Numpy _参数） 249如果self.\u final\u估计器不是无： -->250自我最终估计值拟合（Xt，y，**拟合参数） 251返回自我 252 /用户/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc-in-fit（self，X，y，**fit_参数） 246这个估计器 247 """ -->248 Xt，拟合参数=自拟合（X，y，**拟合参数） 249如果self.\u final\u估计器不是无： 250自我最终估计值拟合（Xt，y，**拟合参数） /Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc in_fit（self，X，y，**fit_参数） 211 Xt，安装的变压器=安装的变压器( 212个变压器，无，Xt，y， -->213**fit_参数_步骤[名称]） 214#用安装的变压器更换阶梯变压器 215#变压器。这在加载变压器时是必要的 /Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/memory.pyc in_uuuuu调用（self，*args，**kwargs） 360 361定义调用（self，*args，**kwargs）： -->362返回self.func（*args，**kwargs） 363 364 def呼叫和搁置（self、*args、**kwargs）： /Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc in_fit_transform_one（变压器、重量、X、y、**fit_参数） 579**拟合参数）： 580如果hasattr（变压器，“配合变换”）： -->581 res=变换器。拟合变换（X，y，**拟合参数） 582其他： 583 res=变换器.fit（X，y，**拟合参数）.transform（X） in-fit_变换（self，X，y） 138 ''' 139 -->140自适配（X，y） 141返回自变换（X） 合适（自身、X、y） 73#大多数负特征需要检查df分位数（1-q） 74#为了使用正确的分位数值 --->75位=np百分位（X[：，自我特征列表]，自我q*100，轴=0） 76负=np百分位（X[：，自特性列表]，（1.0-自特性q）*100，轴=0） 77 索引器：索引10超出大小为10的轴1的界限

当您将管道发送到GridSearchCV时，

最佳估算器\uu

还包含管道对象（无论您是仅调整管道的单个部分还是所有部分）

所以当你这样做的时候：

knn_pipe_tuned = Pipeline([('impute', imp), ('engineer',topQ), ('scale', scaler),
                ('select', selector), ('kNN', knn_gs.best_estimator_)])

你基本上是这样做的：

knn_pipe_tuned = Pipeline([('impute', imp), ('engineer',topQ), ('scale', scaler),
                ('select', selector), ('kNN', Pipeline([('impute', imp),           
                                                        ('engineer',topQ), 
                                                        ('scale', scaler),
                                                        ('select', selector), 
                                                        ('kNN', knn)]))])

因此，这将再次

插补

，

工程师

，

缩放

，

选择已经通过所有这些的数据。我确信这不是您想要的

执行交叉验证时，您只需执行以下操作：

knn_pipe_tuned = knn_gs.best_estimator_

哦！我完全错过了。我一直认为
最佳估计量\uuu
只是估计量本身，而不是整个管道，但你所说的完全有道理。既然代码现在运行顺利，产生的结果看起来是适当的规模，我正在标记这一点。谢谢你的帮助！一半相关的to您的问题，但通过设置
knn_pipe\u tuned=knn_gs
，或只是
knn_scores=cross_validate（knn_gs，features，label，groups=None，score=['precision'，'recall'，'f1']，cv=cv_1000）
，您可以进行嵌套交叉验证，从而获得无偏的cv结果。
knn_pipe_tuned = Pipeline([('impute', imp), ('engineer',topQ), ('scale', scaler),
                    ('select', selector), ('kNN', knn_gs.best_estimator_)])


cv_1000 = StratifiedShuffleSplit(n_splits=1000, test_size=0.2, random_state=42)

from sklearn.model_selection import cross_validate
knn_scores = cross_validate(knn_pipe_tuned, features, labels, groups=None, 
                            scoring=['precision', 'recall', 'f1'], cv=cv_1000)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-147-4f04d5e63a0b> in <module>()
     12 from sklearn.model_selection import cross_validate
     13 knn_scores = cross_validate(knn_pipe_tuned, features, labels, groups=None, 
---> 14                             scoring=['precision', 'recall', 'f1'], cv=cv_1000)
     15 
     16 knn_cv_results = pd.DataFrame(knn_scores)

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc 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:

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc 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:

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc in dispatch_one_batch(self, iterator)
    623                 return False
    624             else:
--> 625                 self._dispatch(tasks)
    626                 return True
    627 

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc 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 

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc 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)

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/_parallel_backends.pyc 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):

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.pyc 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):

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/model_selection/_validation.pyc 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)
    456             estimator.fit(X_train, **fit_params)
    457         else:
--> 458             estimator.fit(X_train, y_train, **fit_params)
    459 
    460     except Exception as e:

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
    248         Xt, fit_params = self._fit(X, y, **fit_params)
    249         if self._final_estimator is not None:
--> 250             self._final_estimator.fit(Xt, y, **fit_params)
    251         return self
    252 

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc in fit(self, X, y, **fit_params)
    246             This estimator
    247         """
--> 248         Xt, fit_params = self._fit(X, y, **fit_params)
    249         if self._final_estimator is not None:
    250             self._final_estimator.fit(Xt, y, **fit_params)

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc in _fit(self, X, y, **fit_params)
    211                 Xt, fitted_transformer = fit_transform_one_cached(
    212                     cloned_transformer, None, Xt, y,
--> 213                     **fit_params_steps[name])
    214                 # Replace the transformer of the step with the fitted
    215                 # transformer. This is necessary when loading the transformer

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/externals/joblib/memory.pyc in __call__(self, *args, **kwargs)
    360 
    361     def __call__(self, *args, **kwargs):
--> 362         return self.func(*args, **kwargs)
    363 
    364     def call_and_shelve(self, *args, **kwargs):

/Users/emigre459/anaconda3/envs/ML_MiniProjects/lib/python2.7/site-packages/sklearn/pipeline.pyc in _fit_transform_one(transformer, weight, X, y, **fit_params)
    579                        **fit_params):
    580     if hasattr(transformer, 'fit_transform'):
--> 581         res = transformer.fit_transform(X, y, **fit_params)
    582     else:
    583         res = transformer.fit(X, y, **fit_params).transform(X)

<ipython-input-108-dfcab4b62582> in fit_transform(self, X, y)
    138         '''
    139 
--> 140         self.fit(X, y)
    141         return self.transform(X)

<ipython-input-108-dfcab4b62582> in fit(self, X, y)
     73             #Majority-negative features need to check df.quantile(1-q)
     74                 #in order to be using correct quantile value
---> 75             pos = np.percentile(X[:, self.feature_list], self.q * 100, axis = 0)
     76             neg = np.percentile(X[:, self.feature_list], (1.0 - self.q) * 100, axis = 0)
     77 

IndexError: index 10 is out of bounds for axis 1 with size 10

knn_pipe_tuned = Pipeline([('impute', imp), ('engineer',topQ), ('scale', scaler),
                ('select', selector), ('kNN', knn_gs.best_estimator_)])

knn_pipe_tuned = Pipeline([('impute', imp), ('engineer',topQ), ('scale', scaler),
                ('select', selector), ('kNN', Pipeline([('impute', imp),           
                                                        ('engineer',topQ), 
                                                        ('scale', scaler),
                                                        ('select', selector), 
                                                        ('kNN', knn)]))])

knn_pipe_tuned = knn_gs.best_estimator_