全部， 尝试在pipleline中使用KNN imputer时遇到问题。我的工作流程如下所示。

我已经将数值变量和类别变量分开，并构建了一个如下所示的pipleline

numeric_transformer = Pipeline(steps=[
    ('imputer',KNN(k=3)),('scaler',StandardScaler())])


categorical_transformer = Pipeline(steps=[
    ('imputer',SimpleImputer(strategy='most_frequent',fill_value='missing')),('onehot',OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num',numeric_transformer,num_attr),('cat',categorical_transformer,cat_attr)])

我想使用KNN imputer来估算数字列中的缺失值。

我进行了逻辑回归

clf_logreg = Pipeline(steps=[('preprocessor',preprocessor),('classifier',LogisticRegression())])
clf_logreg.fit(X_train,Y_train)

上面的代码块工作正常，但是当我尝试预测X_train时，出现以下错误。请帮帮我。谢谢

train_pred_logreg = clf_logreg.predict(X_train)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-121-f17e49913947> in <module>
      1 #train_pred_logreg = clf_logreg.predict(X_train)
----> 2 test_pred_logreg = clf_logreg.predict(X_test)
      3 
      4 print(confusion_matrix(y_true=Y_train,y_pred = train_pred_logreg))
      5 

/opt/conda/lib/python3.6/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)

/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py in predict(self,X,**predict_params)
    419         Xt = X
    420         for _,name,transform in self._iter(with_final=False):
--> 421             Xt = transform.transform(Xt)
    422         return self.steps[-1][-1].predict(Xt,**predict_params)
    423 

/opt/conda/lib/python3.6/site-packages/sklearn/compose/_column_transformer.py in transform(self,X)
    537                                  'remainder keyword')
    538 
--> 539         Xs = self._fit_transform(X,None,_transform_one,fitted=True)
    540         self._validate_output(Xs)
    541 

/opt/conda/lib/python3.6/site-packages/sklearn/compose/_column_transformer.py in _fit_transform(self,y,func,fitted)
    418                     message=self._log_message(name,idx,len(transformers)))
    419                 for idx,(name,trans,column,weight) in enumerate(
--> 420                         self._iter(fitted=fitted,replace_strings=True),1))
    421         except ValueError as e:
    422             if "Expected 2D array,got 1D array instead" in str(e):

/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in __call__(self,iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in dispatch_one_batch(self,iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in _dispatch(self,batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch,callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here,causing self._jobs to

/opt/conda/lib/python3.6/site-packages/joblib/_parallel_backends.py in apply_async(self,callback)
    180     def apply_async(self,callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

/opt/conda/lib/python3.6/site-packages/joblib/_parallel_backends.py in __init__(self,batch)
    547         # Don't delay the application,to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in __call__(self)
    223         with parallel_backend(self._backend,n_jobs=self._n_jobs):
    224             return [func(*args,**kwargs)
--> 225                     for func,args,kwargs in self.items]
    226 
    227     def __len__(self):

/opt/conda/lib/python3.6/site-packages/joblib/parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend,kwargs in self.items]
    226 
    227     def __len__(self):

/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py in _transform_one(transformer,weight,**fit_params)
    693 
    694 def _transform_one(transformer,**fit_params):
--> 695     res = transformer.transform(X)
    696     # if we have a weight for this transformer,multiply output
    697     if weight is None:

/opt/conda/lib/python3.6/site-packages/sklearn/pipeline.py in _transform(self,X)
    538         Xt = X
    539         for _,_,transform in self._iter():
--> 540             Xt = transform.transform(Xt)
    541         return Xt
    542 

/opt/conda/lib/python3.6/site-packages/fancyimpute/solver.py in transform(self,y)
    223             "doesn't support inductive mode. Only %s.fit_transform is "
    224             "supported at this time." % (
--> 225                 self.__class__.__name__,self.__class__.__name__))

ValueError: KNN.transform not implemented! This imputation algorithm likely doesn't support inductive mode. Only KNN.fit_transform is supported at this time.

当我尝试使用错误消息中所示的fit_transform时，出现以下错误

clf_logreg.fit_transform(X_train,Y_train)

AttributeError: 'LogisticRegression' object has no attribute 'transform'