advanced_supvervised_model_trainer.py 文件源码

def knn(self,
            scoring_metric='roc_auc',
            hyperparameter_grid=None,
            randomized_search=True,
            number_iteration_samples=10):
        """
        A light wrapper for Sklearn's knn classifier that performs randomized search over an overridable default
        hyperparameter grid.

        Args:
            scoring_metric (str): Any sklearn scoring metric appropriate for classification
            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_classification('KNN')
        if hyperparameter_grid is None:
            neighbors = list(range(5, 26))
            hyperparameter_grid = {'n_neighbors': neighbors, 'weights': ['uniform', 'distance']}
            number_iteration_samples = 10

            print('KNN Grid: {}'.format(hyperparameter_grid))
        algorithm = get_algorithm(KNeighborsClassifier,
                                  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