如何在scikit中将f1_score参数传递给make_scorer学习如何与cross_val_score一起使用？

如何解决如何在scikit中将f1_score参数传递给make_scorer学习如何与cross_val_score一起使用？

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))