如何在管道中使用 imblearn 欠采样器？

Question

我有以下管道结构：

    from imblearn.under_sampling import RandomUnderSampler
    from imblearn.pipeline import Pipeline


    sel = SelectKBest(k='all',score_func=chi2)
    under = RandomUnderSampler(sampling_strategy=0.2)
    preprocessor = ColumnTransformer(transformers=[('num', numeric_transformer, numeric_cols)])
    final_pipe = Pipeline(steps=[('sample',under),('preprocessor', preprocessor),('var',VarianceThreshold()),('sel',sel),('clf', model)])

但是我收到以下错误：

TypeError: All intermediate steps of the chain should be estimators that implement fit and transform or fit_resample (but not both) or be a string 'passthrough' '<class 'sklearn.compose._column_transformer.make_column_selector'>' (type <class 'type'>) doesn't)

我不明白我做错了什么？ 有人可以帮忙吗？

Answer 1

@Math12，我最近遇到了同样的问题，我解决它的方法是将 RandomUnderSampler() 包装在自定义 function 中，然后由 FunctionTransformer 进一步转换。

我已将您的代码改写为这样，并且有效。

下面是代码示例的片段

from imblearn.under_sampling import RandomUnderSampler
    from imblearn.pipeline import Pipeline


    sel = SelectKBest(k='all',score_func=chi2)
    preprocessor = ColumnTransformer(transformers=[('num', numeric_transformer, numeric_cols)])

def Data_Preprocessing_3(df):
    # fit random under sampler on the train data
    rus = RandomUnderSampler(sampling_strategy=0.2)
    df = rus.fit_resample(df)
    return df

# in a separate code line outside the above function, transform the function with a FunctionTransformer
under = FunctionTransformer(Data_Preprocessing_3)

#implement your pipeline as done initially
    final_pipe = Pipeline(steps=[('sample',under),('preprocessor', preprocessor),('var',VarianceThreshold()),('sel',sel),('clf', model)])