MLMachine¶
- class zvt.ml.ml.MLMachine(entity_ids: Optional[List[str]] = None, start_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = '2015-01-01', end_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = '2021-12-01', predict_start_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = '2021-06-01', predict_steps: int = 20, level: Union[zvt.contract.IntervalLevel, str] = IntervalLevel.LEVEL_1DAY, adjust_type: Optional[Union[zvt.contract.AdjustType, str]] = None, data_provider: Optional[str] = None, label_method: str = 'raw')¶
Bases:
object
- __init__(entity_ids: Optional[List[str]] = None, start_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = '2015-01-01', end_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = '2021-12-01', predict_start_timestamp: Union[str, pandas._libs.tslibs.timestamps.Timestamp] = '2021-06-01', predict_steps: int = 20, level: Union[zvt.contract.IntervalLevel, str] = IntervalLevel.LEVEL_1DAY, adjust_type: Optional[Union[zvt.contract.AdjustType, str]] = None, data_provider: Optional[str] = None, label_method: str = 'raw') None ¶
- Parameters
entity_ids –
start_timestamp –
end_timestamp –
predict_start_timestamp –
predict_steps –
level –
adjust_type –
data_provider –
label_method – raw, change, or behavior_cls
- build_feature(entity_ids: List[str], start_timestamp: pandas._libs.tslibs.timestamps.Timestamp, end_timestamp: pandas._libs.tslibs.timestamps.Timestamp) pandas.core.frame.DataFrame ¶
- result df format
col1 col2 col3 …
- entity_id timestamp
1.2 0.5 0.3 … 1.0 0.7 0.2 …
- Parameters
entity_ids – entity id list
start_timestamp –
end_timestamp –
- Return type
pd.DataFrame