439 lines
17 KiB
Python
439 lines
17 KiB
Python
# coding: utf-8
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# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme
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"""Scikit-Learn Wrapper interface for XGBoost."""
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from __future__ import absolute_import
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import numpy as np
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from .core import Booster, DMatrix, XGBoostError
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from .training import train
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from .compat import (SKLEARN_INSTALLED, XGBModelBase,
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XGBClassifierBase, XGBRegressorBase, LabelEncoder)
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class XGBModel(XGBModelBase):
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# pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name
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"""Implementation of the Scikit-Learn API for XGBoost.
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Parameters
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----------
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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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n_estimators : int
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Number of boosted trees to fit.
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silent : boolean
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Whether to print messages while running boosting.
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objective : string
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Specify the learning task and the corresponding learning objective.
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nthread : int
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Number of parallel threads used to run xgboost.
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gamma : float
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Minimum loss reduction required to make a further partition on a leaf node of the tree.
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min_child_weight : int
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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 split, in each level.
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reg_alpha : float (xgb's alpha)
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L2 regularization term on weights
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reg_lambda : float (xgb's lambda)
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L1 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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seed : int
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Random number seed.
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missing : float, optional
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Value in the data which needs to be present as a missing value. If
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None, defaults to np.nan.
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"""
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def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100,
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silent=True, objective="reg:linear",
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nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0,
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subsample=1, colsample_bytree=1, colsample_bylevel=1,
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reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
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base_score=0.5, seed=0, missing=None):
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if not SKLEARN_INSTALLED:
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raise XGBoostError('sklearn needs to be installed in order to use this module')
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self.max_depth = max_depth
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self.learning_rate = learning_rate
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self.n_estimators = n_estimators
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self.silent = silent
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self.objective = objective
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self.nthread = nthread
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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.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.seed = seed
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self.missing = missing if missing is not None else np.nan
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self._Booster = None
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def __setstate__(self, state):
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# backward compatiblity code
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# load booster from raw if it is raw
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# the booster now support pickle
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bst = state["_Booster"]
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if bst is not None and not isinstance(bst, Booster):
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state["_Booster"] = Booster(model_file=bst)
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self.__dict__.update(state)
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def 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 self._Booster is None:
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raise XGBoostError('need to call fit beforehand')
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return self._Booster
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def get_params(self, deep=False):
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"""Get parameter.s"""
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params = super(XGBModel, self).get_params(deep=deep)
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if params['missing'] is np.nan:
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params['missing'] = None # sklearn doesn't handle nan. see #4725
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if not params.get('eval_metric', True):
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del params['eval_metric'] # don't give as None param to Booster
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return params
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def get_xgb_params(self):
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"""Get xgboost type parameters."""
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xgb_params = self.get_params()
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xgb_params['silent'] = 1 if self.silent else 0
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if self.nthread <= 0:
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xgb_params.pop('nthread', None)
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return xgb_params
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def fit(self, X, y, eval_set=None, eval_metric=None,
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early_stopping_rounds=None, verbose=True):
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# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init, redefined-variable-type
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"""
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Fit the gradient boosting model
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Parameters
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----------
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X : array_like
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Feature matrix
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y : array_like
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Labels
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eval_set : list, optional
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A list of (X, y) tuple pairs to use as a validation set for
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early-stopping
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eval_metric : str, callable, optional
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If a str, should be a built-in evaluation metric to use. See
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doc/parameter.md. If callable, a custom evaluation metric. The call
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signature is func(y_predicted, y_true) where y_true will be a
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DMatrix object such that you may need to call the get_label
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method. It must return a str, value pair where the str is a name
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for the evaluation and value is the value of the evaluation
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function. This objective is always minimized.
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early_stopping_rounds : int
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Activates early stopping. Validation error needs to decrease at
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least every <early_stopping_rounds> round(s) to continue training.
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Requires at least one item in evals. If there's more than one,
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will use the last. Returns the model from the last iteration
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(not the best one). If early stopping occurs, the model will
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have two additional fields: bst.best_score and bst.best_iteration.
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verbose : bool
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If `verbose` and an evaluation set is used, writes the evaluation
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metric measured on the validation set to stderr.
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"""
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trainDmatrix = DMatrix(X, label=y, missing=self.missing)
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evals_result = {}
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if eval_set is not None:
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evals = list(DMatrix(x[0], label=x[1]) for x in eval_set)
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evals = list(zip(evals, ["validation_{}".format(i) for i in
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range(len(evals))]))
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else:
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evals = ()
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params = self.get_xgb_params()
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feval = eval_metric if callable(eval_metric) else None
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if eval_metric is not None:
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if callable(eval_metric):
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eval_metric = None
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else:
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params.update({'eval_metric': eval_metric})
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self._Booster = train(params, trainDmatrix,
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self.n_estimators, evals=evals,
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early_stopping_rounds=early_stopping_rounds,
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evals_result=evals_result, feval=feval,
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verbose_eval=verbose)
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if evals_result:
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for val in evals_result.items():
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evals_result_key = list(val[1].keys())[0]
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evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
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self.evals_result_ = evals_result
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if early_stopping_rounds is not None:
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self.best_score = self._Booster.best_score
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self.best_iteration = self._Booster.best_iteration
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return self
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def predict(self, data, output_margin=False, ntree_limit=0):
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# pylint: disable=missing-docstring,invalid-name
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test_dmatrix = DMatrix(data, missing=self.missing)
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return self.booster().predict(test_dmatrix,
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output_margin=output_margin,
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ntree_limit=ntree_limit)
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def evals_result(self):
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"""Return the evaluation results.
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If eval_set is passed to the `fit` function, you can call evals_result() to
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get evaluation results for all passed eval_sets. When eval_metric is also
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passed to the `fit` function, the evals_result will contain the eval_metrics
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passed to the `fit` function
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Returns
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-------
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evals_result : dictionary
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Example
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-------
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param_dist = {'objective':'binary:logistic', 'n_estimators':2}
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clf = xgb.XGBModel(**param_dist)
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clf.fit(X_train, y_train,
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eval_set=[(X_train, y_train), (X_test, y_test)],
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eval_metric='logloss',
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verbose=True)
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evals_result = clf.evals_result()
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The variable evals_result will contain:
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{'validation_0': {'logloss': ['0.604835', '0.531479']},
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'validation_1': {'logloss': ['0.41965', '0.17686']}}
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"""
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if self.evals_result_:
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evals_result = self.evals_result_
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else:
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raise XGBoostError('No results.')
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return evals_result
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class XGBClassifier(XGBModel, XGBClassifierBase):
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# pylint: disable=missing-docstring,too-many-arguments,invalid-name
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__doc__ = """Implementation of the scikit-learn API for XGBoost classification.
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""" + '\n'.join(XGBModel.__doc__.split('\n')[2:])
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def __init__(self, max_depth=3, learning_rate=0.1,
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n_estimators=100, silent=True,
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objective="binary:logistic",
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nthread=-1, gamma=0, min_child_weight=1,
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max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1,
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reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
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base_score=0.5, seed=0, missing=None):
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super(XGBClassifier, self).__init__(max_depth, learning_rate,
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n_estimators, silent, objective,
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nthread, gamma, min_child_weight,
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max_delta_step, subsample,
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colsample_bytree, colsample_bylevel,
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reg_alpha, reg_lambda,
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scale_pos_weight, base_score, seed, missing)
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def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
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early_stopping_rounds=None, verbose=True):
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# pylint: disable = attribute-defined-outside-init,arguments-differ, redefined-variable-type
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"""
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Fit gradient boosting classifier
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Parameters
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----------
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X : array_like
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Feature matrix
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y : array_like
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Labels
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sample_weight : array_like
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Weight for each instance
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eval_set : list, optional
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A list of (X, y) pairs to use as a validation set for
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early-stopping
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eval_metric : str, callable, optional
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If a str, should be a built-in evaluation metric to use. See
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doc/parameter.md. If callable, a custom evaluation metric. The call
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signature is func(y_predicted, y_true) where y_true will be a
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DMatrix object such that you may need to call the get_label
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method. It must return a str, value pair where the str is a name
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for the evaluation and value is the value of the evaluation
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function. This objective is always minimized.
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early_stopping_rounds : int, optional
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Activates early stopping. Validation error needs to decrease at
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least every <early_stopping_rounds> round(s) to continue training.
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Requires at least one item in evals. If there's more than one,
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will use the last. Returns the model from the last iteration
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(not the best one). If early stopping occurs, the model will
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have two additional fields: bst.best_score and bst.best_iteration.
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verbose : bool
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If `verbose` and an evaluation set is used, writes the evaluation
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metric measured on the validation set to stderr.
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"""
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evals_result = {}
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self.classes_ = list(np.unique(y))
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self.n_classes_ = len(self.classes_)
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if self.n_classes_ > 2:
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# Switch to using a multiclass objective in the underlying XGB instance
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self.objective = "multi:softprob"
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xgb_options = self.get_xgb_params()
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xgb_options['num_class'] = self.n_classes_
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else:
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xgb_options = self.get_xgb_params()
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feval = eval_metric if callable(eval_metric) else None
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if eval_metric is not None:
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if callable(eval_metric):
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eval_metric = None
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else:
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xgb_options.update({"eval_metric": eval_metric})
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if eval_set is not None:
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# TODO: use sample_weight if given?
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evals = list(DMatrix(x[0], label=x[1], missing=self.missing) for x in eval_set)
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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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self._features_count = X.shape[1]
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self._le = LabelEncoder().fit(y)
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training_labels = self._le.transform(y)
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if sample_weight is not None:
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train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
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missing=self.missing)
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else:
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train_dmatrix = DMatrix(X, label=training_labels,
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missing=self.missing)
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self._Booster = train(xgb_options, train_dmatrix, self.n_estimators,
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evals=evals,
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early_stopping_rounds=early_stopping_rounds,
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evals_result=evals_result, feval=feval,
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verbose_eval=verbose)
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if evals_result:
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for val in evals_result.items():
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evals_result_key = list(val[1].keys())[0]
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evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
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self.evals_result_ = evals_result
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if early_stopping_rounds is not None:
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self.best_score = self._Booster.best_score
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self.best_iteration = self._Booster.best_iteration
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return self
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def predict(self, data, output_margin=False, ntree_limit=0):
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test_dmatrix = DMatrix(data, missing=self.missing)
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class_probs = self.booster().predict(test_dmatrix,
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output_margin=output_margin,
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ntree_limit=ntree_limit)
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if len(class_probs.shape) > 1:
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column_indexes = np.argmax(class_probs, axis=1)
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else:
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column_indexes = np.repeat(0, class_probs.shape[0])
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column_indexes[class_probs > 0.5] = 1
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return self._le.inverse_transform(column_indexes)
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def predict_proba(self, data, output_margin=False, ntree_limit=0):
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test_dmatrix = DMatrix(data, missing=self.missing)
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class_probs = self.booster().predict(test_dmatrix,
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output_margin=output_margin,
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ntree_limit=ntree_limit)
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if self.objective == "multi:softprob":
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return class_probs
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else:
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classone_probs = class_probs
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classzero_probs = 1.0 - classone_probs
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return np.vstack((classzero_probs, classone_probs)).transpose()
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def evals_result(self):
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"""Return the evaluation results.
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If eval_set is passed to the `fit` function, you can call evals_result() to
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get evaluation results for all passed eval_sets. When eval_metric is also
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passed to the `fit` function, the evals_result will contain the eval_metrics
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passed to the `fit` function
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Returns
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-------
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evals_result : dictionary
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Example
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-------
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param_dist = {'objective':'binary:logistic', 'n_estimators':2}
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clf = xgb.XGBClassifier(**param_dist)
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clf.fit(X_train, y_train,
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eval_set=[(X_train, y_train), (X_test, y_test)],
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eval_metric='logloss',
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verbose=True)
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evals_result = clf.evals_result()
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The variable evals_result will contain:
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{'validation_0': {'logloss': ['0.604835', '0.531479']},
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'validation_1': {'logloss': ['0.41965', '0.17686']}}
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"""
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if self.evals_result_:
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evals_result = self.evals_result_
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else:
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raise XGBoostError('No results.')
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return evals_result
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@property
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def feature_importances_(self):
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"""
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Returns
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-------
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feature_importances_ : array of shape = [n_features]
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"""
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fs = self.booster().get_fscore()
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keys = [int(k.replace('f', '')) for k in fs.keys()]
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fs_dict = dict(zip(keys, fs.values()))
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all_features_dict = dict.fromkeys(range(0, self._features_count), 0)
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all_features_dict.update(fs_dict)
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return np.array(all_features_dict.values())
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class XGBRegressor(XGBModel, XGBRegressorBase):
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# pylint: disable=missing-docstring
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__doc__ = """Implementation of the scikit-learn API for XGBoost regression.
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""" + '\n'.join(XGBModel.__doc__.split('\n')[2:])
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