secretflow.ml.boost.ss_xgb_v#

Classes:

`XgbModel`(spu, objective, base)	SS Xgb Model & predict.
`Xgb`(spu)	This method provides both classification and regression tree boosting (also known as GBDT, GBM) for vertical split dataset setting by using secret sharing.

class secretflow.ml.boost.ss_xgb_v.XgbModel(spu: SPU, objective: RegType, base: float)[源代码]#

基类：object

SS Xgb Model & predict.

Methods:

`__init__`(spu, objective, base)
`predict`(dtrain[, to_pyu])	predict on dtrain with this model.

__init__(spu: SPU, objective: RegType, base: float) → None[源代码]#

predict(dtrain: Union[FedNdarray, VDataFrame], to_pyu: Optional[PYU] = None) → Union[SPUObject, FedNdarray][源代码]#

predict on dtrain with this model.

参数:

dtrain – [FedNdarray, VDataFrame] vertical split dataset.
to – the prediction initiator if not None predict result is reveal to to_pyu device and save as FedNdarray otherwise, keep predict result in secret and save as SPUObject.

返回:

Pred values store in spu object or FedNdarray.

class secretflow.ml.boost.ss_xgb_v.Xgb(spu: Union[SPU, List[SPU]])[源代码]#

基类：object

This method provides both classification and regression tree boosting (also known as GBDT, GBM) for vertical split dataset setting by using secret sharing.

SS-XGB is short for secret sharing XGB. more details: https://arxiv.org/pdf/2005.08479.pdf

参数:: spu – secret device running MPC protocols

Methods:

`__init__`(spu)
`train`(params, dtrain, label)	train on dtrain and label.

__init__(spu: Union[SPU, List[SPU]]) → None[源代码]#

train(params: Dict, dtrain: Union[FedNdarray, VDataFrame], label: Union[FedNdarray, VDataFrame]) → XgbModel[源代码]#

train on dtrain and label.

参数:

dtrain – {FedNdarray, VDataFrame} vertical split dataset.
label – {FedNdarray, VDataFrame} label column.
params – Dict booster params, details are as follows

booster params details:

num_boost_roundint, default=10
Number of boosting iterations. range: [1, 1024]

‘max_depth’: Maximum depth of a tree.
default: 5 range: [1, 16]

‘learning_rate’: Step size shrinkage used in update to prevents overfitting.
default: 0.3 range: (0, 1]

‘objective’: Specify the learning objective.
default: ‘logistic’ range: [‘linear’, ‘logistic’]

‘reg_lambda’: L2 regularization term on weights.
default: 0.1 range: [0, 10000]

‘subsample’: Subsample ratio of the training instances.
default: 1 range: (0, 1]

‘colsample_bytree’: Subsample ratio of columns when constructing each tree.
default: 1 range: (0, 1]

‘sketch_eps’: This roughly translates into O(1 / sketch_eps) number of bins.
default: 0.1 range: (0, 1]

‘base_score’: The initial prediction score of all instances, global bias.
default: 0

‘seed’: Pseudorandom number generator seed.
default: 42

返回:: XgbModel

secretflow.ml.boost.ss_xgb_v.core