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