advanced_supvervised_model_trainer.py 文件源码

def lasso_regression(self, scoring_metric='neg_mean_squared_error',
                         hyperparameter_grid=None,
                         randomized_search=True,
                         number_iteration_samples=2):
        """
        A light wrapper for Sklearn's lasso regression that performs randomized search over an overridable default
        hyperparameter grid.

        Args:
            scoring_metric (str): Any sklearn scoring metric appropriate for regression
            hyperparameter_grid (dict): hyperparameters by name
            randomized_search (bool): True for randomized search (default)
            number_iteration_samples (int): Number of models to train during the randomized search for exploring the
                hyperparameter space. More may lead to a better model, but will take longer.

        Returns:
            TrainedSupervisedModel:
        """
        self.validate_regression('Lasso Regression')
        if hyperparameter_grid is None:
            hyperparameter_grid = {"fit_intercept": [True, False]}
            number_iteration_samples = 2

        algorithm = get_algorithm(Lasso,
                                  scoring_metric,
                                  hyperparameter_grid,
                                  randomized_search,
                                  number_iteration_samples=number_iteration_samples)

        trained_supervised_model = self._create_trained_supervised_model(algorithm)

        return trained_supervised_model