xgboost.core.QuantileDMatrix Class Reference

Inheritance diagram for xgboost.core.QuantileDMatrix:

Public Member Functions
None	__init__ (self, DataType data, Optional[ArrayLike] label=None, *Optional[ArrayLike] weight=None, Optional[ArrayLike] base_margin=None, Optional[float] missing=None, bool silent=False, Optional[FeatureNames] feature_names=None, Optional[FeatureTypes] feature_types=None, Optional[int] nthread=None, Optional[int] max_bin=None, Optional[DMatrix] ref=None, Optional[ArrayLike] group=None, Optional[ArrayLike] qid=None, Optional[ArrayLike] label_lower_bound=None, Optional[ArrayLike] label_upper_bound=None, Optional[ArrayLike] feature_weights=None, bool enable_categorical=False, DataSplitMode data_split_mode=DataSplitMode.ROW)
Public Member Functions inherited from xgboost.core.DMatrix
None	__del__ (self)
None	set_info (self, *Optional[ArrayLike] label=None, Optional[ArrayLike] weight=None, Optional[ArrayLike] base_margin=None, Optional[ArrayLike] group=None, Optional[ArrayLike] qid=None, Optional[ArrayLike] label_lower_bound=None, Optional[ArrayLike] label_upper_bound=None, Optional[FeatureNames] feature_names=None, Optional[FeatureTypes] feature_types=None, Optional[ArrayLike] feature_weights=None)
np.ndarray	get_float_info (self, str field)
np.ndarray	get_uint_info (self, str field)
None	set_float_info (self, str field, ArrayLike data)
None	set_float_info_npy2d (self, str field, ArrayLike data)
None	set_uint_info (self, str field, ArrayLike data)
None	save_binary (self, Union[str, os.PathLike] fname, bool silent=True)
None	set_label (self, ArrayLike label)
None	set_weight (self, ArrayLike weight)
None	set_base_margin (self, ArrayLike margin)
None	set_group (self, ArrayLike group)
np.ndarray	get_label (self)
np.ndarray	get_weight (self)
np.ndarray	get_base_margin (self)
np.ndarray	get_group (self)
scipy.sparse.csr_matrix	get_data (self)
Tuple[np.ndarray, np.ndarray]	get_quantile_cut (self)
int	num_row (self)
int	num_col (self)
int	num_nonmissing (self)
"DMatrix"	slice (self, Union[List[int], np.ndarray] rindex, bool allow_groups=False)
Optional[FeatureNames]	feature_names (self)
None	feature_names (self, Optional[FeatureNames] feature_names)
Optional[FeatureTypes]	feature_types (self)
None	feature_types (self, Optional[FeatureTypes] feature_types)

Data Fields
	max_bin
	missing
	nthread
	handle
Data Fields inherited from xgboost.core.DMatrix
	missing
	nthread
	silent
	handle
	feature_names
	feature_types

Protected Member Functions
None	_init (self, DataType data, Optional[DMatrix] ref, bool enable_categorical, **Any meta)
Protected Member Functions inherited from xgboost.core.DMatrix
None	_init_from_iter (self, DataIter iterator, bool enable_categorical)

Protected Attributes
	_silent

Detailed Description

A DMatrix variant that generates quantilized data directly from input for the
``hist`` tree method. This DMatrix is primarily designed to save memory in training
by avoiding intermediate storage. Set ``max_bin`` to control the number of bins
during quantisation, which should be consistent with the training parameter
``max_bin``. When ``QuantileDMatrix`` is used for validation/test dataset, ``ref``
should be another ``QuantileDMatrix``(or ``DMatrix``, but not recommended as it
defeats the purpose of saving memory) constructed from training dataset.  See
:py:obj:`xgboost.DMatrix` for documents on meta info.

.. note::

    Do not use ``QuantileDMatrix`` as validation/test dataset without supplying a
    reference (the training dataset) ``QuantileDMatrix`` using ``ref`` as some
    information may be lost in quantisation.

.. versionadded:: 1.7.0

Parameters
----------
max_bin :
    The number of histogram bin, should be consistent with the training parameter
    ``max_bin``.

ref :
    The training dataset that provides quantile information, needed when creating
    validation/test dataset with ``QuantileDMatrix``. Supplying the training DMatrix
    as a reference means that the same quantisation applied to the training data is
    applied to the validation/test data

Constructor & Destructor Documentation

__init__()

None xgboost.core.QuantileDMatrix.__init__	(		self,
		DataType	data,
		Optional[ArrayLike]	label = None,
		*Optional[ArrayLike]	weight = None,
		Optional[ArrayLike]	base_margin = None,
		Optional[float]	missing = None,
		bool	silent = False,
		Optional[FeatureNames]	feature_names = None,
		Optional[FeatureTypes]	feature_types = None,
		Optional[int]	nthread = None,
		Optional[int]	group = None,
		Optional[DMatrix]	qid = None,
		Optional[ArrayLike]	label_lower_bound = None,
		Optional[ArrayLike]	label_upper_bound = None,
		Optional[ArrayLike]	feature_weights = None,
		Optional[ArrayLike]	enable_categorical = None,
		Optional[ArrayLike]	data_split_mode = None,
		bool	enable_categorical = False,
		DataSplitMode	data_split_mode = DataSplitMode.ROW
	)

Parameters
----------
data :
    Data source of DMatrix. See :ref:`py-data` for a list of supported input
    types.
label :
    Label of the training data.
weight :
    Weight for each instance.

     .. note::

         For ranking task, weights are per-group.  In ranking task, one weight
         is assigned to each 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 :
    Base margin used for boosting from existing model.
missing :
    Value in the input data which needs to be present as a missing value. If
    None, defaults to np.nan.
silent :
    Whether print messages during construction
feature_names :
    Set names for features.
feature_types :

    Set types for features.  When `enable_categorical` is set to `True`, string
    "c" represents categorical data type while "q" represents numerical feature
    type. For categorical features, the input is assumed to be preprocessed and
    encoded by the users. The encoding can be done via
    :py:class:`sklearn.preprocessing.OrdinalEncoder` or pandas dataframe
    `.cat.codes` method. This is useful when users want to specify categorical
    features without having to construct a dataframe as input.

nthread :
    Number of threads to use for loading data when parallelization is
    applicable. If -1, uses maximum threads available on the system.
group :
    Group size for all ranking group.
qid :
    Query ID for data samples, used for ranking.
label_lower_bound :
    Lower bound for survival training.
label_upper_bound :
    Upper bound for survival training.
feature_weights :
    Set feature weights for column sampling.
enable_categorical :

    .. versionadded:: 1.3.0

    .. note:: This parameter is experimental

    Experimental support of specializing for categorical features.  Do not set
    to True unless you are interested in development. Also, JSON/UBJSON
    serialization format is required.

Reimplemented from xgboost.core.DMatrix.

Reimplemented in xgboost.core.DeviceQuantileDMatrix.

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

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