如何在管道内使用SMOTENC？

Question

如果您能让我知道如何使用SMOTENC，我将非常感激。我写道：

num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:123]].columns.values)
cat_indices1 = list(X.iloc[:,np.r_[94,96,98,99,123:160]].columns.values)
print(len(num_indices1))
print(len(cat_indices1))

pipeline=Pipeline(steps= [
    # Categorical features
    ('feature_processing', FeatureUnion(transformer_list = [
            ('categorical', MultiColumn(cat_indices1)),

            #numeric
            ('numeric', Pipeline(steps = [
                ('select', MultiColumn(num_indices1)),
                ('scale', StandardScaler())
                        ]))
        ])),
    ('clf', rg)
    ]
)

因此，正如我所指出的，我有5个分类特征。实际上，索引123到160与一个分类特性相关，其中有37个可能的值，使用get_dummies将其转换为37个列。

我认为SMOTENC应该插入到分类器('clf', reg)之前，但我不知道如何在SMOTENC中定义"categorical_features“。另外，你能告诉我在哪里使用imblearn.pipeline吗？

提前谢谢。

Answer 1

如下所示，应使用两条管道：

num_indices1 = list(X.iloc[:,np.r_[0:94,95,97,100:120,121:123]].columns.values)
cat_indices1 = list(X.iloc[:,np.r_[94,96,98,99,120]].columns.values)
print(len(num_indices1))
print(len(cat_indices1))
cat_indices = [94, 96, 98, 99, 120]

from imblearn.pipeline import make_pipeline

pipeline=Pipeline(steps= [
    # Categorical features
    ('feature_processing', FeatureUnion(transformer_list = [
            ('categorical', MultiColumn(cat_indices1)),

            #numeric
            ('numeric', Pipeline(steps = [
                ('select', MultiColumn(num_indices1)),
                ('scale', StandardScaler())
                        ]))
        ])),
    ('clf', rg)
    ]
)
pipeline_with_resampling = make_pipeline(SMOTENC(categorical_features=cat_indices), pipeline)