如何将 f1_score 参数传递给 scikit 中的 make_scorer 学习与 cross_val_score 一起使用？
How to pass f1_score arguments to the make_scorer in scikit learn to use with cross_val_score?
我有一个多分类问题（有很多标签），我想使用 'average' = 'weighted' 的 F1 分数。
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", line 256, in <listcomp>
for func, args, 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, X_test, 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 73, in inner_f
return f(**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_classification.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 73, in inner_f
return f(**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_classification.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, X, y, cv=8, n_jobs=-1, 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, X, y, 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, **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_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, 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, y, 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, one of [None, 'micro', 'macro', 'weighted'].
当你查看 documentation 中给出的示例时，你会发现你应该传递评分函数的参数（此处：f1_score）而不是作为字典，而是作为关键字参数：
f1 = make_scorer(f1_score, average='weighted')
np.mean(cross_val_score(model, X, y, cv=8, n_jobs=-1, scorin =f1))
我有一个多分类问题（有很多标签），我想使用 'average' = 'weighted' 的 F1 分数。
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", line 256, in <listcomp>
for func, args, 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, X_test, 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 73, in inner_f
return f(**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_classification.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 73, in inner_f
return f(**kwargs)
File "C:\Users\Alienware\Anaconda3\envs\tf2\lib\site-packages\sklearn\metrics\_classification.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, X, y, cv=8, n_jobs=-1, 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, X, y, 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, **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_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, 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, y, 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, one of [None, 'micro', 'macro', 'weighted'].
当你查看 documentation 中给出的示例时，你会发现你应该传递评分函数的参数（此处：f1_score）而不是作为字典，而是作为关键字参数：
f1 = make_scorer(f1_score, average='weighted')
np.mean(cross_val_score(model, X, y, cv=8, n_jobs=-1, scorin =f1))