scikit.learn cross_val_score中的错误

请参考以下地址的笔记本

LogisticRegression

这部分代码

scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)
print scores
print scores.mean()

在Windows 7 64位计算机上生成以下错误

---------------------------------------------------------------------------
 IndexError                                Traceback (most recent call last)
 <ipython-input-37-4a10affe67c7> in <module>()
 1 # evaluate the model using 10-fold cross-validation
 ----> 2 scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)
  3 print scores
  4 print scores.mean()

 C:\Python27\lib\site-packages\sklearn\cross_validation.pyc in    cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, score_func, pre_dispatch)
  1140                         allow_nans=True, allow_nd=True)
  1141 
  -> 1142     cv = _check_cv(cv, X, y, classifier=is_classifier(estimator))
  1143     scorer = check_scoring(estimator, score_func=score_func, scoring=scoring)
  1144     # We clone the estimator to make sure that all the folds are

  C:\Python27\lib\site-packages\sklearn\cross_validation.pyc in _check_cv(cv, X, y, classifier, warn_mask)
  1366         if classifier:
  1367             if type_of_target(y) in ['binary', 'multiclass']:
  -> 1368                 cv = StratifiedKFold(y, cv, indices=needs_indices)
  1369             else:
  1370                 cv = KFold(_num_samples(y), cv, indices=needs_indices)

  C:\Python27\lib\site-packages\sklearn\cross_validation.pyc in __init__(self, y, n_folds, indices, shuffle, random_state)
  428         for test_fold_idx, per_label_splits in enumerate(zip(*per_label_cvs)):
  429             for label, (_, test_split) in zip(unique_labels, per_label_splits):
--> 430                 label_test_folds = test_folds[y == label]
 431                 # the test split can be too big because we used
 432                 # KFold(max(c, self.n_folds), self.n_folds) instead of

IndexError: too many indices for array 

我正在使用scikit.learn 0.15.2,建议使用here,这可能是Windows 7、64位计算机的特定问题.

==============更新==============

我发现以下代码确实有效

 from sklearn.cross_validation import KFold
 cv = KFold(X.shape[0], 10, shuffle=True, random_state=33)
 scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=cv)
 print scores

==============更新2 =============

似乎由于某些软件包更新,我无法再在计算机上重现此类错误.如果您在Windows 7 64位计算机上遇到相同的问题,请告诉我.

解决方法:

发现这个问题时,我遇到了同样的错误,并且正在寻找答案.

我使用了相同的sklearn.cross_validation.cross_val_score(不同的算法除外)和相同的计算机Windows 7(64位).

我从上面尝试了您的解决方案,它“起作用”,但是它给了我以下警告：

C:\Users\E245713\AppData\Local\Continuum\Anaconda3\lib\site-packages\sklearn\cross_validation.py:1531: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
estimator.fit(X_train, y_train, **fit_params)

阅读警告后,我发现问题与“ y”形(我的标签列)有关.要从警告中尝试的关键字是“ ravel()”.因此,我尝试了以下方法：

y_arr = pd.DataFrame.as_matrix(label)
print(y_arr)
print(y_arr.shape())

这给了我

  [[1]
   [0]
   [1]
   .., 
   [0]
   [0]
   [1]]

  (87939, 1)

当我添加’ravel()’时：

y_arr = pd.DataFrame.as_matrix(label).ravel()
print(y_arr)
print(y_arr.shape())

它给了我：

[1 0 1 ..., 0 0 1]

(87939,)

y_arr的维数必须为(87939,)而不是(87939,1)的形式.
之后,我的原始cross_val_score无需添加Kfold代码即可工作.

希望这可以帮助.

标签：cross-validation,scikit-learn,python
来源： https://codeday.me/bug/20191121/2049444.html