我正在尝试将数据集中的电影情节的文本列与每个电影的评分（MPAA评分-G，PG，PG-13，R；不是IMDb用户的评分）的分类列结合起来。我正在使用sklearn的FeatureUnion对象，但是由于使用太多命名参数调用fit_transform方法，我不断收到错误消息。这是我的代码：
# create training and testing sets
X_train, X_test, y_train, y_test = train_test_split(movie_ratings[['Genre', 'Plot']], pd.get_dummies(movie_ratings['Rated']), random_state=56)
''' create a processing pipeline and feature union '''
# create function transformers
get_genre_data = FunctionTransformer(lambda x: x['Genre'], validate=False)
get_plot_data = FunctionTransformer(lambda x: x['Plot'], validate=False)
# obtain the data
genres = get_genre_data.fit_transform(movie_ratings)
plots = get_plot_data.fit_transform(movie_ratings)
# # join the processing in a feature union
join_data_formats = FeatureUnion(
transformer_list = [
('genres', Pipeline([
('selector', get_genre_data),
('one_hot_encoder', LabelEncoder())
])),
('plots', Pipeline([
('selector', get_plot_data),
('count_vectorizer', CountVectorizer(tokenizer=nltk.tokenize)),
('tfidf_transformer', TfidfTransformer())
]))
]
)
# # instantiate a nested pipeline
pipeline = Pipeline([
('feature_union', join_data_formats),
('neural_network', MLPClassifier(alpha=0.01, hidden_layer_sizes=(100,), early_stopping=False, verbose=True))
])
# # fit the pipeline to the training data
pipeline.fit(X_train, y_train)
...并且抛出的错误是：
34 # # fit the pipeline to the training data
---> 35 pipeline.fit(X_train, y_train)
...
TypeError: fit_transform() takes 2 positional arguments but 3 were given
我要去哪里错了？非常感谢您的帮助！
更新：这是完整的堆栈跟踪：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-171-f57d9b24a9c8> in <module>()
28 # print(y_test.shape)
29
---> 30 pipeline.fit(X_train, y_train)
31 y_pred = pipeline.predict(X_test)
32
~\Anaconda3\lib\site-packages\sklearn\pipeline.py 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)
~\Anaconda3\lib\site-packages\sklearn\pipeline.py 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
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\memory.py 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):
~\Anaconda3\lib\site-packages\sklearn\pipeline.py 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)
~\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
737 delayed(_fit_transform_one)(trans, weight, X, y,
738 **fit_params)
--> 739 for name, trans, weight in self._iter())
740
741 if not result:
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py 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:
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
623 return False
624 else:
--> 625 self._dispatch(tasks)
626 return True
627
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py 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
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py 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)
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py 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):
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py 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):
~\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
~\Anaconda3\lib\site-packages\sklearn\pipeline.py 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)
~\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
281 Xt, fit_params = self._fit(X, y, **fit_params)
282 if hasattr(last_step, 'fit_transform'):
--> 283 return last_step.fit_transform(Xt, y, **fit_params)
284 elif last_step is None:
285 return Xt
TypeError: fit_transform() takes 2 positional arguments but 3 were given