لماذا cross_val_score لا تعمل مع roc_auc ويظهر لي هذا الخطأ: ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.

لدي الكود التالي:

from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_iris
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
dataset = load_iris()
Xdata = pd.DataFrame(data=dataset.data, columns=dataset.feature_names)
encoder = OneHotEncoder()
ydata = encoder.fit_transform(pd.DataFrame(dataset.target)).toarray()
model = DecisionTreeClassifier(max_depth=1)
cross_val_score(model, Xdata, ydata, cv=4, scoring="roc_auc")
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-20-6be5be8289ab> in <module>
     11 model = DecisionTreeClassifier(max_depth=1)
     12 
---> 13 cross_val_score(model, Xdata, ydata, cv=4, scoring="roc_auc")

~\anaconda3\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)
    388                                 fit_params=fit_params,
    389                                 pre_dispatch=pre_dispatch,
--> 390                                 error_score=error_score)
    391     return cv_results['test_score']
    392 

~\anaconda3\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)
    234             return_times=True, return_estimator=return_estimator,
    235             error_score=error_score)
--> 236         for train, test in cv.split(X, y, groups))
    237 
    238     zipped_scores = list(zip(*scores))

~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
   1002             # remaining jobs.
   1003             self._iterating = False
-> 1004             if self.dispatch_one_batch(iterator):
   1005                 self._iterating = self._original_iterator is not None
   1006 

~\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
    833                 return False
    834             else:
--> 835                 self._dispatch(tasks)
    836                 return True
    837 

~\anaconda3\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
    752         with self._lock:
    753             job_idx = len(self._jobs)
--> 754             job = self._backend.apply_async(batch, callback=cb)
    755             # A job can complete so quickly than its callback is
    756             # called before we get here, causing self._jobs to

~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
    207     def apply_async(self, func, callback=None):
    208         """Schedule a func to be run"""
--> 209         result = ImmediateResult(func)
    210         if callback:
    211             callback(result)

~\anaconda3\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
    588         # Don't delay the application, to avoid keeping the input
    589         # arguments in memory
--> 590         self.results = batch()
    591 
    592     def get(self):

~\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~\anaconda3\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    254         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    255             return [func(*args, **kwargs)
--> 256                     for func, args, kwargs in self.items]
    257 
    258     def __len__(self):

~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
    542     else:
    543         fit_time = time.time() - start_time
--> 544         test_scores = _score(estimator, X_test, y_test, scorer)
    545         score_time = time.time() - start_time - fit_time
    546         if return_train_score:

~\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py in _score(estimator, X_test, y_test, scorer)
    589         scores = scorer(estimator, X_test)
    590     else:
--> 591         scores = scorer(estimator, X_test, y_test)
    592 
    593     error_msg = ("scoring must return a number, got %s (%s) "

~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in __call__(self, estimator, *args, **kwargs)
     85             if isinstance(scorer, _BaseScorer):
     86                 score = scorer._score(cached_call, estimator,
---> 87                                       *args, **kwargs)
     88             else:
     89                 score = scorer(estimator, *args, **kwargs)

~\anaconda3\lib\site-packages\sklearn\metrics\_scorer.py in _score(self, method_caller, clf, X, y, sample_weight)
    330                                                  **self._kwargs)
    331         else:
--> 332             return self._sign * self._score_func(y, y_pred, **self._kwargs)
    333 
    334     def _factory_args(self):

~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in roc_auc_score(y_true, y_score, average, sample_weight, max_fpr, multi_class, labels)
    393                                              max_fpr=max_fpr),
    394                                      y_true, y_score, average,
--> 395                                      sample_weight=sample_weight)
    396 
    397 

~\anaconda3\lib\site-packages\sklearn\metrics\_base.py in _average_binary_score(binary_metric, y_true, y_score, average, sample_weight)
    118         y_score_c = y_score.take([c], axis=not_average_axis).ravel()
    119         score[c] = binary_metric(y_true_c, y_score_c,
--> 120                                  sample_weight=score_weight)
    121 
    122     # Average the results

~\anaconda3\lib\site-packages\sklearn\metrics\_ranking.py in _binary_roc_auc_score(y_true, y_score, sample_weight, max_fpr)
    219     """Binary roc auc score"""
    220     if len(np.unique(y_true)) != 2:
--> 221         raise ValueError("Only one class present in y_true. ROC AUC score "
    222                          "is not defined in that case.")
    223 

ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.