitCallbackStep.joblib'
),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[0]/MultiplyByN/MultiplyByN.joblib'
),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[0]/MultiplyByN/MultiplyByN.joblib'
),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[0]/Pipeline[0].joblib'),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[1]/FitCallbackStep/FitCallbackStep.joblib'
),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[1]/MultiplyByN/MultiplyByN.joblib'
),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline[1]/Pipeline[1].joblib'),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline/FitCallbackStep/FitCallbackStep.joblib'
),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/Pipeline/MultiplyByN/MultiplyByN.joblib'
),
os.path.join(tmpdir,
'StepClonerForEachDataInput/Pipeline/Pipeline.joblib'),
os.path.join(
tmpdir,
'StepClonerForEachDataInput/StepClonerForEachDataInput.joblib')
]
for p in saved_paths:
assert os.path.exists(p)
def test_step_cloner_should_set_train():
tape = TapeCallbackFunction()
p = StepClonerForEachDataInput(
Pipeline([FitCallbackStep(tape), MultiplyByN(2)]))
data_inputs = _create_data((2, 2))
expected_outputs = _create_data((2, 2))
p, processed_outputs = p.fit_transform(data_inputs, expected_outputs)
p.set_train(False)
assert not p.is_train
assert not p.steps_as_tuple[0][1].is_train
assert not p.steps_as_tuple[1][1].is_train
def test_step_cloner_should_inverse_transform():
tape = TapeCallbackFunction()
p = StepClonerForEachDataInput(
Pipeline([FitCallbackStep(tape), MultiplyByN(2)]))
data_inputs = _create_data((2, 2))
expected_outputs = _create_data((2, 2))
p, processed_outputs = p.fit_transform(data_inputs, expected_outputs)
p = p.reverse()
assert np.array_equal(processed_outputs, data_inputs * 2)
inverse_processed_outputs = p.inverse_transform(processed_outputs)
assert np.array_equal(np.array(inverse_processed_outputs),
np.array(data_inputs))
def test_step_cloner_should_load_sub_steps(tmpdir):
tape = TapeCallbackFunction()
p = StepClonerForEachDataInput(Pipeline(
[FitCallbackStep(tape), MultiplyByN(2)]),
cache_folder_when_no_handle=tmpdir)
data_inputs = _create_data((2, 2))
expected_outputs = _create_data((2, 2))
p, processed_outputs = p.fit_transform(data_inputs, expected_outputs)
p.save(ExecutionContext(tmpdir), full_dump=True)
loaded_step_cloner = ExecutionContext(tmpdir).load(
'StepClonerForEachDataInput')
assert isinstance(loaded_step_cloner.wrapped, Pipeline)
assert len(loaded_step_cloner.steps_as_tuple) == len(data_inputs)
assert isinstance(loaded_step_cloner.steps_as_tuple[0][1], Pipeline)
assert isinstance(loaded_step_cloner.steps_as_tuple[1][1], Pipeline)