rice_run.py 文件源码

def knn(train_sample, validation_sample, features, seed):
    log_base = np.e
    knn_est = KNeighborsRegressor(n_neighbors=1, weights='distance', algorithm='auto', leaf_size=30,
                                  p=1).fit(
        train_sample[features], np.log1p(train_sample['volume']) / np.log(log_base))
    knn_prob = np.power(log_base, knn_est.predict(validation_sample[features])) - 1
    print_mape(validation_sample['volume'], knn_prob, 'KNN')
    return knn_prob