我想做的事：
我希望在多类问题上使用cross_val_score计算roc_auc
我试图做的：
这是一个用虹膜数据集制作的可重现的例子。
from sklearn.datasets import load_iris
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import cross_val_score
iris = load_iris()
X = pd.DataFrame(data=iris.data, columns=iris.feature_names)
我热门编码我的目标
encoder = OneHotEncoder()
y = encoder.fit_transform(pd.DataFrame(iris.target)).toarray()
我使用决策树分类器
model = DecisionTreeClassifier(max_depth=1)
最后我执行cross val
cross_val_score(model, X, y, cv=3, scoring="roc_auc")
失败的是什么：
最后一行抛出以下错误
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-87-91dc6fa67512> in <module>()
----> 1 cross_val_score(model, X, y, cv=3, scoring="roc_auc")
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
340 n_jobs=n_jobs, verbose=verbose,
341 fit_params=fit_params,
--> 342 pre_dispatch=pre_dispatch)
343 return cv_results['test_score']
344
~/programs/anaconda3/lib/python3.7/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)
204 fit_params, return_train_score=return_train_score,
205 return_times=True)
--> 206 for train, test in cv.split(X, y, groups))
207
208 if return_train_score:
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
777 # was dispatched. In particular this covers the edge
778 # case of Parallel used with an exhausted iterator.
--> 779 while self.dispatch_one_batch(iterator):
780 self._iterating = True
781 else:
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
623 return False
624 else:
--> 625 self._dispatch(tasks)
626 return True
627
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
586 dispatch_timestamp = time.time()
587 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 588 job = self._backend.apply_async(batch, callback=cb)
589 self._jobs.append(job)
590
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
109 def apply_async(self, func, callback=None):
110 """Schedule a func to be run"""
--> 111 result = ImmediateResult(func)
112 if callback:
113 callback(result)
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
330 # Don't delay the application, to avoid keeping the input
331 # arguments in memory
--> 332 self.results = batch()
333
334 def get(self):
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~/programs/anaconda3/lib/python3.7/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, error_score)
486 fit_time = time.time() - start_time
487 # _score will return dict if is_multimetric is True
--> 488 test_scores = _score(estimator, X_test, y_test, scorer, is_multimetric)
489 score_time = time.time() - start_time - fit_time
490 if return_train_score:
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py in _score(estimator, X_test, y_test, scorer, is_multimetric)
521 """
522 if is_multimetric:
--> 523 return _multimetric_score(estimator, X_test, y_test, scorer)
524 else:
525 if y_test is None:
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_validation.py in _multimetric_score(estimator, X_test, y_test, scorers)
551 score = scorer(estimator, X_test)
552 else:
--> 553 score = scorer(estimator, X_test, y_test)
554
555 if hasattr(score, 'item'):
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/metrics/scorer.py in __call__(self, clf, X, y, sample_weight)
204 **self._kwargs)
205 else:
--> 206 return self._sign * self._score_func(y, y_pred, **self._kwargs)
207
208 def _factory_args(self):
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/metrics/ranking.py in roc_auc_score(y_true, y_score, average, sample_weight)
275 return _average_binary_score(
276 _binary_roc_auc_score, y_true, y_score, average,
--> 277 sample_weight=sample_weight)
278
279
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/metrics/base.py in _average_binary_score(binary_metric, y_true, y_score, average, sample_weight)
116 y_score_c = y_score.take([c], axis=not_average_axis).ravel()
117 score[c] = binary_metric(y_true_c, y_score_c,
--> 118 sample_weight=score_weight)
119
120 # Average the results
~/programs/anaconda3/lib/python3.7/site-packages/sklearn/metrics/ranking.py in _binary_roc_auc_score(y_true, y_score, sample_weight)
266 def _binary_roc_auc_score(y_true, y_score, sample_weight=None):
267 if len(np.unique(y_true)) != 2:
--> 268 raise ValueError("Only one class present in y_true. ROC AUC score "
269 "is not defined in that case.")
270
ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.
我的环境：
蟒蛇== 3.7.2
清除== 0.19.2
我的问题：
这是一个错误，还是我错过了使用？
使用scikit-learn的交叉验证功能不必要的烦恼是，默认情况下，数据不会被洗牌;将拖沓作为默认选择可能是一个好主意 - 当然，这可能会先假设cross_val_score首先有一个改组参数，但不幸的是它不是（docs）。
那么，这是正在发生的事情;虹膜数据集的150个样本是分层的：
iris.target[0:50]
# result
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0])
iris.target[50:100]
# result:
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1])
iris.target[100:150]
# result:
array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2])
现在，如上所示，对包含150个样本的3个CV程序进行分层，并显示错误消息：
ValueError: Only one class present in y_true
应该有希望开始有意义：在你的3个验证折叠的每一个中只有一个标签存在，因此不可能进行ROC计算（更不用说在每个验证折叠中模型看到相应训练折叠中看不到的标签）。
因此，只需将您的数据随机播放：
from sklearn.utils import shuffle
X_s, y_s = shuffle(X, y)
cross_val_score(model, X_s, y_s, cv=3, scoring="roc_auc")
你应该没事