def fine_tune(self,
data: InputData,
iterations: int,
max_lead_time: timedelta = timedelta(minutes=5)):
"""
This method is used for hyperparameter searching
:param data: data used for hyperparameter searching
:param iterations: max number of iterations evaluable for hyperparameter optimization
:param max_lead_time: max time(seconds) for tuning evaluation
"""
self._init(data.task)
prepared_data = data.prepare_for_modelling(is_for_fit=True)
try:
fitted_model, tuned_params = self._eval_strategy.fit_tuned(
train_data=prepared_data,
iterations=iterations,
max_lead_time=max_lead_time)
if fitted_model is None:
raise ValueError(f'{self.model_type} can not be fitted')
self.params = tuned_params
if not self.params:
self.params = DEFAULT_PARAMS_STUB
except Exception as ex:
print(f'Tuning failed because of {ex}')
fitted_model = self._eval_strategy.fit(train_data=data)
self.params = DEFAULT_PARAMS_STUB
predict_train = self.predict(fitted_model, data)
return fitted_model, predict_train
def fit(self, data: InputData):
"""
This method is used for defining and running of the evaluation strategy
to train the model with the data provided
:param data: data used for model training
:return: tuple of trained model and prediction on train data
"""
self._init(data.task)
prepared_data = data.prepare_for_modelling(is_for_fit=True)
fitted_model = self._eval_strategy.fit(train_data=prepared_data)
predict_train = self.predict(fitted_model, data)
return fitted_model, predict_train
def predict(self,
fitted_model,
data: InputData,
output_mode: str = 'default'):
"""
This method is used for defining and running of the evaluation strategy
to predict with the data provided
:param fitted_model: trained model object
:param data: data used for prediction
"""
self._init(data.task, output_mode=output_mode)
prepared_data = data.prepare_for_modelling(is_for_fit=False)
prediction = self._eval_strategy.predict(trained_model=fitted_model,
predict_data=prepared_data)
prediction = _post_process_prediction_using_original_input(
prediction=prediction, input_data=data)
return prediction
