如何解决Xgboost 未与校准分类器一起运行
我正在尝试使用校准分类器运行 XGboost,以下是我遇到错误的代码片段:
from sklearn.calibration import CalibratedClassifierCV
from xgboost import XGBClassifier
import numpy as np
x_train =np.array([1,2,3,4,5,6,10,]).reshape(-1,1)
y_train = np.array([1,1,3])
x_cfl=XGBClassifier(n_estimators=1)
x_cfl.fit(x_train,y_train)
sig_clf = CalibratedClassifierCV(x_cfl,method="sigmoid")
sig_clf.fit(x_train,y_train)
错误:
TypeError: predict_proba() got an unexpected keyword argument 'X'"
完整跟踪:
TypeError Traceback (most recent call last)
<ipython-input-48-08dd0b4ae8aa> in <module>
----> 1 sig_clf.fit(x_train,y_train)
~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in fit(self,X,y,sample_weight)
309 parallel = Parallel(n_jobs=self.n_jobs)
310
--> 311 self.calibrated_classifiers_ = parallel(
312 delayed(_fit_classifier_calibrator_pair)(
313 clone(base_estimator),train=train,test=test,~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in __call__(self,iterable)
1039 # remaining jobs.
1040 self._iterating = False
-> 1041 if self.dispatch_one_batch(iterator):
1042 self._iterating = self._original_iterator is not None
1043
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in dispatch_one_batch(self,iterator)
857 return False
858 else:
--> 859 self._dispatch(tasks)
860 return True
861
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in _dispatch(self,batch)
775 with self._lock:
776 job_idx = len(self._jobs)
--> 777 job = self._backend.apply_async(batch,callback=cb)
778 # A job can complete so quickly than its callback is
779 # called before we get here,causing self._jobs to
~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py in apply_async(self,func,callback)
206 def apply_async(self,callback=None):
207 """Schedule a func to be run"""
--> 208 result = ImmediateResult(func)
209 if callback:
210 callback(result)
~/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py in __init__(self,batch)
570 # Don't delay the application,to avoid keeping the input
571 # arguments in memory
--> 572 self.results = batch()
573
574 def get(self):
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in __call__(self)
260 # change the default number of processes to -1
261 with parallel_backend(self._backend,n_jobs=self._n_jobs):
--> 262 return [func(*args,**kwargs)
263 for func,args,kwargs in self.items]
264
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in <listcomp>(.0)
260 # change the default number of processes to -1
261 with parallel_backend(self._backend,kwargs in self.items]
264
~/anaconda3/lib/python3.8/site-packages/sklearn/utils/fixes.py in __call__(self,*args,**kwargs)
220 def __call__(self,**kwargs):
221 with config_context(**self.config):
--> 222 return self.function(*args,**kwargs)
~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in _fit_classifier_calibrator_pair(estimator,train,test,supports_sw,method,classes,sample_weight)
443 n_classes = len(classes)
444 pred_method = _get_prediction_method(estimator)
--> 445 predictions = _compute_predictions(pred_method,X[test],n_classes)
446
447 sw = None if sample_weight is None else sample_weight[test]
~/anaconda3/lib/python3.8/site-packages/sklearn/calibration.py in _compute_predictions(pred_method,n_classes)
499 (X.shape[0],1).
500 """
--> 501 predictions = pred_method(X=X)
502 if hasattr(pred_method,'__name__'):
503 method_name = pred_method.__name__
TypeError: predict_proba() got an unexpected keyword argument 'X'
我对此感到非常惊讶,因为它一直在为我运行直到昨天,当我使用其他分类器时也在运行相同的代码。
from sklearn.calibration import CalibratedClassifierCV
from xgboost import XGBClassifier
import numpy as np
x_train = np.array([1,3])
x_cfl=LGBMClassifier(n_estimators=1)
x_cfl.fit(x_train,y_train)
输出:
CalibratedClassifierCV(base_estimator=LGBMClassifier(n_estimators=1))
我的 Xgboost 安装有问题吗??我使用 conda 进行安装,最后我记得我昨天卸载了 xgboost 并重新安装了它。
我的 xgboost 版本:
1.3.0
解决方法
我相信问题来自 XGBoost。 解释如下:https://github.com/dmlc/xgboost/pull/6555
XGBoost 定义:
predict_proba(self,data,...
代替:
predict_proba(self,X,...
由于 sklearn 0.24 调用 clf.predict_proba(X=X)
,因此会引发异常。
这里有一个在不更改包版本的情况下解决问题的想法:创建一个继承 XGBoostClassifier
的类以使用正确的参数名称覆盖 predict_proba
并调用 super()
。
现在已经修复了,scikit-learn=0.24 好像有一个bug
我降级到 0.22.2.post1 并且修复了!
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