在 imblearn 管道中使用 SMOTENC 實現 FAMD 時出現 AttributeError

Question

我正在嘗試使用 FAMD、SMOTENC 和其他預處理步驟來實現管道。 但是，它每次都會出錯。 如果我從管道中刪除 FAMD，它工作正常。

我的代碼：

#Seperate the dataset in two parts
num_df= X_train_new.select_dtypes(include=[np.number]).columns
cat_df= X_train_new.select_dtypes(exclude=[np.number]).columns

#Create a mask for categorical features
categorical_feature_mask = X_train_new.dtypes == object
print(categorical_feature_mask)

from sklearn.pipeline import make_pipeline
from sklearn.compose import make_column_transformer
from sklearn.compose import make_column_selector as selector

#Create a pipeline to automate the preprocessing steps and SMOTENC together
num_pipe = make_pipeline(SimpleImputer(strategy='median'))
cat_pipe = make_pipeline(SimpleImputer(strategy='most_frequent'),
                          OneHotEncoder(handle_unknown='ignore'))
transformer= make_column_transformer((num_pipe, selector(dtype_include='number')),
                                      (cat_pipe, selector(dtype_include='object')),n_jobs=2)
#Undersampling with SMOTENC
from imblearn.over_sampling import SMOTENC
smote= SMOTENC(categorical_features=categorical_feature_mask,random_state=99)

!pip install prince
from prince import FAMD
famd=FAMD(n_components=4,random_state=99)

from imblearn.pipeline import make_pipeline as imb_pipeline
#Fit the random forest learner
rf=RandomForestClassifier(n_estimators=300random_state=99)
pipe=imb_pipeline(transformer,smote,famd,rf)
pipe.fit(X_train_new,y_train_new)
print('Training Accuracy:%s'%pipe.score(X_train_new,y_train_new))

錯誤：

AttributeError                            Traceback (most recent call last)

<ipython-input-24-2b7ea084a318> in <module>()
      3 rf=RandomForestClassifier(n_estimators=300,max_features=3,criterion='entropy',random_state=99)
      4 pipe=imb_pipeline(transformer,smote,famd,rf)
----> 5 pipe.fit(X_train_new,y_train_new)
      6 print('Training Accuracy:%s'%pipe.score(X_train_new,y_train_new))

6 frames

/usr/local/lib/python3.7/dist-packages/imblearn/pipeline.py in fit(self, X, y, **fit_params)
    235 
    236         """
--> 237         Xt, yt, fit_params = self._fit(X, y, **fit_params)
    238         if self._final_estimator is not None:
    239             self._final_estimator.fit(Xt, yt, **fit_params)

/usr/local/lib/python3.7/dist-packages/imblearn/pipeline.py in _fit(self, X, y, **fit_params)
    195                     Xt, fitted_transformer = fit_transform_one_cached(
    196                         cloned_transformer, None, Xt, yt,
--> 197                         **fit_params_steps[name])
    198                 elif hasattr(cloned_transformer, "fit_resample"):
    199                     Xt, yt, fitted_transformer = fit_resample_one_cached(

/usr/local/lib/python3.7/dist-packages/joblib/memory.py in __call__(self, *args, **kwargs)
    350 
    351     def __call__(self, *args, **kwargs):
--> 352         return self.func(*args, **kwargs)
    353 
    354     def call_and_shelve(self, *args, **kwargs):

/usr/local/lib/python3.7/dist-packages/imblearn/pipeline.py in _fit_transform_one(transformer, weight, X, y, **fit_params)
    564 def _fit_transform_one(transformer, weight, X, y, **fit_params):
    565     if hasattr(transformer, 'fit_transform'):
--> 566         res = transformer.fit_transform(X, y, **fit_params)
    567     else:
    568         res = transformer.fit(X, y, **fit_params).transform(X)

/usr/local/lib/python3.7/dist-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
    572         else:
    573             # fit method of arity 2 (supervised transformation)
--> 574             return self.fit(X, y, **fit_params).transform(X)
    575 
    576 

/usr/local/lib/python3.7/dist-packages/prince/famd.py in fit(self, X, y)
     27 
     28         # Separate numerical columns from categorical columns
---> 29         num_cols = X.select_dtypes(np.number).columns.tolist()
     30         cat_cols = list(set(X.columns) - set(num_cols))
     31 

/usr/local/lib/python3.7/dist-packages/scipy/sparse/base.py in __getattr__(self, attr)
    689             return self.getnnz()
    690         else:
--> 691             raise AttributeError(attr + " not found")
    692 
    693     def transpose(self, axes=None, copy=False):

AttributeError: select_dtypes not found

Answer 1

tl;dr：嘗試將sparse=False添加到您的OneHotEncoder 。 考慮使用prince提出問題，以處理稀疏輸入。

從回溯中可以看出，問題在於FAMD.fit嘗試X.select_dtypes來分離分類數據和數值數據。 select_dtypes is a pandas function, so normally I would assume that prince is written to operate on dataframes and not the numpy arrays that sklearn uses internally (after converting from frames if necessary). 但是，查看源代碼，他們確實將 numpy 數組轉換為 dataframe 的幾行代碼。 但是，最后一條跟蹤消息來自 scipy。 這暗示您的X實際上可能是一個稀疏數組。 實際上， OneHotEncoder （在您的管道中較早）更喜歡 output 稀疏 arrays，而ColumnTransformer根據其組成部分和參數sparse_threshold確定是轉換為稀疏還是密集。