sampling.py 文件源码

def k_fold_sample_data_set(x, y, folds):
    """
    This function uses a k-fold approach as a re-sampling strategy
    :param x: numpy array
        - Includes the train data
    :param y: numpy array
        - Includes the actual value of each data sample
    :param folds: integer
        - The number of folds that splits the data set
    :return: list of lists
        - The training and test samples extracted from the training set
    """
    x_train_list, y_train_list, x_test_list, y_test_list = list(), list(), list(), list()
    try:
        kf = KFold(x.shape[0], n_folds=folds, shuffle=True)
        for train_index, test_index in kf:
            x_train_list.append(x[train_index])
            y_train_list.append(y[train_index])
            x_test_list.append(x[test_index])
            y_test_list.append(y[test_index])
        return x_train_list, y_train_list, x_test_list, y_test_list
    except AttributeError as e:
        print(e.args, "- Please, use numpy arrays as inputs")
        exit()