python - Feature union and Function returns with Pipelines

Question

I am struggling to get this pipeline to work. I'm working on a text classification problem where I have one binary feature and the other is text(TFIDF vectorized). I wanted to perform Oversampling to one of the classes and hence I'm defining my own method. Here's my trial so far: `

get_text_data = FunctionTransformer(lambda x: x['FinalText'], validate=False)
get_numeric_data = FunctionTransformer(lambda x: x[['boolean']], validate=False)
pipe_svm = Pipeline([
    ('features', FeatureUnion([
            ('numeric_features', Pipeline([
                ('selector', get_numeric_data)
            ])),
             ('text_features', Pipeline([
                ('selector', get_text_data),
                ('xtrain', CustomOversampling(X_train['FinalText']))
                 
            ]))
         ])),
    ('clf', svm.LinearSVC(class_weight = 'balanced'))
])

    pipe_svm.fit(X_train,y_train)`
    
        def CustomOversampling(input) 
    .....
           return Combinedmatrix,combinedyframe
    
    TypeError: Last step of Pipeline should implement fit or be the string 'passthrough'. '(<157911x10951 sparse matrix of type '<class 'numpy.float64'>'

question from:https://stackoverflow.com/questions/65838392/feature-union-and-function-returns-with-pipelines

Answer 1

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