fcfeb4959c
Deprecate positional arguments in following functions: - `__init__` for all classes in sklearn module. - `fit` method for all classes in sklearn module. - dask interface. - `set_info` for `DMatrix` class. Refactor the evaluation matrices handling.
1350 lines
54 KiB
Python
1350 lines
54 KiB
Python
# coding: utf-8
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# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912, C0302
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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 numpy as np
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from .core import Booster, DMatrix, XGBoostError, _deprecate_positional_args
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from .training import train
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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 (SKLEARN_INSTALLED, XGBModelBase,
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XGBClassifierBase, XGBRegressorBase, XGBoostLabelEncoder)
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def _objective_decorator(func):
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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: callable
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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: callable
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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, dmatrix):
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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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__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 = '''
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max_depth : int
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Maximum tree depth for base learners.
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learning_rate : float
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Boosting learning rate (xgb's "eta")
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verbosity : int
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The degree of verbosity. Valid values are 0 (silent) - 3 (debug).
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objective : string or callable
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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: string
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Specify which booster to use: gbtree, gblinear or dart.
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tree_method: string
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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 parameters
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document.
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n_jobs : 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 dowm both
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algorithms.
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gamma : 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 : float
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Minimum sum of instance weight(hessian) needed in a child.
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max_delta_step : int
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Maximum delta step we allow each tree's weight estimation to be.
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subsample : float
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Subsample ratio of the training instance.
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colsample_bytree : float
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Subsample ratio of columns when constructing each tree.
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colsample_bylevel : float
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Subsample ratio of columns for each level.
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colsample_bynode : float
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Subsample ratio of columns for each split.
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reg_alpha : float (xgb's alpha)
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L1 regularization term on weights
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reg_lambda : float (xgb's lambda)
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L2 regularization term on weights
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scale_pos_weight : float
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Balancing of positive and negative weights.
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base_score:
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The initial prediction score of all instances, global bias.
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random_state : 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: int
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Used for boosting random forest.
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monotone_constraints : str
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Constraint of variable monotonicity. See tutorial for more
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information.
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interaction_constraints : 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: string, default "gain"
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The feature importance type for the feature_importances\\_ property:
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either "gain", "weight", "cover", "total_gain" or "total_cover".
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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(header, items, extra_parameters=None, end_note=None):
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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 commom 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):
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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):
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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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@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__(self, max_depth=None, learning_rate=None, n_estimators=100,
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verbosity=None, objective=None, booster=None,
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tree_method=None, n_jobs=None, gamma=None,
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min_child_weight=None, max_delta_step=None, subsample=None,
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colsample_bytree=None, colsample_bylevel=None,
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colsample_bynode=None, reg_alpha=None, reg_lambda=None,
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scale_pos_weight=None, base_score=None, random_state=None,
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missing=np.nan, num_parallel_tree=None,
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monotone_constraints=None, interaction_constraints=None,
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importance_type="gain", gpu_id=None,
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validate_parameters=None, **kwargs):
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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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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
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self.subsample = subsample
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self.colsample_bytree = colsample_bytree
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self.colsample_bylevel = colsample_bylevel
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self.colsample_bynode = colsample_bynode
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self.reg_alpha = reg_alpha
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self.reg_lambda = reg_lambda
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self.scale_pos_weight = scale_pos_weight
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self.base_score = base_score
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self.missing = missing
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self.num_parallel_tree = num_parallel_tree
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self.kwargs = kwargs
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self.random_state = random_state
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self.n_jobs = n_jobs
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self.monotone_constraints = monotone_constraints
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self.interaction_constraints = interaction_constraints
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self.importance_type = importance_type
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self.gpu_id = gpu_id
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self.validate_parameters = validate_parameters
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def _wrap_evaluation_matrices(self, X, y, group,
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sample_weight, base_margin, feature_weights,
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eval_set, sample_weight_eval_set, eval_group,
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label_transform=lambda x: x):
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'''Convert array_like evaluation matrices into DMatrix'''
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if sample_weight_eval_set is not None:
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assert eval_set is not None
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assert len(sample_weight_eval_set) == len(eval_set)
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if eval_group is not None:
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assert eval_set is not None
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assert len(eval_group) == len(eval_set)
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y = label_transform(y)
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train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
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base_margin=base_margin,
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missing=self.missing, nthread=self.n_jobs)
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train_dmatrix.set_info(feature_weights=feature_weights, group=group)
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if eval_set is not None:
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if sample_weight_eval_set is None:
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sample_weight_eval_set = [None] * len(eval_set)
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if eval_group is None:
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eval_group = [None] * len(eval_set)
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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 valid_X is X and valid_y is y and \
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sample_weight_eval_set[i] is sample_weight and eval_group[i] is group:
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evals.append(train_dmatrix)
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else:
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m = DMatrix(valid_X,
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label=label_transform(valid_y),
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missing=self.missing, weight=sample_weight_eval_set[i],
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nthread=self.n_jobs)
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m.set_info(group=eval_group[i])
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evals.append(m)
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nevals = len(evals)
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eval_names = ["validation_{}".format(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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evals = ()
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return train_dmatrix, evals
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def _more_tags(self):
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'''Tags used for scikit-learn data validation.'''
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return {'allow_nan': True, 'no_validation': True}
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def get_booster(self):
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"""Get the underlying xgboost Booster of this model.
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This will raise an exception when fit was not called
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Returns
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-------
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booster : a xgboost booster of underlying model
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"""
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if not hasattr(self, '_Booster'):
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from sklearn.exceptions import NotFittedError
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raise NotFittedError('need to call fit or load_model beforehand')
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return self._Booster
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def set_params(self, **params):
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"""Set the parameters of this estimator. Modification of the sklearn method to
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allow unknown kwargs. This allows using the full range of xgboost
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parameters that are not defined as member variables in sklearn grid
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search.
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Returns
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-------
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self
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"""
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if not params:
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# Simple optimization to gain speed (inspect is slow)
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return self
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# this concatenates kwargs into paraemters, enabling `get_params` for
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# obtaining parameters from keyword paraemters.
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for key, value in params.items():
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if hasattr(self, key):
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setattr(self, key, value)
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else:
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self.kwargs[key] = value
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return self
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def get_params(self, deep=True):
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# pylint: disable=attribute-defined-outside-init
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"""Get parameters."""
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# Based on: https://stackoverflow.com/questions/59248211
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# The basic flow in `get_params` is:
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# 0. Return parameters in subclass first, by using inspect.
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# 1. Return parameters in `XGBModel` (the base class).
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# 2. Return whatever in `**kwargs`.
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# 3. Merge them.
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params = super().get_params(deep)
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cp = copy.copy(self)
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cp.__class__ = cp.__class__.__bases__[0]
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params.update(cp.__class__.get_params(cp, deep))
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# if kwargs is a dict, update params accordingly
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if isinstance(self.kwargs, dict):
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params.update(self.kwargs)
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if isinstance(params['random_state'], np.random.RandomState):
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params['random_state'] = params['random_state'].randint(
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np.iinfo(np.int32).max)
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def parse_parameter(value):
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for t in (int, float, str):
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try:
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ret = t(value)
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return ret
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except ValueError:
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continue
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return None
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# Get internal parameter values
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try:
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config = json.loads(self.get_booster().save_config())
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stack = [config]
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internal = {}
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while stack:
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obj = stack.pop()
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for k, v in obj.items():
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if k.endswith('_param'):
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for p_k, p_v in v.items():
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internal[p_k] = p_v
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elif isinstance(v, dict):
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stack.append(v)
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for k, v in internal.items():
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if k in params.keys() and params[k] is None:
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params[k] = parse_parameter(v)
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except ValueError:
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pass
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return params
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def get_xgb_params(self):
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"""Get xgboost specific parameters."""
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params = self.get_params()
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# Parameters that should not go into native learner.
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wrapper_specific = {
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'importance_type', 'kwargs', 'missing', 'n_estimators', 'use_label_encoder'}
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filtered = dict()
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for k, v in params.items():
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if k not in wrapper_specific:
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filtered[k] = v
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return filtered
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def get_num_boosting_rounds(self):
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"""Gets the number of xgboost boosting rounds."""
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return self.n_estimators
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def save_model(self, fname: str):
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"""Save the model to a file.
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The model is saved in an XGBoost internal format which is universal
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among the various XGBoost interfaces. Auxiliary attributes of the
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Python Booster object (such as feature names) will not be saved.
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.. note::
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See:
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https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
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Parameters
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----------
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fname : string
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Output file name
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"""
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meta = dict()
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for k, v in self.__dict__.items():
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if k == '_le':
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meta['_le'] = self._le.to_json()
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continue
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if k == '_Booster':
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continue
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if k == 'classes_':
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# numpy array is not JSON serializable
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meta['classes_'] = self.classes_.tolist()
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continue
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try:
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json.dumps({k: v})
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meta[k] = v
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except TypeError:
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warnings.warn(str(k) + ' is not saved in Scikit-Learn meta.')
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meta['type'] = type(self).__name__
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meta = json.dumps(meta)
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self.get_booster().set_attr(scikit_learn=meta)
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self.get_booster().save_model(fname)
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# Delete the attribute after save
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self.get_booster().set_attr(scikit_learn=None)
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def load_model(self, fname):
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# pylint: disable=attribute-defined-outside-init
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"""Load the model from a file.
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The model is loaded from an XGBoost internal format which is universal
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among the various XGBoost interfaces. Auxiliary attributes of the
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Python Booster object (such as feature names) will not be loaded.
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Parameters
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----------
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fname : string
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Input file name.
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"""
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if not hasattr(self, '_Booster'):
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self._Booster = Booster({'n_jobs': self.n_jobs})
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self._Booster.load_model(fname)
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meta = self._Booster.attr('scikit_learn')
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if meta is None:
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warnings.warn(
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'Loading a native XGBoost model with Scikit-Learn interface.')
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return
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meta = json.loads(meta)
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states = dict()
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for k, v in meta.items():
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if k == '_le':
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self._le = XGBoostLabelEncoder()
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self._le.from_json(v)
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continue
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if k == 'classes_':
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self.classes_ = np.array(v)
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continue
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if k == 'use_label_encoder':
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self.use_label_encoder = bool(v)
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continue
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if k == 'type' and type(self).__name__ != v:
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msg = 'Current model type: {}, '.format(type(self).__name__) + \
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'type of model in file: {}'.format(v)
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raise TypeError(msg)
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if k == 'type':
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continue
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states[k] = v
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self.__dict__.update(states)
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# Delete the attribute after load
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self.get_booster().set_attr(scikit_learn=None)
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@_deprecate_positional_args
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def fit(self, X, y, *, sample_weight=None, base_margin=None,
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eval_set=None, eval_metric=None, early_stopping_rounds=None,
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verbose=True, xgb_model=None, sample_weight_eval_set=None,
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feature_weights=None,
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callbacks=None):
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# pylint: disable=invalid-name,attribute-defined-outside-init
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"""Fit gradient boosting model
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|
|
Parameters
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|
----------
|
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X : array_like
|
|
Feature matrix
|
|
y : array_like
|
|
Labels
|
|
sample_weight : array_like
|
|
instance weights
|
|
base_margin : array_like
|
|
global bias for each instance.
|
|
eval_set : list, optional
|
|
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
|
|
If a str, should be a built-in evaluation metric to use. See
|
|
doc/parameter.rst.
|
|
If a list of str, should be the list of multiple built-in evaluation metrics
|
|
to use.
|
|
If callable, a custom evaluation metric. The call
|
|
signature is ``func(y_predicted, y_true)`` where ``y_true`` will be a
|
|
DMatrix object such that you may need to call the ``get_label``
|
|
method. It must return a str, value pair where the str is a name
|
|
for the evaluation and value is the value of the evaluation
|
|
function. The callable custom objective is always minimized.
|
|
early_stopping_rounds : int
|
|
Activates early stopping. Validation metric needs to improve at least once in
|
|
every **early_stopping_rounds** round(s) to continue training.
|
|
Requires at least one item in **eval_set**.
|
|
The method returns the model from the last iteration (not the best one).
|
|
If there's more than one item in **eval_set**, the last entry will be used
|
|
for early stopping.
|
|
If there's more than one metric in **eval_metric**, the last metric will be
|
|
used for early stopping.
|
|
If early stopping occurs, the model will have three additional fields:
|
|
``clf.best_score``, ``clf.best_iteration`` and ``clf.best_ntree_limit``.
|
|
verbose : bool
|
|
If `verbose` and an evaluation set is used, writes the evaluation
|
|
metric measured on the validation set to stderr.
|
|
xgb_model : str
|
|
file name of stored XGBoost model or 'Booster' instance XGBoost model to be
|
|
loaded before training (allows training continuation).
|
|
sample_weight_eval_set : list, optional
|
|
A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
|
|
instance weights on the i-th validation set.
|
|
feature_weights: array_like
|
|
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 : list of callback functions
|
|
List of callback functions that are applied at end of each iteration.
|
|
It is possible to use predefined callbacks by using :ref:`callback_api`.
|
|
Example:
|
|
|
|
.. code-block:: python
|
|
|
|
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
|
|
save_best=True)]
|
|
|
|
"""
|
|
self.n_features_in_ = X.shape[1]
|
|
|
|
train_dmatrix = DMatrix(data=X, label=y, weight=sample_weight,
|
|
base_margin=base_margin,
|
|
missing=self.missing,
|
|
nthread=self.n_jobs)
|
|
train_dmatrix.set_info(feature_weights=feature_weights)
|
|
|
|
evals_result = {}
|
|
|
|
train_dmatrix, evals = self._wrap_evaluation_matrices(
|
|
X, y, group=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, eval_group=None)
|
|
params = self.get_xgb_params()
|
|
|
|
if callable(self.objective):
|
|
obj = _objective_decorator(self.objective)
|
|
params["objective"] = "reg:squarederror"
|
|
else:
|
|
obj = None
|
|
|
|
feval = eval_metric if callable(eval_metric) else None
|
|
if eval_metric is not None:
|
|
if callable(eval_metric):
|
|
eval_metric = None
|
|
else:
|
|
params.update({'eval_metric': eval_metric})
|
|
|
|
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, feval=feval,
|
|
verbose_eval=verbose, xgb_model=xgb_model,
|
|
callbacks=callbacks)
|
|
|
|
if evals_result:
|
|
for val in evals_result.items():
|
|
evals_result_key = list(val[1].keys())[0]
|
|
evals_result[val[0]][evals_result_key] = val[1][
|
|
evals_result_key]
|
|
self.evals_result_ = evals_result
|
|
|
|
if early_stopping_rounds is not None:
|
|
self.best_score = self._Booster.best_score
|
|
self.best_iteration = self._Booster.best_iteration
|
|
self.best_ntree_limit = self._Booster.best_ntree_limit
|
|
return self
|
|
|
|
def predict(self, data, output_margin=False, ntree_limit=None,
|
|
validate_features=True, base_margin=None):
|
|
"""
|
|
Predict with `data`.
|
|
|
|
.. note:: This function is not thread safe.
|
|
|
|
For each booster object, predict can only be called from one thread.
|
|
If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
|
|
of model object and then call ``predict()``.
|
|
|
|
.. code-block:: python
|
|
|
|
preds = bst.predict(dtest, ntree_limit=num_round)
|
|
|
|
Parameters
|
|
----------
|
|
data : numpy.array/scipy.sparse
|
|
Data to predict with
|
|
output_margin : bool
|
|
Whether to output the raw untransformed margin value.
|
|
ntree_limit : int
|
|
Limit number of trees in the prediction; defaults to best_ntree_limit if defined
|
|
(i.e. it has been trained with early stopping), otherwise 0 (use all trees).
|
|
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.
|
|
Returns
|
|
-------
|
|
prediction : numpy array
|
|
"""
|
|
# pylint: disable=missing-docstring,invalid-name
|
|
test_dmatrix = DMatrix(data, base_margin=base_margin,
|
|
missing=self.missing, nthread=self.n_jobs)
|
|
# get ntree_limit to use - if none specified, default to
|
|
# best_ntree_limit if defined, otherwise 0.
|
|
if ntree_limit is None:
|
|
ntree_limit = getattr(self, "best_ntree_limit", 0)
|
|
return self.get_booster().predict(test_dmatrix,
|
|
output_margin=output_margin,
|
|
ntree_limit=ntree_limit,
|
|
validate_features=validate_features)
|
|
|
|
def apply(self, X, ntree_limit=0):
|
|
"""Return the predicted leaf every tree for each sample.
|
|
|
|
Parameters
|
|
----------
|
|
X : array_like, shape=[n_samples, n_features]
|
|
Input features matrix.
|
|
|
|
ntree_limit : int
|
|
Limit number of trees in the prediction; defaults to 0 (use all trees).
|
|
|
|
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.
|
|
"""
|
|
test_dmatrix = DMatrix(X, missing=self.missing, nthread=self.n_jobs)
|
|
return self.get_booster().predict(test_dmatrix,
|
|
pred_leaf=True,
|
|
ntree_limit=ntree_limit)
|
|
|
|
def evals_result(self):
|
|
"""Return the evaluation results.
|
|
|
|
If **eval_set** is passed to the `fit` function, you can call
|
|
``evals_result()`` to get evaluation results for all passed **eval_sets**.
|
|
When **eval_metric** is also passed to the `fit` function, the
|
|
**evals_result** will contain the **eval_metrics** passed to the `fit` function.
|
|
|
|
Returns
|
|
-------
|
|
evals_result : dictionary
|
|
|
|
Example
|
|
-------
|
|
|
|
.. code-block:: python
|
|
|
|
param_dist = {'objective':'binary:logistic', 'n_estimators':2}
|
|
|
|
clf = xgb.XGBModel(**param_dist)
|
|
|
|
clf.fit(X_train, y_train,
|
|
eval_set=[(X_train, y_train), (X_test, y_test)],
|
|
eval_metric='logloss',
|
|
verbose=True)
|
|
|
|
evals_result = clf.evals_result()
|
|
|
|
The variable **evals_result** will contain:
|
|
|
|
.. code-block:: python
|
|
|
|
{'validation_0': {'logloss': ['0.604835', '0.531479']},
|
|
'validation_1': {'logloss': ['0.41965', '0.17686']}}
|
|
"""
|
|
if self.evals_result_:
|
|
evals_result = self.evals_result_
|
|
else:
|
|
raise XGBoostError('No results.')
|
|
|
|
return evals_result
|
|
|
|
@property
|
|
def feature_importances_(self):
|
|
"""
|
|
Feature importances property
|
|
|
|
.. note:: Feature importance is defined only for tree boosters
|
|
|
|
Feature importance is only defined when the decision tree model is chosen as base
|
|
learner (`booster=gbtree`). It is not defined for other base learner types, such
|
|
as linear learners (`booster=gblinear`).
|
|
|
|
Returns
|
|
-------
|
|
feature_importances_ : array of shape ``[n_features]``
|
|
|
|
"""
|
|
if self.get_params()['booster'] not in {'gbtree', 'dart'}:
|
|
raise AttributeError(
|
|
'Feature importance is not defined for Booster type {}'
|
|
.format(self.booster))
|
|
b = self.get_booster()
|
|
score = b.get_score(importance_type=self.importance_type)
|
|
all_features = [score.get(f, 0.) for f in b.feature_names]
|
|
all_features = np.array(all_features, dtype=np.float32)
|
|
return all_features / all_features.sum()
|
|
|
|
@property
|
|
def coef_(self):
|
|
"""
|
|
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(
|
|
'Coefficients are not defined for Booster type {}'
|
|
.format(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):
|
|
"""
|
|
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(
|
|
'Intercept (bias) is not defined for Booster type {}'
|
|
.format(self.booster))
|
|
b = self.get_booster()
|
|
return np.array(json.loads(b.get_dump(dump_format='json')[0])['bias'])
|
|
|
|
|
|
@xgboost_model_doc(
|
|
"Implementation of the scikit-learn API for XGBoost classification.",
|
|
['model', 'objective'], extra_parameters='''
|
|
n_estimators : int
|
|
Number of boosting rounds.
|
|
use_label_encoder : bool
|
|
(Deprecated) Use the label encoder from scikit-learn to encode the labels. For new code,
|
|
we recommend that you set this parameter to False.
|
|
''')
|
|
class XGBClassifier(XGBModel, XGBClassifierBase):
|
|
# pylint: disable=missing-docstring,invalid-name,too-many-instance-attributes
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, objective="binary:logistic", use_label_encoder=True, **kwargs):
|
|
self.use_label_encoder = use_label_encoder
|
|
super().__init__(objective=objective, **kwargs)
|
|
|
|
@_deprecate_positional_args
|
|
def fit(self, X, y, *, sample_weight=None, base_margin=None,
|
|
eval_set=None, eval_metric=None,
|
|
early_stopping_rounds=None, verbose=True, xgb_model=None,
|
|
sample_weight_eval_set=None, feature_weights=None, callbacks=None):
|
|
# pylint: disable = attribute-defined-outside-init,arguments-differ,too-many-statements
|
|
|
|
can_use_label_encoder = True
|
|
label_encoding_check_error = (
|
|
'The label must consist of integer labels of form 0, 1, 2, ..., [num_class - 1].')
|
|
label_encoder_deprecation_msg = (
|
|
'The use of label encoder in XGBClassifier is deprecated and will be ' +
|
|
'removed in a future release. To remove this warning, do the ' +
|
|
'following: 1) Pass option use_label_encoder=False when constructing ' +
|
|
'XGBClassifier object; and 2) Encode your labels (y) as integers ' +
|
|
'starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].')
|
|
|
|
evals_result = {}
|
|
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_)
|
|
can_use_label_encoder = False
|
|
if not cp.array_equal(self.classes_, cp.arange(self.n_classes_)):
|
|
raise ValueError(label_encoding_check_error)
|
|
elif _is_cupy_array(y):
|
|
import cupy as cp # pylint: disable=E0401
|
|
self.classes_ = cp.unique(y)
|
|
self.n_classes_ = len(self.classes_)
|
|
can_use_label_encoder = False
|
|
if not cp.array_equal(self.classes_, cp.arange(self.n_classes_)):
|
|
raise ValueError(label_encoding_check_error)
|
|
else:
|
|
self.classes_ = np.unique(y)
|
|
self.n_classes_ = len(self.classes_)
|
|
if not self.use_label_encoder and (
|
|
not np.array_equal(self.classes_, np.arange(self.n_classes_))):
|
|
raise ValueError(label_encoding_check_error)
|
|
|
|
xgb_options = self.get_xgb_params()
|
|
|
|
if callable(self.objective):
|
|
obj = _objective_decorator(self.objective)
|
|
# Use default value. Is it really not used ?
|
|
xgb_options["objective"] = "binary:logistic"
|
|
else:
|
|
obj = None
|
|
|
|
if self.n_classes_ > 2:
|
|
# Switch to using a multiclass objective in the underlying
|
|
# XGB instance
|
|
xgb_options['objective'] = 'multi:softprob'
|
|
xgb_options['num_class'] = self.n_classes_
|
|
|
|
feval = eval_metric if callable(eval_metric) else None
|
|
if eval_metric is not None:
|
|
if callable(eval_metric):
|
|
eval_metric = None
|
|
else:
|
|
xgb_options.update({"eval_metric": eval_metric})
|
|
|
|
if self.use_label_encoder:
|
|
if not can_use_label_encoder:
|
|
raise ValueError('The option use_label_encoder=True is incompatible with inputs ' +
|
|
'of type cuDF or cuPy. Please set use_label_encoder=False when ' +
|
|
'constructing XGBClassifier object. NOTE: ' +
|
|
label_encoder_deprecation_msg)
|
|
warnings.warn(label_encoder_deprecation_msg, UserWarning)
|
|
self._le = XGBoostLabelEncoder().fit(y)
|
|
label_transform = self._le.transform
|
|
else:
|
|
label_transform = (lambda x: x)
|
|
|
|
if len(X.shape) != 2:
|
|
# Simply raise an error here since there might be many
|
|
# different ways of reshaping
|
|
raise ValueError(
|
|
'Please reshape the input data X into 2-dimensional matrix.')
|
|
|
|
self._features_count = X.shape[1]
|
|
self.n_features_in_ = self._features_count
|
|
|
|
train_dmatrix, evals = self._wrap_evaluation_matrices(
|
|
X, y, group=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,
|
|
eval_group=None, label_transform=label_transform)
|
|
|
|
self._Booster = train(xgb_options, train_dmatrix,
|
|
self.get_num_boosting_rounds(),
|
|
evals=evals,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
evals_result=evals_result, obj=obj, feval=feval,
|
|
verbose_eval=verbose, xgb_model=xgb_model,
|
|
callbacks=callbacks)
|
|
|
|
self.objective = xgb_options["objective"]
|
|
if evals_result:
|
|
for val in evals_result.items():
|
|
evals_result_key = list(val[1].keys())[0]
|
|
evals_result[val[0]][
|
|
evals_result_key] = val[1][evals_result_key]
|
|
self.evals_result_ = evals_result
|
|
|
|
if early_stopping_rounds is not None:
|
|
self.best_score = self._Booster.best_score
|
|
self.best_iteration = self._Booster.best_iteration
|
|
self.best_ntree_limit = self._Booster.best_ntree_limit
|
|
|
|
return self
|
|
|
|
fit.__doc__ = XGBModel.fit.__doc__.replace(
|
|
'Fit gradient boosting model',
|
|
'Fit gradient boosting classifier', 1)
|
|
|
|
def predict(self, data, output_margin=False, ntree_limit=None,
|
|
validate_features=True, base_margin=None):
|
|
"""
|
|
Predict with `data`.
|
|
|
|
.. note:: This function is not thread safe.
|
|
|
|
For each booster object, predict can only be called from one thread.
|
|
If you want to run prediction using multiple thread, call
|
|
``xgb.copy()`` to make copies of model object and then call
|
|
``predict()``.
|
|
|
|
.. code-block:: python
|
|
|
|
preds = bst.predict(dtest, ntree_limit=num_round)
|
|
|
|
Parameters
|
|
----------
|
|
data : array_like
|
|
Feature matrix.
|
|
output_margin : bool
|
|
Whether to output the raw untransformed margin value.
|
|
ntree_limit : int
|
|
Limit number of trees in the prediction; defaults to
|
|
best_ntree_limit if defined (i.e. it has been trained with early
|
|
stopping), otherwise 0 (use all trees).
|
|
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.
|
|
|
|
Returns
|
|
-------
|
|
prediction : numpy array
|
|
"""
|
|
test_dmatrix = DMatrix(data, base_margin=base_margin,
|
|
missing=self.missing, nthread=self.n_jobs)
|
|
if ntree_limit is None:
|
|
ntree_limit = getattr(self, "best_ntree_limit", 0)
|
|
class_probs = self.get_booster().predict(
|
|
test_dmatrix,
|
|
output_margin=output_margin,
|
|
ntree_limit=ntree_limit,
|
|
validate_features=validate_features)
|
|
if output_margin:
|
|
# If output_margin is active, simply return the scores
|
|
return class_probs
|
|
|
|
if len(class_probs.shape) > 1:
|
|
column_indexes = np.argmax(class_probs, axis=1)
|
|
else:
|
|
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, data, ntree_limit=None, validate_features=False,
|
|
base_margin=None):
|
|
"""
|
|
Predict the probability of each `data` example being of a given class.
|
|
|
|
.. note:: This function is not thread safe
|
|
|
|
For each booster object, predict can only be called from one
|
|
thread. If you want to run prediction using multiple thread, call
|
|
``xgb.copy()`` to make copies of model object and then call predict
|
|
|
|
Parameters
|
|
----------
|
|
data : array_like
|
|
Feature matrix.
|
|
ntree_limit : int
|
|
Limit number of trees in the prediction; defaults to best_ntree_limit if defined
|
|
(i.e. it has been trained with early stopping), otherwise 0 (use all trees).
|
|
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.
|
|
|
|
Returns
|
|
-------
|
|
prediction : numpy array
|
|
a numpy array with the probability of each data example being of a given class.
|
|
"""
|
|
test_dmatrix = DMatrix(data, base_margin=base_margin,
|
|
missing=self.missing, nthread=self.n_jobs)
|
|
if ntree_limit is None:
|
|
ntree_limit = getattr(self, "best_ntree_limit", 0)
|
|
class_probs = self.get_booster().predict(test_dmatrix,
|
|
ntree_limit=ntree_limit,
|
|
validate_features=validate_features)
|
|
if self.objective == "multi:softprob":
|
|
return class_probs
|
|
classone_probs = class_probs
|
|
classzero_probs = 1.0 - classone_probs
|
|
return np.vstack((classzero_probs, classone_probs)).transpose()
|
|
|
|
def evals_result(self):
|
|
"""Return the evaluation results.
|
|
|
|
If **eval_set** is passed to the `fit` function, you can call
|
|
``evals_result()`` to get evaluation results for all passed **eval_sets**.
|
|
When **eval_metric** is also passed to the `fit` function, the
|
|
**evals_result** will contain the **eval_metrics** passed to the `fit` function.
|
|
|
|
Returns
|
|
-------
|
|
evals_result : dictionary
|
|
|
|
Example
|
|
-------
|
|
|
|
.. code-block:: python
|
|
|
|
param_dist = {'objective':'binary:logistic', 'n_estimators':2}
|
|
|
|
clf = xgb.XGBClassifier(**param_dist)
|
|
|
|
clf.fit(X_train, y_train,
|
|
eval_set=[(X_train, y_train), (X_test, y_test)],
|
|
eval_metric='logloss',
|
|
verbose=True)
|
|
|
|
evals_result = clf.evals_result()
|
|
|
|
The variable **evals_result** will contain
|
|
|
|
.. code-block:: python
|
|
|
|
{'validation_0': {'logloss': ['0.604835', '0.531479']},
|
|
'validation_1': {'logloss': ['0.41965', '0.17686']}}
|
|
"""
|
|
if self.evals_result_:
|
|
evals_result = self.evals_result_
|
|
else:
|
|
raise XGBoostError('No results.')
|
|
|
|
return evals_result
|
|
|
|
|
|
@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.
|
|
use_label_encoder : bool
|
|
(Deprecated) Use the label encoder from scikit-learn to encode the labels. For new code,
|
|
we recommend that you set this parameter to False.
|
|
''')
|
|
class XGBRFClassifier(XGBClassifier):
|
|
# pylint: disable=missing-docstring
|
|
@_deprecate_positional_args
|
|
def __init__(self, *,
|
|
learning_rate=1,
|
|
subsample=0.8,
|
|
colsample_bynode=0.8,
|
|
reg_lambda=1e-5,
|
|
use_label_encoder=True,
|
|
**kwargs):
|
|
super().__init__(learning_rate=learning_rate,
|
|
subsample=subsample,
|
|
colsample_bynode=colsample_bynode,
|
|
reg_lambda=reg_lambda,
|
|
use_label_encoder=use_label_encoder,
|
|
**kwargs)
|
|
|
|
def get_xgb_params(self):
|
|
params = super().get_xgb_params()
|
|
params['num_parallel_tree'] = self.n_estimators
|
|
return params
|
|
|
|
def get_num_boosting_rounds(self):
|
|
return 1
|
|
|
|
|
|
@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="reg:squarederror", **kwargs):
|
|
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=1, subsample=0.8, colsample_bynode=0.8,
|
|
reg_lambda=1e-5, **kwargs):
|
|
super().__init__(learning_rate=learning_rate, subsample=subsample,
|
|
colsample_bynode=colsample_bynode,
|
|
reg_lambda=reg_lambda, **kwargs)
|
|
|
|
def get_xgb_params(self):
|
|
params = super().get_xgb_params()
|
|
params['num_parallel_tree'] = self.n_estimators
|
|
return params
|
|
|
|
def get_num_boosting_rounds(self):
|
|
return 1
|
|
|
|
|
|
@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.
|
|
|
|
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]``.
|
|
''')
|
|
class XGBRanker(XGBModel):
|
|
# pylint: disable=missing-docstring,too-many-arguments,invalid-name
|
|
@_deprecate_positional_args
|
|
def __init__(self, *, objective='rank:pairwise', **kwargs):
|
|
super().__init__(objective=objective, **kwargs)
|
|
if callable(self.objective):
|
|
raise ValueError(
|
|
"custom objective function not supported by XGBRanker")
|
|
if "rank:" not in self.objective:
|
|
raise ValueError("please use XGBRanker for ranking task")
|
|
|
|
@_deprecate_positional_args
|
|
def fit(self, X, y, *, group, sample_weight=None, base_margin=None,
|
|
eval_set=None, sample_weight_eval_set=None,
|
|
eval_group=None, eval_metric=None,
|
|
early_stopping_rounds=None, verbose=False, xgb_model=None,
|
|
feature_weights=None, callbacks=None):
|
|
# pylint: disable = attribute-defined-outside-init,arguments-differ
|
|
"""Fit gradient boosting ranker
|
|
|
|
Parameters
|
|
----------
|
|
X : array_like
|
|
Feature matrix
|
|
y : array_like
|
|
Labels
|
|
group : array_like
|
|
Size of each query group of training data. Should have as many
|
|
elements as the query groups in the training data
|
|
sample_weight : array_like
|
|
Query group weights
|
|
|
|
.. 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 : array_like
|
|
Global bias for each instance.
|
|
eval_set : list, optional
|
|
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.
|
|
sample_weight_eval_set : list, optional
|
|
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.
|
|
|
|
eval_group : list of arrays, optional
|
|
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_metric : str, list of str, optional
|
|
If a str, should be a built-in evaluation metric to use. See
|
|
doc/parameter.rst.
|
|
If a list of str, should be the list of multiple built-in evaluation metrics
|
|
to use. The custom evaluation metric is not yet supported for the ranker.
|
|
early_stopping_rounds : int
|
|
Activates early stopping. Validation metric needs to improve at least once in
|
|
every **early_stopping_rounds** round(s) to continue training.
|
|
Requires at least one item in **eval_set**.
|
|
The method returns the model from the last iteration (not the best one).
|
|
If there's more than one item in **eval_set**, the last entry will be used
|
|
for early stopping.
|
|
If there's more than one metric in **eval_metric**, the last metric
|
|
will be used for early stopping.
|
|
If early stopping occurs, the model will have three additional
|
|
fields: ``clf.best_score``, ``clf.best_iteration`` and
|
|
``clf.best_ntree_limit``.
|
|
verbose : bool
|
|
If `verbose` and an evaluation set is used, writes the evaluation
|
|
metric measured on the validation set to stderr.
|
|
xgb_model : str
|
|
file name of stored XGBoost model or 'Booster' instance XGBoost
|
|
model to be loaded before training (allows training continuation).
|
|
feature_weights: array_like
|
|
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 : list of callback functions
|
|
List of callback functions that are applied at end of each
|
|
iteration. It is possible to use predefined callbacks by using
|
|
:ref:`callback_api`. Example:
|
|
|
|
.. code-block:: python
|
|
|
|
callbacks = [xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
|
|
save_best=True)]
|
|
|
|
"""
|
|
# check if group information is provided
|
|
if group is None:
|
|
raise ValueError("group is required for ranking task")
|
|
|
|
if eval_set is not None:
|
|
if eval_group is None:
|
|
raise ValueError(
|
|
"eval_group is required if eval_set is not None")
|
|
if len(eval_group) != len(eval_set):
|
|
raise ValueError(
|
|
"length of eval_group should match that of eval_set")
|
|
if any(group is None for group in eval_group):
|
|
raise ValueError(
|
|
"group is required for all eval datasets for ranking task")
|
|
|
|
self.n_features_in_ = X.shape[1]
|
|
|
|
train_dmatrix, evals = self._wrap_evaluation_matrices(
|
|
X, y, group=group, sample_weight=sample_weight, base_margin=base_margin,
|
|
feature_weights=feature_weights, eval_set=eval_set,
|
|
sample_weight_eval_set=sample_weight_eval_set,
|
|
eval_group=eval_group)
|
|
|
|
evals_result = {}
|
|
params = self.get_xgb_params()
|
|
|
|
feval = eval_metric if callable(eval_metric) else None
|
|
if eval_metric is not None:
|
|
if callable(eval_metric):
|
|
raise ValueError(
|
|
'Custom evaluation metric is not yet supported for XGBRanker.')
|
|
params.update({'eval_metric': eval_metric})
|
|
|
|
self._Booster = train(params, train_dmatrix,
|
|
self.n_estimators,
|
|
early_stopping_rounds=early_stopping_rounds,
|
|
evals=evals,
|
|
evals_result=evals_result, feval=feval,
|
|
verbose_eval=verbose, xgb_model=xgb_model,
|
|
callbacks=callbacks)
|
|
|
|
self.objective = params["objective"]
|
|
|
|
if evals_result:
|
|
for val in evals_result.items():
|
|
evals_result_key = list(val[1].keys())[0]
|
|
evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
|
|
self.evals_result = evals_result
|
|
|
|
if early_stopping_rounds is not None:
|
|
self.best_score = self._Booster.best_score
|
|
self.best_iteration = self._Booster.best_iteration
|
|
self.best_ntree_limit = self._Booster.best_ntree_limit
|
|
|
|
return self
|
|
|
|
def predict(self, data, output_margin=False,
|
|
ntree_limit=0, validate_features=True, base_margin=None):
|
|
|
|
test_dmatrix = DMatrix(data, base_margin=base_margin,
|
|
missing=self.missing)
|
|
if ntree_limit is None:
|
|
ntree_limit = getattr(self, "best_ntree_limit", 0)
|
|
|
|
return self.get_booster().predict(test_dmatrix,
|
|
output_margin=output_margin,
|
|
ntree_limit=ntree_limit,
|
|
validate_features=validate_features)
|
|
|
|
predict.__doc__ = XGBModel.predict.__doc__
|