最近使用了LightBGM的Dataset，记录一下：
1.说明： classlightgbm.Dataset(data, label=None, reference=None, weight=None, group=None, init_score=None, silent=False, feature_name='auto', categorical_feature='auto', params=None, free_raw_data=True)
Bases: object
Dataset in LightGBM.
Constract Dataset.
| Parameters: |
- data (string, numpy array, pandas DataFrame, scipy.sparse or list of numpy arrays) – Data source of Dataset. If string, it represents the path to txt file.
- label (list, numpy 1-D array, pandas one-column DataFrame/Series or None, optional (default=None)) – Label of the data.
- reference (Dataset or None, optional (default=None)) – If this is Dataset for validation, training data should be used as reference.
- weight (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Weight for each instance.
- group (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Group/query size for Dataset.
- init_score (list, numpy 1-D array, pandas Series or None, optional (default=None)) – Init score for Dataset.
- silent (bool, optional (default=False)) – Whether to print messages during construction.
- feature_name (list of strings or 'auto', optional (default="auto")) – Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.
- categorical_feature (list of strings or int, or 'auto', optional (default="auto")) – Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names (need to specify
feature_name as well). If ‘auto’ and data is pandas DataFrame, pandas categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). All negative values in categorical features will be treated as missing values.
- params (dict or None, optional (default=None)) – Other parameters.
- free_raw_data (bool, optional (default=True)) – If True, raw data is freed after constructing inner Dataset.
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输出是一个dataset对象
2.使用：
根据说明使用自己的数据，我这里data和label都用了DataFrame格式的