【发布时间】：2019-11-27 10:01:20
【问题描述】：
在 python 中运行此代码时出现 KeyError:0：
full_pipeline.fit(X_train, y_train)
这是完整的代码：
from gensim.sklearn_api import D2VTransformer
from sklearn.pipeline import FeatureUnion, Pipeline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
name_pipeline = Pipeline( steps = [
( 'feature_selector', FeatureSelector(['name']) ),
( 'feature_transformer', D2VTransformer() ) ] )
description_pipeline = Pipeline( steps = [
( 'feature_selector', FeatureSelector(['description']) ),
( 'feature_transformer', D2VTransformer() ) ] )
X_pipeline = FeatureUnion( transformer_list = [
( 'name_pipeline', name_pipeline ),
( 'description_pipeline', description_pipeline ) ] )
#Split up the train and test data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1)
clf = LogisticRegression(random_state=0, class_weight='balanced', solver='lbfgs', max_iter=1000, multi_class='multinomial')
full_pipeline = Pipeline( steps =
[ ( 'pipeline', X_pipeline),
( 'model', clf ) ] )
full_pipeline.fit(X_train, y_train)
这是我得到的错误：
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2896 try:
-> 2897 return self._engine.get_loc(key)
2898 except KeyError:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 0
During handling of the above exception, another exception occurred:
KeyError Traceback (most recent call last)
19 frames
<ipython-input-14-0ddbaedffb67> in <module>()
25 ( 'model', clf ) ] )
26
---> 27 full_pipeline.fit(X_train, y_train)
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
350 This estimator
351 """
--> 352 Xt, fit_params = self._fit(X, y, **fit_params)
353 with _print_elapsed_time('Pipeline',
354 self._log_message(len(self.steps) - 1)):
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
315 message_clsname='Pipeline',
316 message=self._log_message(step_idx),
--> 317 **fit_params_steps[name])
318 # Replace the transformer of the step with the fitted
319 # transformer. This is necessary when loading the transformer
/usr/local/lib/python3.6/dist-packages/joblib/memory.py in __call__(self, *args, **kwargs)
353
354 def __call__(self, *args, **kwargs):
--> 355 return self.func(*args, **kwargs)
356
357 def call_and_shelve(self, *args, **kwargs):
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
714 with _print_elapsed_time(message_clsname, message):
715 if hasattr(transformer, 'fit_transform'):
--> 716 res = transformer.fit_transform(X, y, **fit_params)
717 else:
718 res = transformer.fit(X, y, **fit_params).transform(X)
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
910 sum of n_components (output dimension) over transformers.
911 """
--> 912 results = self._parallel_func(X, y, fit_params, _fit_transform_one)
913 if not results:
914 # All transformers are None
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in _parallel_func(self, X, y, fit_params, func)
940 message=self._log_message(name, idx, len(transformers)),
941 **fit_params) for idx, (name, transformer,
--> 942 weight) in enumerate(transformers, 1))
943
944 def transform(self, X):
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in __call__(self, iterable)
1001 # remaining jobs.
1002 self._iterating = False
-> 1003 if self.dispatch_one_batch(iterator):
1004 self._iterating = self._original_iterator is not None
1005
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
832 return False
833 else:
--> 834 self._dispatch(tasks)
835 return True
836
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in _dispatch(self, batch)
751 with self._lock:
752 job_idx = len(self._jobs)
--> 753 job = self._backend.apply_async(batch, callback=cb)
754 # A job can complete so quickly than its callback is
755 # called before we get here, causing self._jobs to
/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
199 def apply_async(self, func, callback=None):
200 """Schedule a func to be run"""
--> 201 result = ImmediateResult(func)
202 if callback:
203 callback(result)
/usr/local/lib/python3.6/dist-packages/joblib/_parallel_backends.py in __init__(self, batch)
580 # Don't delay the application, to avoid keeping the input
581 # arguments in memory
--> 582 self.results = batch()
583
584 def get(self):
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in __call__(self)
254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
255 return [func(*args, **kwargs)
--> 256 for func, args, kwargs in self.items]
257
258 def __len__(self):
/usr/local/lib/python3.6/dist-packages/joblib/parallel.py in <listcomp>(.0)
254 with parallel_backend(self._backend, n_jobs=self._n_jobs):
255 return [func(*args, **kwargs)
--> 256 for func, args, kwargs in self.items]
257
258 def __len__(self):
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
714 with _print_elapsed_time(message_clsname, message):
715 if hasattr(transformer, 'fit_transform'):
--> 716 res = transformer.fit_transform(X, y, **fit_params)
717 else:
718 res = transformer.fit(X, y, **fit_params).transform(X)
/usr/local/lib/python3.6/dist-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
391 return Xt
392 if hasattr(last_step, 'fit_transform'):
--> 393 return last_step.fit_transform(Xt, y, **fit_params)
394 else:
395 return last_step.fit(Xt, y, **fit_params).transform(Xt)
/usr/local/lib/python3.6/dist-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
554 else:
555 # fit method of arity 2 (supervised transformation)
--> 556 return self.fit(X, y, **fit_params).transform(X)
557
558
/usr/local/lib/python3.6/dist-packages/gensim/sklearn_api/d2vmodel.py in fit(self, X, y)
158
159 """
--> 160 if isinstance(X[0], doc2vec.TaggedDocument):
161 d2v_sentences = X
162 else:
/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py in __getitem__(self, key)
2993 if self.columns.nlevels > 1:
2994 return self._getitem_multilevel(key)
-> 2995 indexer = self.columns.get_loc(key)
2996 if is_integer(indexer):
2997 indexer = [indexer]
/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
2897 return self._engine.get_loc(key)
2898 except KeyError:
-> 2899 return self._engine.get_loc(self._maybe_cast_indexer(key))
2900 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
2901 if indexer.ndim > 1 or indexer.size > 1:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()
KeyError: 0
有谁知道为什么会发生这种情况？我认为这与 D2VTransformer 有关，因为当我运行下面的代码时，我遇到了同样的错误：
model = D2VTransformer(min_count=1, size=5)
docvecs = model.fit_transform(X_train)
但是当试图从数据框中只选择一列时：
docvecs = model.fit_transform(X_train['name'])
它不会引发错误，这就是为什么当我创建管道时我只使用了一列，但仍然出现错误。
这就是X_train 的外观。
name description
9107 way great entrepreneur push limit help succeed way great entrepreneur push limit
7706 dit het team week week dit het team week week
3995 decorate home jewel tone feel bold colour choice inspire fill home abun...
5220 attic meat district attic meat district
3412 tee apparel choose design item clothe accessory piece inde...
... ... ...
3830 marque web designer mode marque web designer
3261 design holiday rest bite try lear magazine dai... design holiday rest bite try lear
2415 hallucinatory house father spirit music room hold tower season rug produce early...
7223 jacket rise jacket rise
4697 cupcake bake explorer love love chocolate cupcake top kind easy foll...
还有一些关于 X_train 的详细信息：
X_train.shape
(7159, 2)
X_train.dtypes
name object
description object
dtype: object
【问题讨论】：
-
X是如何创建的，有哪些有代表性的数据？此外，我在您的错误堆栈中没有看到任何与您的触发代码相对应的帧/行。你有没有以某种方式缩小堆栈？ （我建议只显示最小的触发代码和完整的错误堆栈，以清楚地识别代码的每一行和库代码，与错误有关。） -
@gojomo 我编辑了帖子并包含了两张图片，一张显示训练数据，X_train，另一张显示引发错误的行。在上面的代码中，当我不使用管道时，运行
full_pipeline.fit(X_train, y_train)会引发图像中包含的相同错误。这正是我遇到的错误，它可能只是因为我使用的是 Google Colab 而显示的。 -
谢谢，尽管将数据或错误堆栈视为文本比屏幕截图更有帮助。 （如果图像托管消失，它还将确保该问题仍然有意义，并使将来遇到相同问题的其他人更容易找到该问题。）此外，与通常的 Python 错误相比，这些堆栈有点奇怪和不完整堆栈，不显示所有帧，也不显示每一帧的代码行。有没有办法让它显示更多，或者通过点击界面的某些部分（比如中间的“4 帧”分隔线）或选择其他选项以获得更简单的输出？
-
@gojomo 谢谢，你是对的，可以扩展帧分隔符，我已经更新了上面的错误消息。我希望这会有所帮助。
-
谢谢！这个完整的错误堆栈是由哪个确切的代码触发的？ （理想情况下，它是紧接在它之前的一个精确的最小示例——例如您稍后显示的以
fit_transform()结尾的 2 行代码——但它似乎是一些在第 10 行有一个fit()的代码，前面有一个其他代码中未显示的注释行。因此，不确定它是由您共享的确切其他代码生成的。）如果您在传递之前检查X_train的大小（和其他方面）会发生什么到fit/fit_transform?
标签： python dataframe pipeline gensim doc2vec