DaskXGBForecast

dask XGBoost forecaster

Wrapper of xgboost.dask.DaskXGBRegressor that adds a model_ property that contains the fitted model and is sent to the workers in the forecasting step.

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DaskXGBForecast

 DaskXGBForecast (max_depth:Optional[int]=None,
                  max_leaves:Optional[int]=None,
                  max_bin:Optional[int]=None,
                  grow_policy:Optional[str]=None,
                  learning_rate:Optional[float]=None,
                  n_estimators:Optional[int]=None,
                  verbosity:Optional[int]=None, objective:Union[str,xgboos
                  t.sklearn._SklObjWProto,Callable[[Any,Any],Tuple[numpy.n
                  darray,numpy.ndarray]],NoneType]=None,
                  booster:Optional[str]=None,
                  tree_method:Optional[str]=None,
                  n_jobs:Optional[int]=None, gamma:Optional[float]=None,
                  min_child_weight:Optional[float]=None,
                  max_delta_step:Optional[float]=None,
                  subsample:Optional[float]=None,
                  sampling_method:Optional[str]=None,
                  colsample_bytree:Optional[float]=None,
                  colsample_bylevel:Optional[float]=None,
                  colsample_bynode:Optional[float]=None,
                  reg_alpha:Optional[float]=None,
                  reg_lambda:Optional[float]=None,
                  scale_pos_weight:Optional[float]=None,
                  base_score:Optional[float]=None, random_state:Union[nump
                  y.random.mtrand.RandomState,numpy.random._generator.Gene
                  rator,int,NoneType]=None, missing:float=nan,
                  num_parallel_tree:Optional[int]=None, monotone_constrain
                  ts:Union[Dict[str,int],str,NoneType]=None, interaction_c
                  onstraints:Union[str,Sequence[Sequence[str]],NoneType]=N
                  one, importance_type:Optional[str]=None,
                  device:Optional[str]=None,
                  validate_parameters:Optional[bool]=None,
                  enable_categorical:bool=False,
                  feature_types:Optional[Sequence[str]]=None,
                  max_cat_to_onehot:Optional[int]=None,
                  max_cat_threshold:Optional[int]=None,
                  multi_strategy:Optional[str]=None,
                  eval_metric:Union[str,List[str],Callable,NoneType]=None,
                  early_stopping_rounds:Optional[int]=None, callbacks:Opti
                  onal[List[xgboost.callback.TrainingCallback]]=None,
                  **kwargs:Any)

Implementation of the Scikit-Learn API for XGBoost. See :doc:/python/sklearn_estimator for more information.

	Type	Default	Details
max_depth	Optional	None	Maximum tree depth for base learners.
max_leaves	Optional	None	Maximum number of leaves; 0 indicates no limit.
max_bin	Optional	None	If using histogram-based algorithm, maximum number of bins per feature
grow_policy	Optional	None	Tree growing policy. - depthwise: Favors splitting at nodes closest to the node, - lossguide: Favors splitting at nodes with highest loss change.
learning_rate	Optional	None	Boosting learning rate (xgb’s “eta”)
n_estimators	Optional	None	Number of gradient boosted trees. Equivalent to number of boosting rounds.
verbosity	Optional	None	The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
objective	Union	None	Specify the learning task and the corresponding learning objective or a custom objective function to be used. For custom objective, see :doc:/tutorials/custom_metric_obj and :ref:custom-obj-metric for more information, along with the end note for function signatures.
booster	Optional	None
tree_method	Optional	None	Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from the parameters document :doc:tree method<br/></treemethod>
n_jobs	Optional	None	Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.
gamma	Optional	None	(min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight	Optional	None	Minimum sum of instance weight(hessian) needed in a child.
max_delta_step	Optional	None	Maximum delta step we allow each tree’s weight estimation to be.
subsample	Optional	None	Subsample ratio of the training instance.
sampling_method	Optional	None	Sampling method. Used only by the GPU version of hist tree method. - uniform: Select random training instances uniformly. - gradient_based: Select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)
colsample_bytree	Optional	None	Subsample ratio of columns when constructing each tree.
colsample_bylevel	Optional	None	Subsample ratio of columns for each level.
colsample_bynode	Optional	None	Subsample ratio of columns for each split.
reg_alpha	Optional	None	L1 regularization term on weights (xgb’s alpha).
reg_lambda	Optional	None	L2 regularization term on weights (xgb’s lambda).
scale_pos_weight	Optional	None	Balancing of positive and negative weights.
base_score	Optional	None	The initial prediction score of all instances, global bias.
random_state	Union	None	Random number seed. .. note:: Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.
missing	float	nan	Value in the data which needs to be present as a missing value. Default to :py:data:numpy.nan.
num_parallel_tree	Optional	None
monotone_constraints	Union	None	Constraint of variable monotonicity. See :doc:tutorial </tutorials/monotonic> for more information.
interaction_constraints	Union	None	Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g. [[0, 1], [2,<br/>3, 4]], where each inner list is a group of indices of features that are allowed to interact with each other. See :doc:tutorial<br/></tutorials/feature_interaction_constraint> for more information
importance_type	Optional	None
device	Optional	None	.. versionadded:: 2.0.0 Device ordinal, available options are cpu, cuda, and gpu.
validate_parameters	Optional	None	Give warnings for unknown parameter.
enable_categorical	bool	False	See the same parameter of :py:class:DMatrix for details.
feature_types	Optional	None	.. versionadded:: 1.7.0 Used for specifying feature types without constructing a dataframe. See :py:class:DMatrix for details.
max_cat_to_onehot	Optional	None	.. versionadded:: 1.6.0 .. note:: This parameter is experimental A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Also, enable_categorical needs to be set to have categorical feature support. See :doc:Categorical Data<br/></tutorials/categorical> and :ref:cat-param for details.
max_cat_threshold	Optional	None	.. versionadded:: 1.7.0 .. note:: This parameter is experimental Maximum number of categories considered for each split. Used only by partition-based splits for preventing over-fitting. Also, enable_categorical needs to be set to have categorical feature support. See :doc:Categorical Data<br/></tutorials/categorical> and :ref:cat-param for details.
multi_strategy	Optional	None	.. versionadded:: 2.0.0 .. note:: This parameter is working-in-progress. The strategy used for training multi-target models, including multi-target regression and multi-class classification. See :doc:/tutorials/multioutput for more information. - one_output_per_tree: One model for each target. - multi_output_tree: Use multi-target trees.
eval_metric	Union	None	.. versionadded:: 1.6.0 Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in :py:mod:sklearn.metrics, or any other user defined metric that looks like sklearn.metrics. If custom objective is also provided, then custom metric should implement the corresponding reverse link function. Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping. For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see :py:obj:xgboost.callback.EarlyStopping. See :doc:/tutorials/custom_metric_obj and :ref:custom-obj-metric for more information. .. code-block:: python from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor( tree_method=“hist”, eval_metric=mean_absolute_error, ) reg.fit(X, y, eval_set=[(X, y)])
early_stopping_rounds	Optional	None	.. versionadded:: 1.6.0 - Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set in :py:meth:fit. - If early stopping occurs, the model will have two additional attributes: :py:attr:best_score and :py:attr:best_iteration. These are used by the :py:meth:predict and :py:meth:apply methods to determine the optimal number of trees during inference. If users want to access the full model (including trees built after early stopping), they can specify the iteration_range in these inference methods. In addition, other utilities like model plotting can also use the entire model. - If you prefer to discard the trees after best_iteration, consider using the callback function :py:class:xgboost.callback.EarlyStopping. - If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping.
callbacks	Optional	None	List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using :ref:Callback API <callback_api>. .. note:: States in callback are not preserved during training, which means callback objects can not be reused for multiple training sessions without reinitialization or deepcopy. .. code-block:: python for params in parameters_grid: # be sure to (re)initialize the callbacks before each run callbacks = [xgb.callback.LearningRateScheduler(custom_rates)] reg = xgboost.XGBRegressor(**params, callbacks=callbacks) reg.fit(X, y)
kwargs	Any		Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found :doc:here </parameter>. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError. .. note:: **kwargs unsupported by scikit-learn **kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.
Returns	None