def _fit_data_container(self, data_container: DataContainer, context: ExecutionContext) -> BaseStep:
assert self.wrapped is not None
step = StepClonerForEachDataInput(self.wrapped)
step = step.handle_fit(data_container, context)
return step
def _fit_data_container(self, data_container: DataContainer, context: ExecutionContext) -> BaseStep:
assert self.wrapped is not None
if self.split_data_container_during_fit:
train_data_container, validation_data_container = self.split_data_container(data_container)
else:
train_data_container = data_container
step = StepClonerForEachDataInput(self.wrapped)
step = step.handle_fit(train_data_container, context)
if self.predict_after_fit:
results = step.handle_predict(validation_data_container, context)
self.calculate_score(results)
return self
def _fit_data_container(self, data_container: DataContainer,
context: ExecutionContext) -> BaseStep:
assert self.wrapped is not None
train_data_container, validation_data_container = self.split_data_container(
data_container)
step = StepClonerForEachDataInput(self.wrapped)
step = step.handle_fit(train_data_container, context)
results = step.handle_transform(validation_data_container, context)
self.scores = [
self.scoring_function(a, b)
for a, b in zip(results.data_inputs, results.expected_outputs)
]
self.scores_mean = np.mean(self.scores)
self.scores_std = np.std(self.scores)
return self
def train(self, train_data_container: DataContainer,
context: ExecutionContext):
step = StepClonerForEachDataInput(self.wrapped)
step = step.handle_fit(train_data_container, context)
return step