我正在使用GridSerach来搜索分类器的最佳超参数，如下所述：http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html
以下是一段代码的样子：
X = X.values # convert from pandas Dataframe to numpy array
y = np.array(y)
n_samples, n_features = X.shape
n_outputs = y.shape[0]
inner_cv = cross_validation.StratifiedKFold(y, n_folds=4, shuffle=True, random_state=rnd)
outer_cv = cross_validation.StratifiedKFold(y, n_folds=kFold, shuffle=True, random_state=rnd)
# Non_nested parameter search and scoring
clf = GridSearchCV(estimator=pipeline, param_grid=param_dict, scoring= scores, cv=inner_cv)
# Nested CV with parameter optimization
nested_score = cross_validation.cross_val_score(clf, X=X, y=y, cv=outer_cv)
nested_score.fit(X,y)
nested_scores = nested_score.mean()
但是出于某种原因我收到了这个错误：
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-1-cad4e848fb54> in <module>()
124
125 # Nested CV with parameter optimization
--> 126 nested_score = cross_validation.cross_val_score(clf, X=X, y=y, cv=outer_cv)
127 nested_score.fit(X,y)
128 nested_scores = nested_score.mean()
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\cross_validation.py in cross_val_score(estimator, X, y, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
1431 train, test, verbose, None,
1432 fit_params)
-> 1433 for train, test in cv)
1434 return np.array(scores)[:, 0]
1435
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
798 # was dispatched. In particular this covers the edge
799 # case of Parallel used with an exhausted iterator.
--> 800 while self.dispatch_one_batch(iterator):
801 self._iterating = True
802 else:
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
656 return False
657 else:
--> 658 self._dispatch(tasks)
659 return True
660
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
564
565 if self._pool is None:
--> 566 job = ImmediateComputeBatch(batch)
567 self._jobs.append(job)
568 self.n_dispatched_batches += 1
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __init__(self, batch)
178 # Don't delay the application, to avoid keeping the input
179 # arguments in memory
--> 180 self.results = batch()
181
182 def get(self):
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
70
71 def __call__(self):
---> 72 return [func(*args, **kwargs) for func, args, kwargs in self.items]
73
74 def __len__(self):
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
70
71 def __call__(self):
---> 72 return [func(*args, **kwargs) for func, args, kwargs in self.items]
73
74 def __len__(self):
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\cross_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)
1529 estimator.fit(X_train, **fit_params)
1530 else:
-> 1531 estimator.fit(X_train, y_train, **fit_params)
1532
1533 except Exception as e:
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\grid_search.py in fit(self, X, y)
802
803 """
--> 804 return self._fit(X, y, ParameterGrid(self.param_grid))
805
806
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\grid_search.py in _fit(self, X, y, parameter_iterable)
551 self.fit_params, return_parameters=True,
552 error_score=self.error_score)
--> 553 for parameters in parameter_iterable
554 for train, test in cv)
555
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
798 # was dispatched. In particular this covers the edge
799 # case of Parallel used with an exhausted iterator.
--> 800 while self.dispatch_one_batch(iterator):
801 self._iterating = True
802 else:
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
656 return False
657 else:
--> 658 self._dispatch(tasks)
659 return True
660
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
564
565 if self._pool is None:
--> 566 job = ImmediateComputeBatch(batch)
567 self._jobs.append(job)
568 self.n_dispatched_batches += 1
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __init__(self, batch)
178 # Don't delay the application, to avoid keeping the input
179 # arguments in memory
--> 180 self.results = batch()
181
182 def get(self):
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
70
71 def __call__(self):
---> 72 return [func(*args, **kwargs) for func, args, kwargs in self.items]
73
74 def __len__(self):
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
70
71 def __call__(self):
---> 72 return [func(*args, **kwargs) for func, args, kwargs in self.items]
73
74 def __len__(self):
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\cross_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, error_score)
1522 start_time = time.time()
1523
-> 1524 X_train, y_train = _safe_split(estimator, X, y, train)
1525 X_test, y_test = _safe_split(estimator, X, y, test, train)
1526
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\cross_validation.py in _safe_split(estimator, X, y, indices, train_indices)
1589 X_subset = X[np.ix_(indices, train_indices)]
1590 else:
-> 1591 X_subset = safe_indexing(X, indices)
1592
1593 if y is not None:
C:\Users\Yas\Anaconda3\lib\site-packages\sklearn\utils\__init__.py in safe_indexing(X, indices)
161 indices.dtype.kind == 'i'):
162 # This is often substantially faster than X[indices]
--> 163 return X.take(indices, axis=0)
164 else:
165 return X[indices]
IndexError: index 4549 is out of bounds for size 4549
X和y具有以下尺寸：
X: (6066, 5)
y: (6066,)
感谢您分享您的意见。
答案 0 :(得分：1)
不确定您在这里尝试做什么，但GridsearchCV不是分类器，因此您无法将其传递给cross_val_score。
GridsearchCV使用不同的参数多次运行交叉验证。因此它代表多个分类器。一旦安装好，它确实有一个best_classifier属性。