qlib量化源码分析：qlib/qlib/contrib/model/gbdt.py

qlib gbd（lightgbm)使用了很类似sklearn的接口，dataset.prepare里返回了训练集与测试集。

看GBDT模块的代码，它先调用了_prepare_data，使用数据集dataset.prepare函数，取得train,valid两个部分，然后进行基本的数据检查和转换，以适配模型。

 

def _prepare_data(self, dataset: DatasetH, reweighter=None) -> List[Tuple[lgb.Dataset, str]]:
        """
        The motivation of current version is to make validation optional
        - train segment is necessary;
        """
        ds_l = []
        assert "train" in dataset.segments
        for key in ["train", "valid"]:
            if key in dataset.segments:
                df = dataset.prepare(key, col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
                if df.empty:
                    raise ValueError("Empty data from dataset, please check your dataset config.")
                x, y = df["feature"], df["label"]

                # Lightgbm need 1D array as its label
                if y.values.ndim == 2 and y.values.shape[1] == 1:
                    y = np.squeeze(y.values)
                else:
                    raise ValueError("LightGBM doesn't support multi-label training")

                if reweighter is None:
                    w = None
                elif isinstance(reweighter, Reweighter):
                    w = reweighter.reweight(df)
                else:
                    raise ValueError("Unsupported reweighter type.")
                ds_l.append((lgb.Dataset(x.values, label=y, weight=w), key))
        return ds_l