Inheritance diagram for xgboost.dask.DaskXGBClassifier:

Public Member Functions
"DaskXGBClassifier"	fit (self, _DataT X, _DaskCollection y, *Optional[_DaskCollection] sample_weight=None, Optional[_DaskCollection] base_margin=None, Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] eval_set=None, Optional[Union[str, Sequence[str], Callable]] eval_metric=None, Optional[int] early_stopping_rounds=None, Union[int, bool] verbose=True, Optional[Union[Booster, XGBModel]] xgb_model=None, Optional[Sequence[_DaskCollection]] sample_weight_eval_set=None, Optional[Sequence[_DaskCollection]] base_margin_eval_set=None, Optional[_DaskCollection] feature_weights=None, Optional[Sequence[TrainingCallback]] callbacks=None)

Any	predict_proba (self, _DaskCollection X, bool validate_features=True, Optional[_DaskCollection] base_margin=None, Optional[Tuple[int, int]] iteration_range=None)

Public Member Functions inherited from xgboost.dask.DaskScikitLearnBase
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)

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

	classes_

	n_classes_

	objective

Data Fields inherited from xgboost.dask.DaskScikitLearnBase
	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_

Static Public Attributes
str	end_note

Protected Member Functions
"DaskXGBClassifier"	_fit_async (self, _DataT X, _DaskCollection y, Optional[_DaskCollection] sample_weight, Optional[_DaskCollection] base_margin, Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] eval_set, Optional[Union[str, Sequence[str], Metric]] eval_metric, Optional[Sequence[_DaskCollection]] sample_weight_eval_set, Optional[Sequence[_DaskCollection]] base_margin_eval_set, Optional[int] early_stopping_rounds, Union[int, bool] verbose, Optional[Union[Booster, XGBModel]] xgb_model, Optional[_DaskCollection] feature_weights, Optional[Sequence[TrainingCallback]] callbacks)

_DaskCollection	_predict_proba_async (self, _DataT X, bool validate_features, Optional[_DaskCollection] base_margin, Optional[Tuple[int, int]] iteration_range)

_DaskCollection	_predict_async (self, _DataT data, bool output_margin, bool validate_features, Optional[_DaskCollection] base_margin, Optional[Tuple[int, int]] iteration_range)

Protected Member Functions inherited from xgboost.dask.DaskScikitLearnBase
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
	_Booster

	_fit_async

	_predict_proba_async

Protected Attributes inherited from xgboost.dask.DaskScikitLearnBase
	_apply_async

	_asynchronous

Protected Attributes inherited from xgboost.sklearn.XGBModel
	_Booster

Additional Inherited Members
Static Protected Attributes inherited from xgboost.dask.DaskScikitLearnBase
	_client = None

Member Function Documentation

◆ _predict_async()

_DaskCollection xgboost.dask.DaskXGBClassifier._predict_async	(		self,
		_DataT	data,
		bool	output_margin,
		bool	validate_features,
		Optional[_DaskCollection]	base_margin,
		Optional[Tuple[int, int]]	iteration_range
	)

protected

Reimplemented from xgboost.dask.DaskScikitLearnBase.

◆ fit()

"DaskXGBClassifier" xgboost.dask.DaskXGBClassifier.fit	(		self,
		_DataT	X,
		_DaskCollection	y,
		*Optional[_DaskCollection]	sample_weight = `None`,
		Optional[_DaskCollection]	base_margin = `None`,
		Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]]	eval_set = `None`,
		Optional[Union[str, Sequence[str], Callable]]	eval_metric = `None`,
		Optional[int]	early_stopping_rounds = `None`,
		Union[int, bool]	verbose = `True`,
		Optional[Union[Booster, XGBModel]]	xgb_model = `None`,
		Optional[Sequence[_DaskCollection]]	sample_weight_eval_set = `None`,
		Optional[Sequence[_DaskCollection]]	base_margin_eval_set = `None`,
		Optional[_DaskCollection]	feature_weights = `None`,
		Optional[Sequence[TrainingCallback]]	callbacks = `None`
	)

Fit gradient boosting model.

Note that calling ``fit()`` multiple times will cause the model object to be
re-fit from scratch. To resume training from a previous checkpoint, explicitly
pass ``xgb_model`` argument.

Parameters
----------
X :
    Feature matrix. See :ref:`py-data` for a list of supported types.

    When the ``tree_method`` is set to ``hist``, internally, the
    :py:class:`QuantileDMatrix` will be used instead of the :py:class:`DMatrix`
    for conserving memory. However, this has performance implications when the
    device of input data is not matched with algorithm. For instance, if the
    input is a numpy array on CPU but ``cuda`` is used for training, then the
    data is first processed on CPU then transferred to GPU.
y :
    Labels
sample_weight :
    instance weights
base_margin :
    global bias for each instance.
eval_set :
    A list of (X, y) tuple pairs to use as validation sets, for which
    metrics will be computed.
    Validation metrics will help us track the performance of the model.

eval_metric : str, list of str, or callable, optional

    .. deprecated:: 1.6.0

    Use `eval_metric` in :py:meth:`__init__` or :py:meth:`set_params` instead.

early_stopping_rounds : int

    .. deprecated:: 1.6.0

    Use `early_stopping_rounds` in :py:meth:`__init__` or :py:meth:`set_params`
    instead.
verbose :
    If `verbose` is True and an evaluation set is used, the evaluation metric
    measured on the validation set is printed to stdout at each boosting stage.
    If `verbose` is an integer, the evaluation metric is printed at each
    `verbose` boosting stage. The last boosting stage / the boosting stage found
    by using `early_stopping_rounds` is also printed.
xgb_model :
    file name of stored XGBoost model or 'Booster' instance XGBoost model to be
    loaded before training (allows training continuation).
sample_weight_eval_set :
    A list of the form [L_1, L_2, ..., L_n], where each L_i is an array like
    object storing instance weights for the i-th validation set.
base_margin_eval_set :
    A list of the form [M_1, M_2, ..., M_n], where each M_i is an array like
    object storing base margin for the i-th validation set.
feature_weights :
    Weight for each feature, defines the probability of each feature being
    selected when colsample is being used.  All values must be greater than 0,
    otherwise a `ValueError` is thrown.

callbacks :
    .. deprecated:: 1.6.0
        Use `callbacks` in :py:meth:`__init__` or :py:meth:`set_params` instead.

Reimplemented from xgboost.sklearn.XGBModel.

Reimplemented in xgboost.dask.DaskXGBRFClassifier.

Field Documentation

◆ end_note

str xgboost.dask.DaskXGBClassifier.end_note

static

Initial value:

= """
        .. note::
 
            For dask implementation, group is not supported, use qid instead.
""",

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

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

Public Member Functions

Data Fields

Static Public Attributes

Protected Member Functions

Protected Attributes

Additional Inherited Members

Member Function Documentation

◆ _predict_async()

◆ fit()

Field Documentation

◆ end_note