该示例完全可重现。以下是完整的笔记本（也包含数据下载）：https://github.com/ageron/handson-ml2/blob/master/02_end_to_end_machine_learning_project.ipynb
在上述笔记本的这一部分之后：
full_pipeline_with_predictor = Pipeline([ ("preparation", full_pipeline), ("linear", LinearRegression()) ])full_pipeline_with_predictor.fit(housing, housing_labels)full_pipeline_with_predictor.predict(some_data)我尝试使用以下代码在测试集上获取预测结果：
X_test_prepared = full_pipeline.transform(X_test)final_predictions = full_pipeline_with_predictor.predict(X_test_prepared)但我收到了以下错误：
C:\Users\Alex\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py:430: FutureWarning: Given feature/column names or counts do not match the ones for the data given during fit. This will fail from v0.24. FutureWarning)---------------------------------------------------------------------------Empty Traceback (most recent call last)~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator) 796 try:--> 797 tasks = self._ready_batches.get(block=False) 798 except queue.Empty:~\AppData\Local\Continuum\anaconda3\lib\queue.py in get(self, block, timeout) 166 if not self._qsize():--> 167 raise Empty 168 elif timeout is None:Empty: During handling of the above exception, another exception occurred:ValueError Traceback (most recent call last)<ipython-input-141-dc87b1c9e658> in <module> 5 6 X_test_prepared = full_pipeline.transform(X_test)----> 7 final_predictions = full_pipeline_with_predictor.predict(X_test_prepared) 8 9 final_mse = mean_squared_error(y_test, final_predictions)~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\metaestimators.py in <lambda>(*args, **kwargs) 114 115 # lambda, but not partial, allows help() to work with update_wrapper--> 116 out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs) 117 # update the docstring of the returned function 118 update_wrapper(out, self.fn)~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\pipeline.py in predict(self, X, **predict_params) 417 Xt = X 418 for _, name, transform in self._iter(with_final=False):--> 419 Xt = transform.transform(Xt) 420 return self.steps[-1][-1].predict(Xt, **predict_params) 421 ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in transform(self, X) 586 587 self._validate_features(X.shape[1], X_feature_names)--> 588 Xs = self._fit_transform(X, None, _transform_one, fitted=True) 589 self._validate_output(Xs) 590 ~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in _fit_transform(self, X, y, func, fitted) 455 message=self._log_message(name, idx, len(transformers))) 456 for idx, (name, trans, column, weight) in enumerate(--> 457 self._iter(fitted=fitted, replace_strings=True), 1)) 458 except ValueError as e: 459 if "Expected 2D array, got 1D array instead" in str(e):~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in __call__(self, iterable) 1002 # remaining jobs. 1003 self._iterating = False-> 1004 if self.dispatch_one_batch(iterator): 1005 self._iterating = self._original_iterator is not None 1006 ~\AppData\Local\Continuum\anaconda3\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator) 806 big_batch_size = batch_size * n_jobs 807 --> 808 islice = list(itertools.islice(iterator, big_batch_size)) 809 if len(islice) == 0: 810 return False~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\compose\_column_transformer.py in <genexpr>(.0) 454 message_clsname='ColumnTransformer', 455 message=self._log_message(name, idx, len(transformers)))--> 456 for idx, (name, trans, column, weight) in enumerate( 457 self._iter(fitted=fitted, replace_strings=True), 1)) 458 except ValueError as e:~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\utils\__init__.py in _safe_indexing(X, indices, axis) 404 if axis == 1 and indices_dtype == 'str' and not hasattr(X, 'loc'): 405 raise ValueError(--> 406 "Specifying the columns using strings is only supported for " 407 "pandas DataFrames" 408 )ValueError: Specifying the columns using strings is only supported for pandas DataFrames问题： 我如何修正这个错误？为什么会发生这个错误？
回答：
由于您的最终管道：
full_pipeline_with_predictor = Pipeline([ ("preparation", full_pipeline), ("linear", LinearRegression()) ])显然已经包含了full_pipeline，您不应该再次“准备”您的X_test；这样做，您实际上是对X_test进行了双重“准备”，这是错误的。因此，您的代码应该简单地是
final_predictions = full_pipeline_with_predictor.predict(X_test)正如您为some_data获取预测时所做的那样，即
full_pipeline_with_predictor.predict(some_data)您在将some_data输入到最终管道之前正确地未对其进行“准备”。
使用管道的整个意义就在于此，即避免必须分别运行可能多个准备步骤的fit-predict，而是将所有步骤包装在一个单一的管道中。您在预测some_data时正确地应用了这一过程，但在下一步尝试预测X_test时似乎忘记了这一点。
