xgboost.dask.DaskScikitLearnBase Class Reference

Inheritance diagram for xgboost.dask.DaskScikitLearnBase:

Public Member Functions
Any	predict (self, _DataT X, bool output_margin=False, bool validate_features=True, Optional[_DaskCollection] base_margin=None, Optional[Tuple[int, int]] iteration_range=None)
Any	apply (self, _DataT X, Optional[Tuple[int, int]] iteration_range=None)
Awaitable[Any]	__await__ (self)
Dict	__getstate__ (self)
"distributed.Client"	client (self)
None	client (self, "distributed.Client" clt)
Public Member Functions inherited from xgboost.sklearn.XGBModel
None	__init__ (self, Optional[int] max_depth=None, Optional[int] max_leaves=None, Optional[int] max_bin=None, Optional[str] grow_policy=None, Optional[float] learning_rate=None, Optional[int] n_estimators=None, Optional[int] verbosity=None, SklObjective objective=None, Optional[str] booster=None, Optional[str] tree_method=None, Optional[int] n_jobs=None, Optional[float] gamma=None, Optional[float] min_child_weight=None, Optional[float] max_delta_step=None, Optional[float] subsample=None, Optional[str] sampling_method=None, Optional[float] colsample_bytree=None, Optional[float] colsample_bylevel=None, Optional[float] colsample_bynode=None, Optional[float] reg_alpha=None, Optional[float] reg_lambda=None, Optional[float] scale_pos_weight=None, Optional[float] base_score=None, Optional[Union[np.random.RandomState, int]] random_state=None, float missing=np.nan, Optional[int] num_parallel_tree=None, Optional[Union[Dict[str, int], str]] monotone_constraints=None, Optional[Union[str, Sequence[Sequence[str]]]] interaction_constraints=None, Optional[str] importance_type=None, Optional[str] device=None, Optional[bool] validate_parameters=None, bool enable_categorical=False, Optional[FeatureTypes] feature_types=None, Optional[int] max_cat_to_onehot=None, Optional[int] max_cat_threshold=None, Optional[str] multi_strategy=None, Optional[Union[str, List[str], Callable]] eval_metric=None, Optional[int] early_stopping_rounds=None, Optional[List[TrainingCallback]] callbacks=None, **Any kwargs)
bool	__sklearn_is_fitted__ (self)
Booster	get_booster (self)
"XGBModel"	set_params (self, **Any params)
Dict[str, Any]	get_params (self, bool deep=True)
Dict[str, Any]	get_xgb_params (self)
int	get_num_boosting_rounds (self)
None	save_model (self, Union[str, os.PathLike] fname)
None	load_model (self, ModelIn fname)
"XGBModel"	fit (self, ArrayLike X, ArrayLike y, *Optional[ArrayLike] sample_weight=None, Optional[ArrayLike] base_margin=None, Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] eval_set=None, Optional[Union[str, Sequence[str], Metric]] eval_metric=None, Optional[int] early_stopping_rounds=None, Optional[Union[bool, int]] verbose=True, Optional[Union[Booster, str, "XGBModel"]] xgb_model=None, Optional[Sequence[ArrayLike]] sample_weight_eval_set=None, Optional[Sequence[ArrayLike]] base_margin_eval_set=None, Optional[ArrayLike] feature_weights=None, Optional[Sequence[TrainingCallback]] callbacks=None)
Dict[str, Dict[str, List[float]]]	evals_result (self)
int	n_features_in_ (self)
np.ndarray	feature_names_in_ (self)
float	best_score (self)
int	best_iteration (self)
np.ndarray	feature_importances_ (self)
np.ndarray	coef_ (self)
np.ndarray	intercept_ (self)

Data Fields
	client
Data Fields inherited from xgboost.sklearn.XGBModel
	n_estimators
	objective
	max_depth
	max_leaves
	max_bin
	grow_policy
	learning_rate
	verbosity
	booster
	tree_method
	gamma
	min_child_weight
	max_delta_step
	subsample
	sampling_method
	colsample_bytree
	colsample_bylevel
	colsample_bynode
	reg_alpha
	reg_lambda
	scale_pos_weight
	base_score
	missing
	num_parallel_tree
	random_state
	n_jobs
	monotone_constraints
	interaction_constraints
	importance_type
	device
	validate_parameters
	enable_categorical
	feature_types
	max_cat_to_onehot
	max_cat_threshold
	multi_strategy
	eval_metric
	early_stopping_rounds
	callbacks
	kwargs
	n_classes_
	evals_result_

Protected Member Functions
Any	_predict_async (self, _DataT data, bool output_margin, bool validate_features, Optional[_DaskCollection] base_margin, Optional[Tuple[int, int]] iteration_range)
Any	_apply_async (self, _DataT X, Optional[Tuple[int, int]] iteration_range=None)
Any	_client_sync (self, Callable func, **Any kwargs)
Protected Member Functions inherited from xgboost.sklearn.XGBModel
Dict[str, bool]	_more_tags (self)
str	_get_type (self)
None	_load_model_attributes (self, dict config)
Tuple[ Optional[Union[Booster, str, "XGBModel"]], Optional[Metric], Dict[str, Any], Optional[int], Optional[Sequence[TrainingCallback]],]	_configure_fit (self, Optional[Union[Booster, "XGBModel", str]] booster, Optional[Union[Callable, str, Sequence[str]]] eval_metric, Dict[str, Any] params, Optional[int] early_stopping_rounds, Optional[Sequence[TrainingCallback]] callbacks)
DMatrix	_create_dmatrix (self, Optional[DMatrix] ref, **Any kwargs)
None	_set_evaluation_result (self, TrainingCallback.EvalsLog evals_result)
bool	_can_use_inplace_predict (self)
Tuple[int, int]	_get_iteration_range (self, Optional[Tuple[int, int]] iteration_range)

Protected Attributes
	_predict_async
	_apply_async
	_asynchronous
Protected Attributes inherited from xgboost.sklearn.XGBModel
	_Booster

Static Protected Attributes
	_client = None

Detailed Description

Base class for implementing scikit-learn interface with Dask

Member Function Documentation

_client_sync()

Any xgboost.dask.DaskScikitLearnBase._client_sync (   self, Callable  func, **Any  kwargs  )

protected

Get the correct client, when method is invoked inside a worker we
should use `worker_client' instead of default client.

apply()

Any xgboost.dask.DaskScikitLearnBase.apply	(		self,
		_DataT	X,
		Optional[Tuple[int, int]]	iteration_range = None
	)

Return the predicted leaf every tree for each sample. If the model is trained
with early stopping, then :py:attr:`best_iteration` is used automatically.

Parameters
----------
X : array_like, shape=[n_samples, n_features]
    Input features matrix.

iteration_range :
    See :py:meth:`predict`.

Returns
-------
X_leaves : array_like, shape=[n_samples, n_trees]
    For each datapoint x in X and for each tree, return the index of the
    leaf x ends up in. Leaves are numbered within
    ``[0; 2**(self.max_depth+1))``, possibly with gaps in the numbering.

Reimplemented from xgboost.sklearn.XGBModel.

client()

"distributed.Client" xgboost.dask.DaskScikitLearnBase.client

(

self

)

The dask client used in this model.  The `Client` object can not be serialized for
transmission, so if task is launched from a worker instead of directly from the
client process, this attribute needs to be set at that worker.

predict()

Any xgboost.dask.DaskScikitLearnBase.predict	(		self,
		_DataT	X,
		bool	output_margin = False,
		bool	validate_features = True,
		Optional[_DaskCollection]	base_margin = None,
		Optional[Tuple[int, int]]	iteration_range = None
	)

Predict with `X`.  If the model is trained with early stopping, then
:py:attr:`best_iteration` is used automatically. The estimator uses
`inplace_predict` by default and falls back to using :py:class:`DMatrix` if
devices between the data and the estimator don't match.

.. note:: This function is only thread safe for `gbtree` and `dart`.

Parameters
----------
X :
    Data to predict with.
output_margin :
    Whether to output the raw untransformed margin value.
validate_features :
    When this is True, validate that the Booster's and data's feature_names are
    identical.  Otherwise, it is assumed that the feature_names are the same.
base_margin :
    Margin added to prediction.
iteration_range :
    Specifies which layer of trees are used in prediction.  For example, if a
    random forest is trained with 100 rounds.  Specifying ``iteration_range=(10,
    20)``, then only the forests built during [10, 20) (half open set) rounds
    are used in this prediction.

    .. versionadded:: 1.4.0

Returns
-------
prediction

Reimplemented from xgboost.sklearn.XGBModel.

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The documentation for this class was generated from the following file:

• External/xgboost/python-package/xgboost/dask.py