我想做什么：
我希望使用cross_val_score计算多类问题的roc_auc
我尝试做的事情：
这是一个使用虹膜数据集的可复制示例。
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)
最后我执行跨值
cross_val_score(model, X, y, cv=3, scoring="roc_auc")
出了什么问题：
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：
是错误，还是我在误用？
答案 0 :(得分：2)
使用scikit-learn的交叉验证功能时，不必要的烦恼是，默认情况下，不会对数据进行混洗；最好将改组选项设为默认选择-当然，这将首先假设改组参数首先可用于package jdbcdemo;
import java.sql.*;
public class Driver {
public static void main(String[] args) {
try {
Connection myConn = DriverManager.getConnection("jdbc:mysql//localhost:3306/Test","root","password");
Statement myStmt = myConn.createStatement();
ResultSet myRs = myStmt.executeQuery("select * from TestTable");
while (myRs.next()) {
System.out.println(myRs.getString("Name"));
}
}
catch (Exception exc) {
exc.printStackTrace();
}
}
}
，但不幸的是，它不（{{3} }。
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：
cross_val_score现在，如上图所示，对150个样本进行了3次CV程序分层，并显示一条错误消息：
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])
有望开始有意义：在您的3个验证折中的每一个中仅存在一个标签，因此无法进行ROC计算（更不用说在每个验证折中模型看到相应训练折中看不到的标签的事实）
因此，只需在整理数据之前：
ValueError: Only one class present in y_true
你应该没事。