Cross_val_score不适用于roc_auc和多类

Question

我想做的事：

我想计算一个cross_val_score使用roc_auc多类问题

我试图做的是：

这是使用虹膜数据集制作的可复制示例。

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)

我一个热编码我的目标

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)

最后我执行交叉val

cross_val_score(model, X, y, cv=3, scoring="roc_auc")

失败的原因：

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：

python == 3.7.2

sklearn == 0.19.2

我的问题：

是一个错误，还是我在误用？

Answer 1

scikit-learn的交叉验证功能不必要的麻烦在于，默认情况下，不会对数据进行 混洗 ；最好将改组设置为默认选项-
当然，这将首先假设改组参数首先可用cross_val_score，但不幸的是，它不是（docs）。

所以，这就是正在发生的事情；对虹膜数据集的150个样本进行了 分层 ：

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，一个三重CV过程具有150个样本，如上所示分层，并显示一条错误消息：

ValueError: Only one class present in y_true

应该希望开始有意义：您的3个验证折叠中的每一个仅存在一个标签，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的事实）。

因此，只需先洗牌就可以：

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")

你应该没事的