def METHOD_NAME():
    fd = FileLoader(EXAMPLES_DIR / 'lambdarank', 'rank')
    X_train, y_train, _ = fd.load_dataset('.train', is_sparse=True)
    X_test, _, X_test_fn = fd.load_dataset('.test', is_sparse=True)
    group_train = fd.load_field('.train.query')
    lgb_train = lgb.Dataset(X_train, y_train, group=group_train)
    params = dict(fd.params)
    params['force_col_wise'] = True
    gbm = lgb.LGBMRanker(**params)
    gbm.fit(X_train, y_train, group=group_train)
    sk_pred = gbm.predict(X_test)
    fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred)
    fd.file_load_check(lgb_train, '.train')