def test_step_cloner_should_transform():
tape = TapeCallbackFunction()
p = StepClonerForEachDataInput(
Pipeline([FitCallbackStep(tape), MultiplyByN(2)]))
data_inputs = _create_data((2, 2))
processed_outputs = p.transform(data_inputs)
assert isinstance(p.steps[0], Pipeline)
assert isinstance(p.steps[1], Pipeline)
assert np.array_equal(processed_outputs, data_inputs * 2)
def fit(self, data_inputs, expected_outputs=None) -> 'BaseCrossValidationWrapper':
assert self.wrapped is not None
train_data_inputs, train_expected_outputs, validation_data_inputs, validation_expected_outputs = self.split(
data_inputs, expected_outputs)
step = StepClonerForEachDataInput(self.wrapped)
step = step.fit(train_data_inputs, train_expected_outputs)
results = step.transform(validation_data_inputs)
self.scores = [self.scoring_function(a, b) for a, b in zip(results, validation_expected_outputs)]
self.scores_mean = np.mean(self.scores)
self.scores_std = np.std(self.scores)
return self