问题描述
我有一个多分类问题（有很多标签），我想使用F1分数和'average'='weighted'。
虽然我做错了。这是我的代码：
from sklearn.metrics import f1_score
from sklearn.metrics import make_scorer
f1 = make_scorer(f1_score,{'average' : 'weighted'})
np.mean(cross_val_score(model,X,y,cv=8,n_jobs=-1,scoring = f1))
---------------------------------------------------------------------------
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\joblib\externals\loky\process_executor.py",line 418,in _process_worker
r = call_item()
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\joblib\externals\loky\process_executor.py",line 272,in __call__
return self.fn(*self.args,**self.kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\joblib\_parallel_backends.py",line 608,in __call__
return self.func(*args,**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\joblib\parallel.py",line 256,in __call__
for func,args,kwargs in self.items]
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\joblib\parallel.py",in <listcomp>
for func,kwargs in self.items]
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\model_selection\_validation.py",line 560,in _fit_and_score
test_scores = _score(estimator,X_test,y_test,scorer)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\model_selection\_validation.py",line 607,in _score
scores = scorer(estimator,y_test)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_scorer.py",line 88,in __call__
*args,**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_scorer.py",line 213,in _score
**self._kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\utils\validation.py",line 73,in inner_f
return f(**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_classification.py",line 1047,in f1_score
zero_division=zero_division)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\utils\validation.py",line 1175,in fbeta_score
zero_division=zero_division)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\utils\validation.py",line 1434,in precision_recall_fscore_support
pos_label)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_classification.py",line 1265,in _check_set_wise_labels
% (y_type,average_options))
ValueError: Target is multiclass but average='binary'. Please choose another average setting,one of [None,'micro','macro','weighted'].
"""
The above exception was the direct cause of the following exception:
ValueError Traceback (most recent call last)
<ipython-input-48-0323d7b23fbc> in <module>
----> 1 np.mean(cross_val_score(model,scoring = f1))
~\Anaconda3\envs\tf2\lib\site-packages\sklearn\utils\validation.py in inner_f(*args,**kwargs)
71 FutureWarning)
72 kwargs.update({k: arg for k,arg in zip(sig.parameters,args)})
---> 73 return f(**kwargs)
74 return inner_f
75
~\Anaconda3\envs\tf2\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator,groups,scoring,cv,n_jobs,verbose,fit_params,pre_dispatch,error_score)
404 fit_params=fit_params,405 pre_dispatch=pre_dispatch,--> 406 error_score=error_score)
407 return cv_results['test_score']
408
~\Anaconda3\envs\tf2\lib\site-packages\sklearn\utils\validation.py in inner_f(*args,args)})
---> 73 return f(**kwargs)
74 return inner_f
75
~\Anaconda3\envs\tf2\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator,return_train_score,return_estimator,error_score)
246 return_times=True,return_estimator=return_estimator,247 error_score=error_score)
--> 248 for train,test in cv.split(X,groups))
249
250 zipped_scores = list(zip(*scores))
~\Anaconda3\envs\tf2\lib\site-packages\joblib\parallel.py in __call__(self,iterable)
1015
1016 with self._backend.retrieval_context():
-> 1017 self.retrieve()
1018 # Make sure that we get a last message telling us we are done
1019 elapsed_time = time.time() - self._start_time
~\Anaconda3\envs\tf2\lib\site-packages\joblib\parallel.py in retrieve(self)
907 try:
908 if getattr(self._backend,'supports_timeout',False):
--> 909 self._output.extend(job.get(timeout=self.timeout))
910 else:
911 self._output.extend(job.get())
~\Anaconda3\envs\tf2\lib\site-packages\joblib\_parallel_backends.py in wrap_future_result(future,timeout)
560 AsyncResults.get from multiprocessing."""
561 try:
--> 562 return future.result(timeout=timeout)
563 except LokyTimeoutError:
564 raise TimeoutError()
~\Anaconda3\envs\tf2\lib\concurrent\futures\_base.py in result(self,timeout)
433 raise CancelledError()
434 elif self._state == FINISHED:
--> 435 return self.__get_result()
436 else:
437 raise TimeoutError()
~\Anaconda3\envs\tf2\lib\concurrent\futures\_base.py in __get_result(self)
382 def __get_result(self):
383 if self._exception:
--> 384 raise self._exception
385 else:
386 return self._result
ValueError: Target is multiclass but average='binary'. Please choose another average setting,'weighted'].
解决方法
在查看documentation中给出的示例时，您会发现应该将score函数的参数（此处为f1_score）传递为dict，而不是传递为关键字参数：
f1 = make_scorer(f1_score,average='weighted')
np.mean(cross_val_score(model,X,y,cv=8,n_jobs=-1,scorin =f1))