Error in scikit.learn cross_val_score

Question

please refer to the notebook at the following address

LogisticRegression

this portion of code,

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

generates the following error in a window 7 64bit machine

---------------------------------------------------------------------------
 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 

I am using scikit.learn 0.15.2, it is suggested here that may a specific problem for windows 7, 64 bit machine.

==============update==============

I found the following code actually works

 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

==============update 2=============

it seems due to some package update, I can no longer reproduce such error on my machine. If you are facing the same issue on a windows 7 64bit machine, please let me know.

Answer 1

I had the same error you got and was looking for answers when I found this question.

I used the same sklearn.cross_validation.cross_val_score (except different algorithm) and the same machine windows 7, 64 bit.

I tried your solution from above and it "worked", but it gave me the following warning:

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)

After reading the warning, I figured that the problem has something to do with the shape of 'y' (my label column). The keyword to try from the warning is "ravel()". So, I tried the following:

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

which gave me

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

  (87939, 1)

When I added 'ravel()':

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

it gave me:

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

(87939,)

The dimension of 'y_arr' has to be in the form of (87939,) not (87939,1). After that my original cross_val_score worked without adding the Kfold code.

Hope this helps.