1808 lines
68 KiB
Python
1808 lines
68 KiB
Python
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, too-many-lines
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"""Scikit-Learn Wrapper interface for XGBoost."""
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import copy
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import warnings
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import json
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import os
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from typing import Union, Optional, List, Dict, Callable, Tuple, Any, TypeVar, Type, cast
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from typing import Sequence
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import numpy as np
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from .core import Booster, DMatrix, XGBoostError
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from .core import _deprecate_positional_args, _convert_ntree_limit
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from .core import Metric
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from .training import train
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from .callback import TrainingCallback
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from .data import _is_cudf_df, _is_cudf_ser, _is_cupy_array
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# Do not use class names on scikit-learn directly. Re-define the classes on
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# .compat to guarantee the behavior without scikit-learn
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from .compat import (
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SKLEARN_INSTALLED,
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XGBModelBase,
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XGBClassifierBase,
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XGBRegressorBase,
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XGBoostLabelEncoder,
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)
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array_like = Any
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class XGBRankerMixIn: # pylint: disable=too-few-public-methods
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"""MixIn for ranking, defines the _estimator_type usually defined in scikit-learn base
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classes."""
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_estimator_type = "ranker"
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def _check_rf_callback(
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early_stopping_rounds: Optional[int],
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callbacks: Optional[Sequence[TrainingCallback]],
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) -> None:
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if early_stopping_rounds is not None or callbacks is not None:
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raise NotImplementedError(
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"`early_stopping_rounds` and `callbacks` are not implemented for"
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" random forest."
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)
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_SklObjective = Optional[
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Union[
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str, Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]
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]
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]
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def _objective_decorator(
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func: Callable[[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]
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) -> Callable[[np.ndarray, DMatrix], Tuple[np.ndarray, np.ndarray]]:
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"""Decorate an objective function
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Converts an objective function using the typical sklearn metrics
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signature so that it is usable with ``xgboost.training.train``
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Parameters
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----------
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func:
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Expects a callable with signature ``func(y_true, y_pred)``:
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y_true: array_like of shape [n_samples]
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The target values
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y_pred: array_like of shape [n_samples]
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The predicted values
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Returns
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-------
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new_func:
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The new objective function as expected by ``xgboost.training.train``.
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The signature is ``new_func(preds, dmatrix)``:
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preds: array_like, shape [n_samples]
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The predicted values
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dmatrix: ``DMatrix``
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The training set from which the labels will be extracted using
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``dmatrix.get_label()``
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"""
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def inner(preds: np.ndarray, dmatrix: DMatrix) -> Tuple[np.ndarray, np.ndarray]:
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"""internal function"""
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labels = dmatrix.get_label()
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return func(labels, preds)
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return inner
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def _metric_decorator(func: Callable) -> Metric:
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"""Decorate a metric function from sklearn.
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Converts an metric function that uses the typical sklearn metric signature so that it
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is compatible with :py:func:`train`
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"""
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def inner(y_score: np.ndarray, dmatrix: DMatrix) -> Tuple[str, float]:
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y_true = dmatrix.get_label()
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return func.__name__, func(y_true, y_score)
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return inner
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__estimator_doc = '''
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n_estimators : int
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Number of gradient boosted trees. Equivalent to number of boosting
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rounds.
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'''
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__model_doc = f'''
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max_depth : Optional[int]
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Maximum tree depth for base learners.
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learning_rate : Optional[float]
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Boosting learning rate (xgb's "eta")
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verbosity : Optional[int]
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The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
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objective : {_SklObjective}
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Specify the learning task and the corresponding learning objective or
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a custom objective function to be used (see note below).
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booster: Optional[str]
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Specify which booster to use: gbtree, gblinear or dart.
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tree_method: Optional[str]
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Specify which tree method to use. Default to auto. If this parameter
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is set to default, XGBoost will choose the most conservative option
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available. It's recommended to study this option from the parameters
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document: https://xgboost.readthedocs.io/en/latest/treemethod.html.
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n_jobs : Optional[int]
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Number of parallel threads used to run xgboost. When used with other Scikit-Learn
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algorithms like grid search, you may choose which algorithm to parallelize and
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balance the threads. Creating thread contention will significantly slow down both
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algorithms.
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gamma : Optional[float]
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Minimum loss reduction required to make a further partition on a leaf
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node of the tree.
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min_child_weight : Optional[float]
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Minimum sum of instance weight(hessian) needed in a child.
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max_delta_step : Optional[float]
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Maximum delta step we allow each tree's weight estimation to be.
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subsample : Optional[float]
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Subsample ratio of the training instance.
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colsample_bytree : Optional[float]
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Subsample ratio of columns when constructing each tree.
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colsample_bylevel : Optional[float]
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Subsample ratio of columns for each level.
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colsample_bynode : Optional[float]
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Subsample ratio of columns for each split.
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reg_alpha : Optional[float]
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L1 regularization term on weights (xgb's alpha).
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reg_lambda : Optional[float]
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L2 regularization term on weights (xgb's lambda).
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scale_pos_weight : Optional[float]
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Balancing of positive and negative weights.
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base_score : Optional[float]
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The initial prediction score of all instances, global bias.
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random_state : Optional[Union[numpy.random.RandomState, int]]
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Random number seed.
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.. note::
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Using gblinear booster with shotgun updater is nondeterministic as
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it uses Hogwild algorithm.
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missing : float, default np.nan
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Value in the data which needs to be present as a missing value.
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num_parallel_tree: Optional[int]
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Used for boosting random forest.
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monotone_constraints : Optional[Union[Dict[str, int], str]]
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Constraint of variable monotonicity. See tutorial for more
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information.
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interaction_constraints : Optional[Union[str, List[Tuple[str]]]]
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Constraints for interaction representing permitted interactions. The
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constraints must be specified in the form of a nest list, e.g. [[0, 1],
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[2, 3, 4]], where each inner list is a group of indices of features
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that are allowed to interact with each other. See tutorial for more
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information
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importance_type: Optional[str]
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The feature importance type for the feature_importances\\_ property:
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* For tree model, it's either "gain", "weight", "cover", "total_gain" or
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"total_cover".
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* For linear model, only "weight" is defined and it's the normalized coefficients
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without bias.
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gpu_id : Optional[int]
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Device ordinal.
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validate_parameters : Optional[bool]
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Give warnings for unknown parameter.
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predictor : Optional[str]
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Force XGBoost to use specific predictor, available choices are [cpu_predictor,
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gpu_predictor].
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enable_categorical : bool
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.. versionadded:: 1.5.0
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Experimental support for categorical data. Do not set to true unless you are
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interested in development. Only valid when `gpu_hist` and dataframe are used.
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eval_metric : Optional[Union[str, List[str], Callable]]
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.. versionadded:: 1.6.0
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Metric used for monitoring the training result and early stopping. It can be a
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string or list of strings as names of predefined metric in XGBoost (See
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doc/parameter.rst), one of the metrics in :py:mod:`sklearn.metrics`, or any other
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user defined metric that looks like `sklearn.metrics`.
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If custom objective is also provided, then custom metric should implement the
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corresponding reverse link function.
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Unlike the `scoring` parameter commonly used in scikit-learn, when a callable
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object is provided, it's assumed to be a cost function and by default XGBoost will
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minimize the result during early stopping.
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For advanced usage on Early stopping like directly choosing to maximize instead of
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minimize, see :py:obj:`xgboost.callback.EarlyStopping`.
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See `Custom Objective and Evaluation Metric
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<https://xgboost.readthedocs.io/en/latest/tutorials/custom_metric_obj.html>`_ for
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more.
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.. note::
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This parameter replaces `eval_metric` in :py:meth:`fit` method. The old one
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receives un-transformed prediction regardless of whether custom objective is
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being used.
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.. code-block:: python
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from sklearn.datasets import load_diabetes
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from sklearn.metrics import mean_absolute_error
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X, y = load_diabetes(return_X_y=True)
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reg = xgb.XGBRegressor(
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tree_method="hist",
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eval_metric=mean_absolute_error,
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)
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reg.fit(X, y, eval_set=[(X, y)])
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early_stopping_rounds : Optional[int]
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.. versionadded:: 1.6.0
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Activates early stopping. Validation metric needs to improve at least once in
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every **early_stopping_rounds** round(s) to continue training. Requires at least
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one item in **eval_set** in :py:meth:`xgboost.sklearn.XGBModel.fit`.
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The method returns the model from the last iteration (not the best one). If
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there's more than one item in **eval_set**, the last entry will be used for early
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stopping. If there's more than one metric in **eval_metric**, the last metric
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will be used for early stopping.
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If early stopping occurs, the model will have three additional fields:
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``clf.best_score``, ``clf.best_iteration`` and ``clf.best_ntree_limit``.
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.. note::
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This parameter replaces `early_stopping_rounds` in :py:meth:`fit` method.
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callbacks : Optional[List[TrainingCallback]]
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List of callback functions that are applied at end of each iteration.
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It is possible to use predefined callbacks by using :ref:`callback_api`.
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Example:
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.. code-block:: python
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callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
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save_best=True)]
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kwargs : dict, optional
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Keyword arguments for XGBoost Booster object. Full documentation of
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parameters can be found here:
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https://github.com/dmlc/xgboost/blob/master/doc/parameter.rst.
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Attempting to set a parameter via the constructor args and \\*\\*kwargs
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dict simultaneously will result in a TypeError.
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.. note:: \\*\\*kwargs unsupported by scikit-learn
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\\*\\*kwargs is unsupported by scikit-learn. We do not guarantee
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that parameters passed via this argument will interact properly
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with scikit-learn.
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'''
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__custom_obj_note = '''
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.. note:: Custom objective function
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A custom objective function can be provided for the ``objective``
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parameter. In this case, it should have the signature
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``objective(y_true, y_pred) -> grad, hess``:
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y_true: array_like of shape [n_samples]
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The target values
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y_pred: array_like of shape [n_samples]
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The predicted values
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grad: array_like of shape [n_samples]
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The value of the gradient for each sample point.
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hess: array_like of shape [n_samples]
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The value of the second derivative for each sample point
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'''
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def xgboost_model_doc(
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header: str, items: List[str],
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extra_parameters: Optional[str] = None,
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end_note: Optional[str] = None
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) -> Callable[[Type], Type]:
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'''Obtain documentation for Scikit-Learn wrappers
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Parameters
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----------
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header: str
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An introducion to the class.
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items : list
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A list of common doc items. Available items are:
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- estimators: the meaning of n_estimators
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- model: All the other parameters
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- objective: note for customized objective
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extra_parameters: str
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Document for class specific parameters, placed at the head.
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end_note: str
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Extra notes put to the end.
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'''
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def get_doc(item: str) -> str:
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'''Return selected item'''
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__doc = {'estimators': __estimator_doc,
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'model': __model_doc,
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'objective': __custom_obj_note}
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return __doc[item]
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def adddoc(cls: Type) -> Type:
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doc = ['''
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Parameters
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----------
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''']
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if extra_parameters:
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doc.append(extra_parameters)
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doc.extend([get_doc(i) for i in items])
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if end_note:
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doc.append(end_note)
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full_doc = [header + '\n\n']
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full_doc.extend(doc)
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cls.__doc__ = ''.join(full_doc)
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return cls
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return adddoc
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def _wrap_evaluation_matrices(
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missing: float,
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X: Any,
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y: Any,
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group: Optional[Any],
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qid: Optional[Any],
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sample_weight: Optional[Any],
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base_margin: Optional[Any],
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feature_weights: Optional[Any],
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eval_set: Optional[Sequence[Tuple[Any, Any]]],
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sample_weight_eval_set: Optional[Sequence[Any]],
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base_margin_eval_set: Optional[Sequence[Any]],
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eval_group: Optional[Sequence[Any]],
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eval_qid: Optional[Sequence[Any]],
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create_dmatrix: Callable,
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enable_categorical: bool,
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) -> Tuple[Any, List[Tuple[Any, str]]]:
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"""Convert array_like evaluation matrices into DMatrix. Perform validation on the way.
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"""
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train_dmatrix = create_dmatrix(
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data=X,
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label=y,
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group=group,
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qid=qid,
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weight=sample_weight,
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base_margin=base_margin,
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feature_weights=feature_weights,
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missing=missing,
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enable_categorical=enable_categorical,
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)
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n_validation = 0 if eval_set is None else len(eval_set)
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def validate_or_none(meta: Optional[Sequence], name: str) -> Sequence:
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if meta is None:
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return [None] * n_validation
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if len(meta) != n_validation:
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raise ValueError(
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f"{name}'s length does not equal `eval_set`'s length, " +
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f"expecting {n_validation}, got {len(meta)}"
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)
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return meta
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if eval_set is not None:
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sample_weight_eval_set = validate_or_none(
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sample_weight_eval_set, "sample_weight_eval_set"
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)
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base_margin_eval_set = validate_or_none(
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base_margin_eval_set, "base_margin_eval_set"
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)
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eval_group = validate_or_none(eval_group, "eval_group")
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eval_qid = validate_or_none(eval_qid, "eval_qid")
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evals = []
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for i, (valid_X, valid_y) in enumerate(eval_set):
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# Skip the duplicated entry.
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if all(
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(
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valid_X is X, valid_y is y,
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sample_weight_eval_set[i] is sample_weight,
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base_margin_eval_set[i] is base_margin,
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eval_group[i] is group,
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eval_qid[i] is qid
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)
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):
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evals.append(train_dmatrix)
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else:
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m = create_dmatrix(
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data=valid_X,
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label=valid_y,
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weight=sample_weight_eval_set[i],
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group=eval_group[i],
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qid=eval_qid[i],
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base_margin=base_margin_eval_set[i],
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missing=missing,
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enable_categorical=enable_categorical,
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)
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evals.append(m)
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nevals = len(evals)
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eval_names = [f"validation_{i}" for i in range(nevals)]
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evals = list(zip(evals, eval_names))
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else:
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if any(
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meta is not None
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for meta in [
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sample_weight_eval_set,
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base_margin_eval_set,
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eval_group,
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eval_qid,
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]
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):
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raise ValueError(
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"`eval_set` is not set but one of the other evaluation meta info is "
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"not None."
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)
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evals = []
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return train_dmatrix, evals
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|
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@xgboost_model_doc("""Implementation of the Scikit-Learn API for XGBoost.""",
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['estimators', 'model', 'objective'])
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class XGBModel(XGBModelBase):
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# pylint: disable=too-many-arguments, too-many-instance-attributes, missing-docstring
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def __init__(
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self,
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max_depth: Optional[int] = None,
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learning_rate: Optional[float] = None,
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n_estimators: int = 100,
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|
verbosity: Optional[int] = None,
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objective: _SklObjective = None,
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|
booster: Optional[str] = None,
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|
tree_method: Optional[str] = None,
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|
n_jobs: Optional[int] = None,
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|
gamma: Optional[float] = None,
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|
min_child_weight: Optional[float] = None,
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|
max_delta_step: Optional[float] = None,
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|
subsample: Optional[float] = None,
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|
colsample_bytree: Optional[float] = None,
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|
colsample_bylevel: Optional[float] = None,
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|
colsample_bynode: Optional[float] = None,
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|
reg_alpha: Optional[float] = None,
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reg_lambda: Optional[float] = None,
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scale_pos_weight: Optional[float] = None,
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base_score: Optional[float] = None,
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random_state: Optional[Union[np.random.RandomState, int]] = None,
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missing: float = np.nan,
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num_parallel_tree: Optional[int] = None,
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monotone_constraints: Optional[Union[Dict[str, int], str]] = None,
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interaction_constraints: Optional[Union[str, Sequence[Sequence[str]]]] = None,
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|
importance_type: Optional[str] = None,
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|
gpu_id: Optional[int] = None,
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|
validate_parameters: Optional[bool] = None,
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|
predictor: Optional[str] = None,
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enable_categorical: bool = False,
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eval_metric: Optional[Union[str, List[str], Callable]] = None,
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early_stopping_rounds: Optional[int] = None,
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callbacks: Optional[List[TrainingCallback]] = None,
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**kwargs: Any
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) -> None:
|
|
if not SKLEARN_INSTALLED:
|
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raise XGBoostError(
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"sklearn needs to be installed in order to use this module"
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)
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|
self.n_estimators = n_estimators
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self.objective = objective
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self.max_depth = max_depth
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self.learning_rate = learning_rate
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self.verbosity = verbosity
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self.booster = booster
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self.tree_method = tree_method
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self.gamma = gamma
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self.min_child_weight = min_child_weight
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|
self.max_delta_step = max_delta_step
|
|
self.subsample = subsample
|
|
self.colsample_bytree = colsample_bytree
|
|
self.colsample_bylevel = colsample_bylevel
|
|
self.colsample_bynode = colsample_bynode
|
|
self.reg_alpha = reg_alpha
|
|
self.reg_lambda = reg_lambda
|
|
self.scale_pos_weight = scale_pos_weight
|
|
self.base_score = base_score
|
|
self.missing = missing
|
|
self.num_parallel_tree = num_parallel_tree
|
|
self.random_state = random_state
|
|
self.n_jobs = n_jobs
|
|
self.monotone_constraints = monotone_constraints
|
|
self.interaction_constraints = interaction_constraints
|
|
self.importance_type = importance_type
|
|
self.gpu_id = gpu_id
|
|
self.validate_parameters = validate_parameters
|
|
self.predictor = predictor
|
|
self.enable_categorical = enable_categorical
|
|
self.eval_metric = eval_metric
|
|
self.early_stopping_rounds = early_stopping_rounds
|
|
self.callbacks = callbacks
|
|
if kwargs:
|
|
self.kwargs = kwargs
|
|
|
|
def _more_tags(self) -> Dict[str, bool]:
|
|
'''Tags used for scikit-learn data validation.'''
|
|
return {'allow_nan': True, 'no_validation': True}
|
|
|
|
def __sklearn_is_fitted__(self) -> bool:
|
|
return hasattr(self, "_Booster")
|
|
|
|
def get_booster(self) -> Booster:
|
|
"""Get the underlying xgboost Booster of this model.
|
|
|
|
This will raise an exception when fit was not called
|
|
|
|
Returns
|
|
-------
|
|
booster : a xgboost booster of underlying model
|
|
"""
|
|
if not self.__sklearn_is_fitted__():
|
|
from sklearn.exceptions import NotFittedError
|
|
raise NotFittedError('need to call fit or load_model beforehand')
|
|
return self._Booster
|
|
|
|
def set_params(self, **params: Any) -> "XGBModel":
|
|
"""Set the parameters of this estimator. Modification of the sklearn method to
|
|
allow unknown kwargs. This allows using the full range of xgboost
|
|
parameters that are not defined as member variables in sklearn grid
|
|
search.
|
|
|
|
Returns
|
|
-------
|
|
self
|
|
|
|
"""
|
|
if not params:
|
|
# Simple optimization to gain speed (inspect is slow)
|
|
return self
|
|
|
|
# this concatenates kwargs into parameters, enabling `get_params` for
|
|
# obtaining parameters from keyword parameters.
|
|
for key, value in params.items():
|
|
if hasattr(self, key):
|
|
setattr(self, key, value)
|
|
else:
|
|
if not hasattr(self, "kwargs"):
|
|
self.kwargs = {}
|
|
self.kwargs[key] = value
|
|
|
|
if hasattr(self, '_Booster'):
|
|
parameters = self.get_xgb_params()
|
|
self.get_booster().set_param(parameters)
|
|
|
|
return self
|
|
|
|
def get_params(self, deep: bool = True) -> Dict[str, Any]:
|
|
# pylint: disable=attribute-defined-outside-init
|
|
"""Get parameters."""
|
|
# Based on: https://stackoverflow.com/questions/59248211
|
|
# The basic flow in `get_params` is:
|
|
# 0. Return parameters in subclass first, by using inspect.
|
|
# 1. Return parameters in `XGBModel` (the base class).
|
|
# 2. Return whatever in `**kwargs`.
|
|
# 3. Merge them.
|
|
params = super().get_params(deep)
|
|
cp = copy.copy(self)
|
|
cp.__class__ = cp.__class__.__bases__[0]
|
|
params.update(cp.__class__.get_params(cp, deep))
|
|
# if kwargs is a dict, update params accordingly
|
|
if hasattr(self, "kwargs") and isinstance(self.kwargs, dict):
|
|
params.update(self.kwargs)
|
|
if isinstance(params['random_state'], np.random.RandomState):
|
|
params['random_state'] = params['random_state'].randint(
|
|
np.iinfo(np.int32).max)
|
|
|
|
def parse_parameter(value: Any) -> Optional[Union[int, float, str]]:
|
|
for t in (int, float, str):
|
|
try:
|
|
ret = t(value)
|
|
return ret
|
|
except ValueError:
|
|
continue
|
|
return None
|
|
|
|
# Get internal parameter values
|
|
try:
|
|
config = json.loads(self.get_booster().save_config())
|
|
stack = [config]
|
|
internal = {}
|
|
while stack:
|
|
obj = stack.pop()
|
|
for k, v in obj.items():
|
|
if k.endswith('_param'):
|
|
for p_k, p_v in v.items():
|
|
internal[p_k] = p_v
|
|
elif isinstance(v, dict):
|
|
stack.append(v)
|
|
|
|
for k, v in internal.items():
|
|
if k in params and params[k] is None:
|
|
params[k] = parse_parameter(v)
|
|
except ValueError:
|
|
pass
|
|
return params
|
|
|
|
def get_xgb_params(self) -> Dict[str, Any]:
|
|
"""Get xgboost specific parameters."""
|
|
params = self.get_params()
|
|
# Parameters that should not go into native learner.
|
|
wrapper_specific = {
|
|
"importance_type",
|
|
"kwargs",
|
|
"missing",
|
|
"n_estimators",
|
|
"use_label_encoder",
|
|
"enable_categorical",
|
|
"early_stopping_rounds",
|
|
"callbacks",
|
|
}
|
|
filtered = {}
|
|
for k, v in params.items():
|
|
if k not in wrapper_specific and not callable(v):
|
|
filtered[k] = v
|
|
return filtered
|
|
|
|
def get_num_boosting_rounds(self) -> int:
|
|
"""Gets the number of xgboost boosting rounds."""
|
|
return self.n_estimators
|
|
|
|
def _get_type(self) -> str:
|
|
if not hasattr(self, '_estimator_type'):
|
|
raise TypeError(
|
|
"`_estimator_type` undefined. "
|
|
"Please use appropriate mixin to define estimator type."
|
|
)
|
|
return self._estimator_type # pylint: disable=no-member
|
|
|
|
def save_model(self, fname: Union[str, os.PathLike]) -> None:
|
|
meta = {}
|
|
for k, v in self.__dict__.items():
|
|
if k == '_le':
|
|
meta['_le'] = self._le.to_json()
|
|
continue
|
|
if k == '_Booster':
|
|
continue
|
|
if k == 'classes_':
|
|
# numpy array is not JSON serializable
|
|
meta['classes_'] = self.classes_.tolist()
|
|
continue
|
|
try:
|
|
json.dumps({k: v})
|
|
meta[k] = v
|
|
except TypeError:
|
|
warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.', UserWarning)
|
|
meta['_estimator_type'] = self._get_type()
|
|
meta_str = json.dumps(meta)
|
|
self.get_booster().set_attr(scikit_learn=meta_str)
|
|
self.get_booster().save_model(fname)
|
|
# Delete the attribute after save
|
|
self.get_booster().set_attr(scikit_learn=None)
|
|
|
|
save_model.__doc__ = f"""{Booster.save_model.__doc__}"""
|
|
|
|
def load_model(self, fname: Union[str, bytearray, os.PathLike]) -> None:
|
|
# pylint: disable=attribute-defined-outside-init
|
|
if not hasattr(self, '_Booster'):
|
|
self._Booster = Booster({'n_jobs': self.n_jobs})
|
|
self.get_booster().load_model(fname)
|
|
meta_str = self.get_booster().attr('scikit_learn')
|
|
if meta_str is None:
|
|
# FIXME(jiaming): This doesn't have to be a problem as most of the needed
|
|
# information like num_class and objective is in Learner class.
|
|
warnings.warn(
|
|
'Loading a native XGBoost model with Scikit-Learn interface.'
|
|
)
|
|
return
|
|
meta = json.loads(meta_str)
|
|
states = {}
|
|
for k, v in meta.items():
|
|
if k == '_le':
|
|
self._le = XGBoostLabelEncoder()
|
|
self._le.from_json(v)
|
|
continue
|
|
# FIXME(jiaming): This can be removed once label encoder is gone since we can
|
|
# generate it from `np.arange(self.n_classes_)`
|
|
if k == 'classes_':
|
|
self.classes_ = np.array(v)
|
|
continue
|
|
if k == "_estimator_type":
|
|
if self._get_type() != v:
|
|
raise TypeError(
|
|
"Loading an estimator with different type. "
|
|
f"Expecting: {self._get_type()}, got: {v}"
|
|
)
|
|
continue
|
|
states[k] = v
|
|
self.__dict__.update(states)
|
|
# Delete the attribute after load
|
|
self.get_booster().set_attr(scikit_learn=None)
|
|
|
|
load_model.__doc__ = f"""{Booster.load_model.__doc__}"""
|
|
|
|
# pylint: disable=too-many-branches
|
|
def _configure_fit(
|
|
self,
|
|
booster: Optional[Union[Booster, "XGBModel", str]],
|
|
eval_metric: Optional[Union[Callable, str, Sequence[str]]],
|
|
params: Dict[str, Any],
|
|
early_stopping_rounds: Optional[int],
|
|
callbacks: Optional[Sequence[TrainingCallback]],
|
|
) -> Tuple[
|
|
Optional[Union[Booster, str, "XGBModel"]],
|
|
Optional[Metric],
|
|
Dict[str, Any],
|
|
Optional[int],
|
|
Optional[Sequence[TrainingCallback]],
|
|
]:
|
|
"""Configure parameters for :py:meth:`fit`."""
|
|
if isinstance(booster, XGBModel):
|
|
model: Optional[Union[Booster, str]] = booster.get_booster()
|
|
else:
|
|
model = booster
|
|
|
|
def _deprecated(parameter: str) -> None:
|
|
warnings.warn(
|
|
f"`{parameter}` in `fit` method is deprecated for better compatibility "
|
|
f"with scikit-learn, use `{parameter}` in constructor or`set_params` "
|
|
"instead.",
|
|
UserWarning,
|
|
)
|
|
|
|
def _duplicated(parameter: str) -> None:
|
|
raise ValueError(
|
|
f"2 different `{parameter}` are provided. Use the one in constructor "
|
|
"or `set_params` instead."
|
|
)
|
|
|
|
# Configure evaluation metric.
|
|
if eval_metric is not None:
|
|
_deprecated("eval_metric")
|
|
if self.eval_metric is not None and eval_metric is not None:
|
|
_duplicated("eval_metric")
|
|
# - track where does the evaluation metric come from
|
|
if self.eval_metric is not None:
|
|
from_fit = False
|
|
eval_metric = self.eval_metric
|
|
else:
|
|
from_fit = True
|
|
# - configure callable evaluation metric
|
|
metric: Optional[Metric] = None
|
|
if eval_metric is not None:
|
|
if callable(eval_metric) and from_fit:
|
|
# No need to wrap the evaluation function for old parameter.
|
|
metric = eval_metric
|
|
elif callable(eval_metric):
|
|
# Parameter from constructor or set_params
|
|
metric = _metric_decorator(eval_metric)
|
|
else:
|
|
params.update({"eval_metric": eval_metric})
|
|
|
|
# Configure early_stopping_rounds
|
|
if early_stopping_rounds is not None:
|
|
_deprecated("early_stopping_rounds")
|
|
if early_stopping_rounds is not None and self.early_stopping_rounds is not None:
|
|
_duplicated("early_stopping_rounds")
|
|
early_stopping_rounds = (
|
|
self.early_stopping_rounds
|
|
if self.early_stopping_rounds is not None
|
|
else early_stopping_rounds
|
|
)
|
|
|
|
# Configure callbacks
|
|
if callbacks is not None:
|
|
_deprecated("callbacks")
|
|
if callbacks is not None and self.callbacks is not None:
|
|
_duplicated("callbacks")
|
|
callbacks = self.callbacks if self.callbacks is not None else callbacks
|
|
|
|
# lastly check categorical data support.
|
|
if self.enable_categorical and params.get("tree_method", None) != "gpu_hist":
|
|
raise ValueError(
|
|
"Experimental support for categorical data is not implemented for"
|
|
" current tree method yet."
|
|
)
|
|
|
|
return model, metric, params, early_stopping_rounds, callbacks
|
|
|
|
def _set_evaluation_result(self, evals_result: TrainingCallback.EvalsLog) -> None:
|
|
if evals_result:
|
|
self.evals_result_ = cast(Dict[str, Dict[str, List[float]]], evals_result)
|
|
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: array_like,
|
|
y: array_like,
|
|
*,
|
|
sample_weight: Optional[array_like] = None,
|
|
base_margin: Optional[array_like] = None,
|
|
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: Optional[bool] = True,
|
|
xgb_model: Optional[Union[Booster, str, "XGBModel"]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
|
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
|
feature_weights: Optional[array_like] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None
|
|
) -> "XGBModel":
|
|
# pylint: disable=invalid-name,attribute-defined-outside-init
|
|
"""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
|
|
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` and an evaluation set is used, writes the evaluation metric
|
|
measured on the validation set to stderr.
|
|
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. Only available for `hist`, `gpu_hist` and
|
|
`exact` tree methods.
|
|
|
|
callbacks :
|
|
.. deprecated: 1.6.0
|
|
Use `callbacks` in :py:meth:`__init__` or :py:methd:`set_params` instead.
|
|
"""
|
|
evals_result: TrainingCallback.EvalsLog = {}
|
|
train_dmatrix, evals = _wrap_evaluation_matrices(
|
|
missing=self.missing,
|
|
X=X,
|
|
y=y,
|
|
group=None,
|
|
qid=None,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
feature_weights=feature_weights,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
base_margin_eval_set=base_margin_eval_set,
|
|
eval_group=None,
|
|
eval_qid=None,
|
|
create_dmatrix=lambda **kwargs: DMatrix(nthread=self.n_jobs, **kwargs),
|
|
enable_categorical=self.enable_categorical,
|
|
)
|
|
params = self.get_xgb_params()
|
|
|
|
if callable(self.objective):
|
|
obj: Optional[
|
|
Callable[[np.ndarray, DMatrix], Tuple[np.ndarray, np.ndarray]]
|
|
] = _objective_decorator(self.objective)
|
|
params["objective"] = "reg:squarederror"
|
|
else:
|
|
obj = None
|
|
|
|
model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
|
|
xgb_model, eval_metric, params, early_stopping_rounds, callbacks
|
|
)
|
|
self._Booster = train(
|
|
params,
|
|
train_dmatrix,
|
|
self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
evals_result=evals_result,
|
|
obj=obj,
|
|
custom_metric=metric,
|
|
verbose_eval=verbose,
|
|
xgb_model=model,
|
|
callbacks=callbacks,
|
|
)
|
|
|
|
self._set_evaluation_result(evals_result)
|
|
return self
|
|
|
|
def _can_use_inplace_predict(self) -> bool:
|
|
# When predictor is explicitly set, using `inplace_predict` might result into
|
|
# error with incompatible data type.
|
|
# Inplace predict doesn't handle as many data types as DMatrix, but it's
|
|
# sufficient for dask interface where input is simpiler.
|
|
predictor = self.get_params().get("predictor", None)
|
|
if predictor in ("auto", None) and self.booster != "gblinear":
|
|
return True
|
|
return False
|
|
|
|
def _get_iteration_range(
|
|
self, iteration_range: Optional[Tuple[int, int]]
|
|
) -> Tuple[int, int]:
|
|
if (iteration_range is None or iteration_range[1] == 0):
|
|
# Use best_iteration if defined.
|
|
try:
|
|
iteration_range = (0, self.best_iteration + 1)
|
|
except AttributeError:
|
|
iteration_range = (0, 0)
|
|
if self.booster == "gblinear":
|
|
iteration_range = (0, 0)
|
|
return iteration_range
|
|
|
|
def predict(
|
|
self,
|
|
X: array_like,
|
|
output_margin: bool = False,
|
|
ntree_limit: Optional[int] = None,
|
|
validate_features: bool = True,
|
|
base_margin: Optional[array_like] = None,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> np.ndarray:
|
|
"""Predict with `X`. If the model is trained with early stopping, then `best_iteration`
|
|
is used automatically. For tree models, when data is on GPU, like cupy array or
|
|
cuDF dataframe and `predictor` is not specified, the prediction is run on GPU
|
|
automatically, otherwise it will run on CPU.
|
|
|
|
.. 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.
|
|
ntree_limit :
|
|
Deprecated, use `iteration_range` instead.
|
|
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
|
|
|
|
"""
|
|
iteration_range = _convert_ntree_limit(
|
|
self.get_booster(), ntree_limit, iteration_range
|
|
)
|
|
iteration_range = self._get_iteration_range(iteration_range)
|
|
if self._can_use_inplace_predict():
|
|
try:
|
|
predts = self.get_booster().inplace_predict(
|
|
data=X,
|
|
iteration_range=iteration_range,
|
|
predict_type="margin" if output_margin else "value",
|
|
missing=self.missing,
|
|
base_margin=base_margin,
|
|
validate_features=validate_features,
|
|
)
|
|
if _is_cupy_array(predts):
|
|
import cupy # pylint: disable=import-error
|
|
predts = cupy.asnumpy(predts) # ensure numpy array is used.
|
|
return predts
|
|
except TypeError:
|
|
# coo, csc, dt
|
|
pass
|
|
|
|
test = DMatrix(
|
|
X, base_margin=base_margin,
|
|
missing=self.missing,
|
|
nthread=self.n_jobs,
|
|
enable_categorical=self.enable_categorical
|
|
)
|
|
return self.get_booster().predict(
|
|
data=test,
|
|
iteration_range=iteration_range,
|
|
output_margin=output_margin,
|
|
validate_features=validate_features,
|
|
)
|
|
|
|
def apply(
|
|
self, X: array_like,
|
|
ntree_limit: int = 0,
|
|
iteration_range: Optional[Tuple[int, int]] = None
|
|
) -> np.ndarray:
|
|
"""Return the predicted leaf every tree for each sample. If the model is trained with
|
|
early stopping, then `best_iteration` is used automatically.
|
|
|
|
Parameters
|
|
----------
|
|
X : array_like, shape=[n_samples, n_features]
|
|
Input features matrix.
|
|
|
|
iteration_range :
|
|
See :py:meth:`predict`.
|
|
|
|
ntree_limit :
|
|
Deprecated, use ``iteration_range`` instead.
|
|
|
|
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.
|
|
|
|
"""
|
|
iteration_range = _convert_ntree_limit(
|
|
self.get_booster(), ntree_limit, iteration_range
|
|
)
|
|
iteration_range = self._get_iteration_range(iteration_range)
|
|
test_dmatrix = DMatrix(X, missing=self.missing, nthread=self.n_jobs)
|
|
return self.get_booster().predict(
|
|
test_dmatrix,
|
|
pred_leaf=True,
|
|
iteration_range=iteration_range
|
|
)
|
|
|
|
def evals_result(self) -> Dict[str, Dict[str, List[float]]]:
|
|
"""Return the evaluation results.
|
|
|
|
If **eval_set** is passed to the :py:meth:`fit` function, you can call
|
|
``evals_result()`` to get evaluation results for all passed **eval_sets**. When
|
|
**eval_metric** is also passed to the :py:meth:`fit` function, the
|
|
**evals_result** will contain the **eval_metrics** passed to the :py:meth:`fit`
|
|
function.
|
|
|
|
The returned evaluation result is a dictionary:
|
|
|
|
.. code-block:: python
|
|
|
|
{'validation_0': {'logloss': ['0.604835', '0.531479']},
|
|
'validation_1': {'logloss': ['0.41965', '0.17686']}}
|
|
|
|
Returns
|
|
-------
|
|
evals_result
|
|
|
|
"""
|
|
if getattr(self, "evals_result_", None) is not None:
|
|
evals_result = self.evals_result_
|
|
else:
|
|
raise XGBoostError(
|
|
"No evaluation result, `eval_set` is not used during training."
|
|
)
|
|
|
|
return evals_result
|
|
|
|
@property
|
|
def n_features_in_(self) -> int:
|
|
booster = self.get_booster()
|
|
return booster.num_features()
|
|
|
|
@property
|
|
def feature_names_in_(self) -> np.ndarray:
|
|
"""Names of features seen during :py:meth:`fit`. Defined only when `X` has feature
|
|
names that are all strings."""
|
|
feature_names = self.get_booster().feature_names
|
|
if feature_names is None:
|
|
raise AttributeError(
|
|
"`feature_names_in_` is defined only when `X` has feature names that "
|
|
"are all strings."
|
|
)
|
|
return np.array(feature_names)
|
|
|
|
def _early_stopping_attr(self, attr: str) -> Union[float, int]:
|
|
booster = self.get_booster()
|
|
try:
|
|
return getattr(booster, attr)
|
|
except AttributeError as e:
|
|
raise AttributeError(
|
|
f'`{attr}` in only defined when early stopping is used.'
|
|
) from e
|
|
|
|
@property
|
|
def best_score(self) -> float:
|
|
return float(self._early_stopping_attr('best_score'))
|
|
|
|
@property
|
|
def best_iteration(self) -> int:
|
|
return int(self._early_stopping_attr('best_iteration'))
|
|
|
|
@property
|
|
def best_ntree_limit(self) -> int:
|
|
return int(self._early_stopping_attr('best_ntree_limit'))
|
|
|
|
@property
|
|
def feature_importances_(self) -> np.ndarray:
|
|
"""
|
|
Feature importances property, return depends on `importance_type` parameter.
|
|
|
|
Returns
|
|
-------
|
|
feature_importances_ : array of shape ``[n_features]`` except for multi-class
|
|
linear model, which returns an array with shape `(n_features, n_classes)`
|
|
|
|
"""
|
|
b: Booster = self.get_booster()
|
|
|
|
def dft() -> str:
|
|
return "weight" if self.booster == "gblinear" else "gain"
|
|
score = b.get_score(
|
|
importance_type=self.importance_type if self.importance_type else dft()
|
|
)
|
|
if b.feature_names is None:
|
|
feature_names = [f"f{i}" for i in range(self.n_features_in_)]
|
|
else:
|
|
feature_names = b.feature_names
|
|
# gblinear returns all features so the `get` in next line is only for gbtree.
|
|
all_features = [score.get(f, 0.) for f in feature_names]
|
|
all_features_arr = np.array(all_features, dtype=np.float32)
|
|
total = all_features_arr.sum()
|
|
if total == 0:
|
|
return all_features_arr
|
|
return all_features_arr / total
|
|
|
|
@property
|
|
def coef_(self) -> np.ndarray:
|
|
"""
|
|
Coefficients property
|
|
|
|
.. note:: Coefficients are defined only for linear learners
|
|
|
|
Coefficients are only defined when the linear model is chosen as
|
|
base learner (`booster=gblinear`). It is not defined for other base
|
|
learner types, such as tree learners (`booster=gbtree`).
|
|
|
|
Returns
|
|
-------
|
|
coef_ : array of shape ``[n_features]`` or ``[n_classes, n_features]``
|
|
"""
|
|
if self.get_params()['booster'] != 'gblinear':
|
|
raise AttributeError(
|
|
f"Coefficients are not defined for Booster type {self.booster}"
|
|
)
|
|
b = self.get_booster()
|
|
coef = np.array(json.loads(b.get_dump(dump_format='json')[0])['weight'])
|
|
# Logic for multiclass classification
|
|
n_classes = getattr(self, 'n_classes_', None)
|
|
if n_classes is not None:
|
|
if n_classes > 2:
|
|
assert len(coef.shape) == 1
|
|
assert coef.shape[0] % n_classes == 0
|
|
coef = coef.reshape((n_classes, -1))
|
|
return coef
|
|
|
|
@property
|
|
def intercept_(self) -> np.ndarray:
|
|
"""
|
|
Intercept (bias) property
|
|
|
|
.. note:: Intercept is defined only for linear learners
|
|
|
|
Intercept (bias) is only defined when the linear model is chosen as base
|
|
learner (`booster=gblinear`). It is not defined for other base learner types,
|
|
such as tree learners (`booster=gbtree`).
|
|
|
|
Returns
|
|
-------
|
|
intercept_ : array of shape ``(1,)`` or ``[n_classes]``
|
|
"""
|
|
if self.get_params()['booster'] != 'gblinear':
|
|
raise AttributeError(
|
|
f"Intercept (bias) is not defined for Booster type {self.booster}"
|
|
)
|
|
b = self.get_booster()
|
|
return np.array(json.loads(b.get_dump(dump_format='json')[0])['bias'])
|
|
|
|
|
|
PredtT = TypeVar("PredtT", bound=np.ndarray)
|
|
|
|
|
|
def _cls_predict_proba(n_classes: int, prediction: PredtT, vstack: Callable) -> PredtT:
|
|
assert len(prediction.shape) <= 2
|
|
if len(prediction.shape) == 2 and prediction.shape[1] == n_classes:
|
|
# multi-class
|
|
return prediction
|
|
if (
|
|
len(prediction.shape) == 2
|
|
and n_classes == 2
|
|
and prediction.shape[1] >= n_classes
|
|
):
|
|
# multi-label
|
|
return prediction
|
|
# binary logistic function
|
|
classone_probs = prediction
|
|
classzero_probs = 1.0 - classone_probs
|
|
return vstack((classzero_probs, classone_probs)).transpose()
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"Implementation of the scikit-learn API for XGBoost classification.",
|
|
['model', 'objective'], extra_parameters='''
|
|
n_estimators : int
|
|
Number of boosting rounds.
|
|
''')
|
|
class XGBClassifier(XGBModel, XGBClassifierBase):
|
|
# pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self,
|
|
*,
|
|
objective: _SklObjective = "binary:logistic",
|
|
use_label_encoder: bool = False,
|
|
**kwargs: Any
|
|
) -> None:
|
|
# must match the parameters for `get_params`
|
|
self.use_label_encoder = use_label_encoder
|
|
if use_label_encoder is True:
|
|
raise ValueError("Label encoder was removed in 1.6.")
|
|
super().__init__(objective=objective, **kwargs)
|
|
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: array_like,
|
|
y: array_like,
|
|
*,
|
|
sample_weight: Optional[array_like] = None,
|
|
base_margin: Optional[array_like] = None,
|
|
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: Optional[bool] = True,
|
|
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
|
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
|
feature_weights: Optional[array_like] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None
|
|
) -> "XGBClassifier":
|
|
# pylint: disable = attribute-defined-outside-init,too-many-statements
|
|
evals_result: TrainingCallback.EvalsLog = {}
|
|
|
|
if _is_cudf_df(y) or _is_cudf_ser(y):
|
|
import cupy as cp # pylint: disable=E0401
|
|
|
|
self.classes_ = cp.unique(y.values)
|
|
self.n_classes_ = len(self.classes_)
|
|
expected_classes = cp.arange(self.n_classes_)
|
|
elif _is_cupy_array(y):
|
|
import cupy as cp # pylint: disable=E0401
|
|
|
|
self.classes_ = cp.unique(y)
|
|
self.n_classes_ = len(self.classes_)
|
|
expected_classes = cp.arange(self.n_classes_)
|
|
else:
|
|
self.classes_ = np.unique(np.asarray(y))
|
|
self.n_classes_ = len(self.classes_)
|
|
expected_classes = np.arange(self.n_classes_)
|
|
if (
|
|
self.classes_.shape != expected_classes.shape
|
|
or not (self.classes_ == expected_classes).all()
|
|
):
|
|
raise ValueError(
|
|
f"Invalid classes inferred from unique values of `y`. "
|
|
f"Expected: {expected_classes}, got {self.classes_}"
|
|
)
|
|
|
|
params = self.get_xgb_params()
|
|
|
|
if callable(self.objective):
|
|
obj: Optional[
|
|
Callable[[np.ndarray, DMatrix], Tuple[np.ndarray, np.ndarray]]
|
|
] = _objective_decorator(self.objective)
|
|
# Use default value. Is it really not used ?
|
|
params["objective"] = "binary:logistic"
|
|
else:
|
|
obj = None
|
|
|
|
if self.n_classes_ > 2:
|
|
# Switch to using a multiclass objective in the underlying XGB instance
|
|
if params.get("objective", None) != "multi:softmax":
|
|
params["objective"] = "multi:softprob"
|
|
params["num_class"] = self.n_classes_
|
|
|
|
model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
|
|
xgb_model, eval_metric, params, early_stopping_rounds, callbacks
|
|
)
|
|
train_dmatrix, evals = _wrap_evaluation_matrices(
|
|
missing=self.missing,
|
|
X=X,
|
|
y=y,
|
|
group=None,
|
|
qid=None,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
feature_weights=feature_weights,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
base_margin_eval_set=base_margin_eval_set,
|
|
eval_group=None,
|
|
eval_qid=None,
|
|
create_dmatrix=lambda **kwargs: DMatrix(nthread=self.n_jobs, **kwargs),
|
|
enable_categorical=self.enable_categorical,
|
|
)
|
|
|
|
self._Booster = train(
|
|
params,
|
|
train_dmatrix,
|
|
self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
evals_result=evals_result,
|
|
obj=obj,
|
|
custom_metric=metric,
|
|
verbose_eval=verbose,
|
|
xgb_model=model,
|
|
callbacks=callbacks,
|
|
)
|
|
|
|
if not callable(self.objective):
|
|
self.objective = params["objective"]
|
|
|
|
self._set_evaluation_result(evals_result)
|
|
return self
|
|
|
|
assert XGBModel.fit.__doc__ is not None
|
|
fit.__doc__ = XGBModel.fit.__doc__.replace(
|
|
'Fit gradient boosting model',
|
|
'Fit gradient boosting classifier', 1)
|
|
|
|
def predict(
|
|
self,
|
|
X: array_like,
|
|
output_margin: bool = False,
|
|
ntree_limit: Optional[int] = None,
|
|
validate_features: bool = True,
|
|
base_margin: Optional[array_like] = None,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> np.ndarray:
|
|
class_probs = super().predict(
|
|
X=X,
|
|
output_margin=output_margin,
|
|
ntree_limit=ntree_limit,
|
|
validate_features=validate_features,
|
|
base_margin=base_margin,
|
|
iteration_range=iteration_range,
|
|
)
|
|
if output_margin:
|
|
# If output_margin is active, simply return the scores
|
|
return class_probs
|
|
|
|
if len(class_probs.shape) > 1 and self.n_classes_ != 2:
|
|
# multi-class, turns softprob into softmax
|
|
column_indexes: np.ndarray = np.argmax(class_probs, axis=1) # type: ignore
|
|
elif len(class_probs.shape) > 1 and class_probs.shape[1] != 1:
|
|
# multi-label
|
|
column_indexes = np.zeros(class_probs.shape)
|
|
column_indexes[class_probs > 0.5] = 1
|
|
else:
|
|
# turns soft logit into class label
|
|
column_indexes = np.repeat(0, class_probs.shape[0])
|
|
column_indexes[class_probs > 0.5] = 1
|
|
|
|
if hasattr(self, '_le'):
|
|
return self._le.inverse_transform(column_indexes)
|
|
return column_indexes
|
|
|
|
def predict_proba(
|
|
self,
|
|
X: array_like,
|
|
ntree_limit: Optional[int] = None,
|
|
validate_features: bool = True,
|
|
base_margin: Optional[array_like] = None,
|
|
iteration_range: Optional[Tuple[int, int]] = None,
|
|
) -> np.ndarray:
|
|
""" Predict the probability of each `X` example being of a given class.
|
|
|
|
.. note:: This function is only thread safe for `gbtree` and `dart`.
|
|
|
|
Parameters
|
|
----------
|
|
X : array_like
|
|
Feature matrix.
|
|
ntree_limit : int
|
|
Deprecated, use `iteration_range` instead.
|
|
validate_features : bool
|
|
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 : array_like
|
|
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.
|
|
|
|
Returns
|
|
-------
|
|
prediction :
|
|
a numpy array of shape array-like of shape (n_samples, n_classes) with the
|
|
probability of each data example being of a given class.
|
|
"""
|
|
# custom obj: Do nothing as we don't know what to do.
|
|
# softprob: Do nothing, output is proba.
|
|
# softmax: Unsupported by predict_proba()
|
|
# binary:logistic: Expand the prob vector into 2-class matrix after predict.
|
|
# binary:logitraw: Unsupported by predict_proba()
|
|
if self.objective == "multi:softmax":
|
|
# We need to run a Python implementation of softmax for it. Just ask user to
|
|
# use softprob since XGBoost's implementation has mitigation for floating
|
|
# point overflow. No need to reinvent the wheel.
|
|
raise ValueError(
|
|
"multi:softmax doesn't support `predict_proba`. "
|
|
"Switch to `multi:softproba` instead"
|
|
)
|
|
class_probs = super().predict(
|
|
X=X,
|
|
ntree_limit=ntree_limit,
|
|
validate_features=validate_features,
|
|
base_margin=base_margin,
|
|
iteration_range=iteration_range
|
|
)
|
|
# If model is loaded from a raw booster there's no `n_classes_`
|
|
return _cls_predict_proba(getattr(self, "n_classes_", 0), class_probs, np.vstack)
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"scikit-learn API for XGBoost random forest classification.",
|
|
['model', 'objective'],
|
|
extra_parameters='''
|
|
n_estimators : int
|
|
Number of trees in random forest to fit.
|
|
''')
|
|
class XGBRFClassifier(XGBClassifier):
|
|
# pylint: disable=missing-docstring
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self, *,
|
|
learning_rate: float = 1.0,
|
|
subsample: float = 0.8,
|
|
colsample_bynode: float = 0.8,
|
|
reg_lambda: float = 1e-5,
|
|
**kwargs: Any
|
|
):
|
|
super().__init__(learning_rate=learning_rate,
|
|
subsample=subsample,
|
|
colsample_bynode=colsample_bynode,
|
|
reg_lambda=reg_lambda,
|
|
**kwargs)
|
|
_check_rf_callback(self.early_stopping_rounds, self.callbacks)
|
|
|
|
def get_xgb_params(self) -> Dict[str, Any]:
|
|
params = super().get_xgb_params()
|
|
params['num_parallel_tree'] = self.n_estimators
|
|
return params
|
|
|
|
def get_num_boosting_rounds(self) -> int:
|
|
return 1
|
|
|
|
# pylint: disable=unused-argument
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: array_like,
|
|
y: array_like,
|
|
*,
|
|
sample_weight: Optional[array_like] = None,
|
|
base_margin: Optional[array_like] = None,
|
|
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: Optional[bool] = True,
|
|
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
|
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
|
feature_weights: Optional[array_like] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None
|
|
) -> "XGBRFClassifier":
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
_check_rf_callback(early_stopping_rounds, callbacks)
|
|
super().fit(**args)
|
|
return self
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"Implementation of the scikit-learn API for XGBoost regression.",
|
|
['estimators', 'model', 'objective'])
|
|
class XGBRegressor(XGBModel, XGBRegressorBase):
|
|
# pylint: disable=missing-docstring
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self, *, objective: _SklObjective = "reg:squarederror", **kwargs: Any
|
|
) -> None:
|
|
super().__init__(objective=objective, **kwargs)
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"scikit-learn API for XGBoost random forest regression.",
|
|
['model', 'objective'], extra_parameters='''
|
|
n_estimators : int
|
|
Number of trees in random forest to fit.
|
|
''')
|
|
class XGBRFRegressor(XGBRegressor):
|
|
# pylint: disable=missing-docstring
|
|
@_deprecate_positional_args
|
|
def __init__(
|
|
self,
|
|
*,
|
|
learning_rate: float = 1.0,
|
|
subsample: float = 0.8,
|
|
colsample_bynode: float = 0.8,
|
|
reg_lambda: float = 1e-5,
|
|
**kwargs: Any
|
|
) -> None:
|
|
super().__init__(
|
|
learning_rate=learning_rate,
|
|
subsample=subsample,
|
|
colsample_bynode=colsample_bynode,
|
|
reg_lambda=reg_lambda,
|
|
**kwargs
|
|
)
|
|
_check_rf_callback(self.early_stopping_rounds, self.callbacks)
|
|
|
|
def get_xgb_params(self) -> Dict[str, Any]:
|
|
params = super().get_xgb_params()
|
|
params["num_parallel_tree"] = self.n_estimators
|
|
return params
|
|
|
|
def get_num_boosting_rounds(self) -> int:
|
|
return 1
|
|
|
|
# pylint: disable=unused-argument
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: array_like,
|
|
y: array_like,
|
|
*,
|
|
sample_weight: Optional[array_like] = None,
|
|
base_margin: Optional[array_like] = None,
|
|
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: Optional[bool] = True,
|
|
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
|
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
|
feature_weights: Optional[array_like] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None
|
|
) -> "XGBRFRegressor":
|
|
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
|
|
_check_rf_callback(early_stopping_rounds, callbacks)
|
|
super().fit(**args)
|
|
return self
|
|
|
|
|
|
@xgboost_model_doc(
|
|
'Implementation of the Scikit-Learn API for XGBoost Ranking.',
|
|
['estimators', 'model'],
|
|
end_note='''
|
|
.. note::
|
|
|
|
A custom objective function is currently not supported by XGBRanker.
|
|
Likewise, a custom metric function is not supported either.
|
|
|
|
.. note::
|
|
|
|
Query group information is required for ranking tasks by either using the
|
|
`group` parameter or `qid` parameter in `fit` method.
|
|
|
|
Before fitting the model, your data need to be sorted by query group. When fitting
|
|
the model, you need to provide an additional array that contains the size of each
|
|
query group.
|
|
|
|
For example, if your original data look like:
|
|
|
|
+-------+-----------+---------------+
|
|
| qid | label | features |
|
|
+-------+-----------+---------------+
|
|
| 1 | 0 | x_1 |
|
|
+-------+-----------+---------------+
|
|
| 1 | 1 | x_2 |
|
|
+-------+-----------+---------------+
|
|
| 1 | 0 | x_3 |
|
|
+-------+-----------+---------------+
|
|
| 2 | 0 | x_4 |
|
|
+-------+-----------+---------------+
|
|
| 2 | 1 | x_5 |
|
|
+-------+-----------+---------------+
|
|
| 2 | 1 | x_6 |
|
|
+-------+-----------+---------------+
|
|
| 2 | 1 | x_7 |
|
|
+-------+-----------+---------------+
|
|
|
|
then your group array should be ``[3, 4]``. Sometimes using query id (`qid`)
|
|
instead of group can be more convenient.
|
|
''')
|
|
class XGBRanker(XGBModel, XGBRankerMixIn):
|
|
# pylint: disable=missing-docstring,too-many-arguments,invalid-name
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, objective: str = "rank:pairwise", **kwargs: Any):
|
|
super().__init__(objective=objective, **kwargs)
|
|
if callable(self.objective):
|
|
raise ValueError("custom objective function not supported by XGBRanker")
|
|
if "rank:" not in objective:
|
|
raise ValueError("please use XGBRanker for ranking task")
|
|
|
|
@_deprecate_positional_args
|
|
def fit(
|
|
self,
|
|
X: array_like,
|
|
y: array_like,
|
|
*,
|
|
group: Optional[array_like] = None,
|
|
qid: Optional[array_like] = None,
|
|
sample_weight: Optional[array_like] = None,
|
|
base_margin: Optional[array_like] = None,
|
|
eval_set: Optional[Sequence[Tuple[array_like, array_like]]] = None,
|
|
eval_group: Optional[Sequence[array_like]] = None,
|
|
eval_qid: Optional[Sequence[array_like]] = None,
|
|
eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
|
|
early_stopping_rounds: Optional[int] = None,
|
|
verbose: Optional[bool] = False,
|
|
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
|
|
sample_weight_eval_set: Optional[Sequence[array_like]] = None,
|
|
base_margin_eval_set: Optional[Sequence[array_like]] = None,
|
|
feature_weights: Optional[array_like] = None,
|
|
callbacks: Optional[Sequence[TrainingCallback]] = None
|
|
) -> "XGBRanker":
|
|
# pylint: disable = attribute-defined-outside-init,arguments-differ
|
|
"""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
|
|
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.
|
|
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**.
|
|
|
|
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` and an evaluation set is used, writes the evaluation metric
|
|
measured on the validation set to stderr.
|
|
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. Only available for `hist`, `gpu_hist` and
|
|
`exact` tree methods.
|
|
|
|
callbacks :
|
|
.. deprecated: 1.6.0
|
|
Use `callbacks` in :py:meth:`__init__` or :py:methd:`set_params` instead.
|
|
"""
|
|
# check if group information is provided
|
|
if group is None and qid is None:
|
|
raise ValueError("group or qid is required for ranking task")
|
|
|
|
if eval_set is not None:
|
|
if eval_group is None and eval_qid is None:
|
|
raise ValueError(
|
|
"eval_group or eval_qid is required if eval_set is not None")
|
|
train_dmatrix, evals = _wrap_evaluation_matrices(
|
|
missing=self.missing,
|
|
X=X,
|
|
y=y,
|
|
group=group,
|
|
qid=qid,
|
|
sample_weight=sample_weight,
|
|
base_margin=base_margin,
|
|
feature_weights=feature_weights,
|
|
eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
base_margin_eval_set=base_margin_eval_set,
|
|
eval_group=eval_group,
|
|
eval_qid=eval_qid,
|
|
create_dmatrix=lambda **kwargs: DMatrix(nthread=self.n_jobs, **kwargs),
|
|
enable_categorical=self.enable_categorical,
|
|
)
|
|
|
|
evals_result: TrainingCallback.EvalsLog = {}
|
|
params = self.get_xgb_params()
|
|
|
|
model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
|
|
xgb_model, eval_metric, params, early_stopping_rounds, callbacks
|
|
)
|
|
if callable(metric):
|
|
raise ValueError(
|
|
'Custom evaluation metric is not yet supported for XGBRanker.'
|
|
)
|
|
|
|
self._Booster = train(
|
|
params,
|
|
train_dmatrix,
|
|
self.get_num_boosting_rounds(),
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
evals=evals,
|
|
evals_result=evals_result,
|
|
custom_metric=metric,
|
|
verbose_eval=verbose, xgb_model=model,
|
|
callbacks=callbacks
|
|
)
|
|
|
|
self.objective = params["objective"]
|
|
|
|
self._set_evaluation_result(evals_result)
|
|
return self
|