secretflow.ml.boost.homo_boost.boost_core#

secretflow.ml.boost.homo_boost.boost_core.callback#

Classes:

FedCallbackContainer(callbacks[, metric, is_cv])

Federate version, encapsulates xgboostCallbackContainer.

class secretflow.ml.boost.homo_boost.boost_core.callback.FedCallbackContainer(callbacks: List[TrainingCallback], metric: Optional[Callable] = None, is_cv: bool = False)[源代码]#

基类：CallbackContainer

Federate version, encapsulates xgboostCallbackContainer. .. attribute:: callbacks

list of training callback functions

metric#: eval function callable

is_cv#: whether to do cross validation

Attributes:

EvalsLog(*args, **kwargs)

Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]] 的别名

Methods:

`__init__`(callbacks[, metric, is_cv])
`key`(name)
`after_iteration`(model, epoch, dtrain, evals)	A function to call after training iterations.

EvalsLog(*args, **kwargs)#: Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]] 的别名

__init__(callbacks: List[TrainingCallback], metric: Optional[Callable] = None, is_cv: bool = False)[源代码]#

key(name: str) → str[源代码]#

after_iteration(model, epoch, dtrain, evals) → bool[源代码]#

A function to call after training iterations. :param model: xgboost booster object, which stores training parameters and states :param epoch: number of iteration rounds :param dtrain: DMatrix xgboost format training data :param evals: List[(DMatrix, string)] List of data to evaluate

返回:: Whether the training should be terminated, if the callbacks are successfully executed, return true (eg: EarlyStop returns True, the training is terminated early)
返回类型:: ret

history: Dict[str, Dict[str, Union[List[float], List[Tuple[float, float]]]]]#

secretflow.ml.boost.homo_boost.boost_core.core#

Classes:

FedBooster([params, cache, model_file])

Federated Booster internal Internal implementation, it is not recommended for users to call directly! ! !

class secretflow.ml.boost.homo_boost.boost_core.core.FedBooster(params: Optional[Dict] = None, cache: List = (), model_file: Optional[Union[str, PathLike, Booster, bytearray]] = None)[源代码]#

基类：Booster

Federated Booster internal Internal implementation, it is not recommended for users to call directly! ! !

params#: Parameters for boosters.

cache#: List of cache items.

model_file#: Path to the model file if it’s string or PathLike.

Methods:

__init__([params, cache, model_file])

param params:: Parameters for boosters.

federate_update(params, dtrain, hdata, ...)

federated update function, a variant in xgboost update :param params: Training params dict :param dtrain: Training data in dmatrix format :param hdata: Training data in HdataFrame format :param bin_split_points: Global split point :param iter_round: Iteration rounds :param fobj: Custom evaluation function

save_model(fname)

Save the model to a file.

__init__(params: Optional[Dict] = None, cache: List = (), model_file: Optional[Union[str, PathLike, Booster, bytearray]] = None)[源代码]#

参数:

params (dict) – Parameters for boosters.
cache (list) – List of cache items.
model_file (string/os.PathLike/Booster/bytearray) – Path to the model file if it’s string or PathLike.

federate_update(params: Dict, dtrain: DMatrix, hdata: HDataFrame, bin_split_points: List, iter_round: Optional[int] = None, fobj: Optional[Callable] = None)[源代码]#: federated update function, a variant in xgboost update :param params: Training params dict :param dtrain: Training data in dmatrix format :param hdata: Training data in HdataFrame format :param bin_split_points: Global split point :param iter_round: Iteration rounds :param fobj: Custom evaluation function

save_model(fname: Union[str, PathLike])[源代码]#

Save the model to a file.

fname#: string or os.PathLike, model path, if the suffix is json, store the model in json format

secretflow.ml.boost.homo_boost.boost_core.training#

Training Library containing training routines.

Functions:

train(params, dtrain, hdata, bin_split_points)

Specifies the parameter training level federated version of xgboost.

secretflow.ml.boost.homo_boost.boost_core.training.train(params: Dict, dtrain: DMatrix, hdata: HDataFrame, bin_split_points: List, num_boost_round: int = 10, evals: List = (), obj: Optional[Callable[[ndarray, DMatrix], Tuple[ndarray, ndarray]]] = None, feval: Optional[Callable[[ndarray, DMatrix], Tuple[str, float]]] = None, maximize: Optional[bool] = None, early_stopping_rounds: Optional[int] = None, evals_result: Optional[Dict] = None, verbose_eval: Union[bool, int] = True, xgb_model: Optional[Union[str, PathLike, Booster, bytearray]] = None, callbacks: Optional[Sequence[TrainingCallback]] = None, custom_metric: Optional[Callable[[ndarray, DMatrix], Tuple[str, float]]] = None)[源代码]#

Specifies the parameter training level federated version of xgboost.

参数:

params – Booster parameters. Reference: https://xgboost.readthedocs.io/en/latest/parameter.html
dtrain – The training data passed in in DMatrix format.
hdata – The training data passed in in HDataFrame format, the content is consistent with dtrain
bin_split_points – The global equal-frequency binning of each feature is recorded
num_boost_round – Number of iteration rounds.
evals – A list of validation sets to evaluate during training to help us monitor training effects during training.
obj – custom objective function.
feval – Custom eval function.
maximize – Whether the function in feval is optimized in the maximize direction.
early_stopping_rounds – used to activate the early_stop strategy, the eval metric of the validation set needs to be executed every early stop round Raised at least once. Correspondingly, at least one item must be added to evals. If there are multiple items in eval metric, then the last item The indicator will be used for early stop strategy
evals_result – The evals_result dictionary, used to store the evaluation (eval) results of all items in the watch list.
verbose_eval – If verbose_eval is True, the evaluation metrics on the validation set will be printed out at each iteration, and if verbose_eval is true If it is an int, the evaluation metric of the validation set will be printed after each verbose_eval* iterations
xgb_model – The trained xgb model can transfer the path or the loaded model for relay training or breakpoint retraining
callbacks – a list of callback functions that will be applied to each iteration of training
custom_metric – Custom metric function. See Custom Metric for details.

返回:

a trained booster model

返回类型:

Booster