My input was a csv file which was imported to postgresqldb .Later i am building a cnn using keras.My code below gives the following error "IndexError: too many indices for array". I am quite new to machine learning so I do not have any idea about how to solve this. Any suggestions?
X = dataframe1[['Feature1','Feature2','Feature3','Feature4','Feature5','Feature6','Feature7','Feature8','Feature9','Feature10','Feature11\1','Feature12','Feature13','Feature14']]
Y=result[['label']]
# evaluate model with standardized dataset
results = cross_val_score(estimator, X, Y, cv=kfold)
print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
Error
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-50-0e5d0345015f> in <module>()
2 estimator = KerasClassifier(build_fn=create_baseline, nb_epoch=100, batch_size=5, verbose=0)
3 kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
----> 4 results = cross_val_score(estimator, X, Y, cv=kfold)
5 print("Results: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))
C:\Anacondav3\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
129
130 cv = check_cv(cv, y, classifier=is_classifier(estimator))
--> 131 cv_iter = list(cv.split(X, y, groups))
132 scorer = check_scoring(estimator, scoring=scoring)
133 # We clone the estimator to make sure that all the folds are
C:\Anacondav3\lib\site-packages\sklearn\model_selection\_split.py in split(self, X, y, groups)
320 n_samples))
321
--> 322 for train, test in super(_BaseKFold, self).split(X, y, groups):
323 yield train, test
324
C:\Anacondav3\lib\site-packages\sklearn\model_selection\_split.py in split(self, X, y, groups)
89 X, y, groups = indexable(X, y, groups)
90 indices = np.arange(_num_samples(X))
---> 91 for test_index in self._iter_test_masks(X, y, groups):
92 train_index = indices[np.logical_not(test_index)]
93 test_index = indices[test_index]
C:\Anacondav3\lib\site-packages\sklearn\model_selection\_split.py in _iter_test_masks(self, X, y, groups)
608
609 def _iter_test_masks(self, X, y=None, groups=None):
--> 610 test_folds = self._make_test_folds(X, y)
611 for i in range(self.n_splits):
612 yield test_folds == i
C:\Anacondav3\lib\site-packages\sklearn\model_selection\_split.py in _make_test_folds(self, X, y, groups)
595 for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)):
596 for cls, (_, test_split) in zip(unique_y, per_cls_splits):
--> 597 cls_test_folds = test_folds[y == cls]
598 # the test split can be too big because we used
599 # KFold(...).split(X[:max(c, n_splits)]) when data is not 100%
IndexError: too many indices for array
Is there a different way that I should be declaring the array or dataframe?
Notice that the example in the User Guide shows that X is 2-dimensional while y is 1-dimensional:
>>> X_train.shape, y_train.shape
((90, 4), (90,))
Some programmers use capitalized variables for 2-dimensional arrays and lower-case for 1-dimensional arrays.
Therefore use
Y = result['label']
instead of
Y = result[['label']]
I am assuming that result is a pandas DataFrame. When you index a Dataframe with a list of columns such as ['label'], a sub-DataFrame -- which is 2-dimensional -- is returned. If you index the DataFrame with a single string, a 1-dimensional Series is returned.
Finally, note that the IndexError
IndexError: too many indices for array
is raised on this line
cls_test_folds = test_folds[y == cls]
because y is 2-dimensional so y == cls is a 2-dimensional boolean array and test_folds is 1-dimensional. The situation is similar to the following:
In [72]: test_folds = np.zeros(5, dtype=np.int)
In [73]: y_eq_cls = np.array([(True, ), (False,)])
In [74]: test_folds[y_eq_cls]
IndexError: too many indices for array
来源：https://stackoverflow.com/questions/46374587/python-error-too-many-indices-for-array