在 imblearn 管道中使用 SMOTENC 实现 FAMD 时出现 AttributeError

Question

我正在尝试使用 FAMD、SMOTENC 和其他预处理步骤来实现管道。 但是，它每次都会出错。 如果我从管道中删除 FAMD，它工作正常。

我的代码：

#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))

错误：

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：尝试将sparse=False添加到您的OneHotEncoder 。 考虑使用prince提出问题，以处理稀疏输入。

从回溯中可以看出，问题在于FAMD.fit尝试X.select_dtypes来分离分类数据和数值数据。 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，查看源代码，他们确实将 numpy 数组转换为 dataframe 的几行代码。 但是，最后一条跟踪消息来自 scipy。 这暗示您的X实际上可能是一个稀疏数组。 实际上， OneHotEncoder （在您的管道中较早）更喜欢 output 稀疏 arrays，而ColumnTransformer根据其组成部分和参数sparse_threshold确定是转换为稀疏还是密集。