Inheritance diagram for xgboost.sklearn.XGBRanker:

Public Member Functions
	__init__ (self, str objective="rank:ndcg", *Any kwargs)

"XGBRanker"	fit (self, ArrayLike X, ArrayLike y, *Optional[ArrayLike] group=None, Optional[ArrayLike] qid=None, Optional[ArrayLike] sample_weight=None, Optional[ArrayLike] base_margin=None, Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] eval_set=None, Optional[Sequence[ArrayLike]] eval_group=None, Optional[Sequence[ArrayLike]] eval_qid=None, Optional[Union[str, Sequence[str], Metric]] eval_metric=None, Optional[int] early_stopping_rounds=None, Optional[Union[bool, int]] verbose=False, 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)

ArrayLike	predict (self, ArrayLike X, bool output_margin=False, bool validate_features=True, Optional[ArrayLike] base_margin=None, Optional[Tuple[int, int]] iteration_range=None)

ArrayLike	apply (self, ArrayLike X, Optional[Tuple[int, int]] iteration_range=None)

float	score (self, ArrayLike X, ArrayLike y)

Public Member Functions inherited from xgboost.sklearn.XGBModel
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
	objective

	eval_metric

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
DMatrix	_create_ltr_dmatrix (self, Optional[DMatrix] ref, ArrayLike data, ArrayLike qid, **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

Protected Attributes inherited from xgboost.sklearn.XGBModel
	_Booster

Additional Inherited Members
Static Protected Attributes inherited from xgboost.sklearn.XGBRankerMixIn
str	_estimator_type = "ranker"

Constructor & Destructor Documentation

◆ init()

xgboost.sklearn.XGBRanker.__init__	(		self,
		*str	objective = `"rank:ndcg"`,
		**Any	kwargs
	)

Reimplemented from xgboost.sklearn.XGBModel.

Member Function Documentation

◆ apply()

ArrayLike xgboost.sklearn.XGBRanker.apply	(		self,
		ArrayLike	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.

◆ fit()

"XGBRanker" xgboost.sklearn.XGBRanker.fit	(		self,
		ArrayLike	X,
		ArrayLike	y,
		*Optional[ArrayLike]	group = `None`,
		Optional[ArrayLike]	qid = `None`,
		Optional[ArrayLike]	sample_weight = `None`,
		Optional[ArrayLike]	base_margin = `None`,
		Optional[Sequence[Tuple[ArrayLike, ArrayLike]]]	eval_set = `None`,
		Optional[Sequence[ArrayLike]]	eval_group = `None`,
		Optional[Sequence[ArrayLike]]	eval_qid = `None`,
		Optional[Union[str, Sequence[str], Metric]]	eval_metric = `None`,
		Optional[int]	early_stopping_rounds = `None`,
		Optional[Union[bool, int]]	verbose = `False`,
		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`
	)

Fit gradient boosting ranker

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 this is a :py:class:`pandas.DataFrame` or a :py:class:`cudf.DataFrame`,
    it may contain a special column called ``qid`` for specifying the query
    index. Using a special column is the same as using the `qid` parameter,
    except for being compatible with sklearn utility functions like
    :py:func:`sklearn.model_selection.cross_validation`. The same convention
    applies to the :py:meth:`XGBRanker.score` and :py:meth:`XGBRanker.predict`.

    +-----+----------------+----------------+
    | qid | feat_0         | feat_1         |
    +-----+----------------+----------------+
    | 0   | :math:`x_{00}` | :math:`x_{01}` |
    +-----+----------------+----------------+
    | 1   | :math:`x_{10}` | :math:`x_{11}` |
    +-----+----------------+----------------+
    | 1   | :math:`x_{20}` | :math:`x_{21}` |
    +-----+----------------+----------------+

    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
group :
    Size of each query group of training data. Should have as many elements as
    the query groups in the training data.  If this is set to None, then user
    must provide qid.
qid :
    Query ID for each training sample.  Should have the size of n_samples.  If
    this is set to None, then user must provide group or a special column in X.
sample_weight :
    Query group weights

    .. note:: Weights are per-group for ranking tasks

        In ranking task, one weight is assigned to each query group/id (not each
        data point). This is because we only care about the relative ordering of
        data points within each group, so it doesn't make sense to assign
        weights to individual data points.

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_group :
    A list in which ``eval_group[i]`` is the list containing the sizes of all
    query groups in the ``i``-th pair in **eval_set**.
eval_qid :
    A list in which ``eval_qid[i]`` is the array containing query ID of ``i``-th
    pair in **eval_set**. The special column convention in `X` applies to
    validation datasets as well.

eval_metric : str, list of str, 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 a list of
    group weights on the i-th validation set.

    .. note:: Weights are per-group for ranking tasks

        In ranking task, one weight is assigned to each query group (not each
        data point). This is because we only care about the relative ordering of
        data points within each group, so it doesn't make sense to assign
        weights to individual data points.
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.

◆ predict()

ArrayLike xgboost.sklearn.XGBRanker.predict	(		self,
		ArrayLike	X,
		bool	output_margin = `False`,
		bool	validate_features = `True`,
		Optional[ArrayLike]	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.

◆ score()

float xgboost.sklearn.XGBRanker.score	(		self,
		ArrayLike	X,
		ArrayLike	y
	)

Evaluate score for data using the last evaluation metric. If the model is
trained with early stopping, then :py:attr:`best_iteration` is used
automatically.

Parameters
----------
X : Union[pd.DataFrame, cudf.DataFrame]
  Feature matrix. A DataFrame with a special `qid` column.

y :
  Labels

Returns
-------
score :
  The result of the first evaluation metric for the ranker.

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

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

Public Member Functions

Data Fields

Protected Member Functions

Protected Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ apply()

◆ fit()

◆ predict()

◆ score()

◆ init()