python之我可以向 Scikit learn Pipeline 添加异常值检测和删除吗

我想在 Scikit-Learn 中创建一个管道，其中一个特定步骤是异常值检测和删除，允许将转换后的数据传递给其他转换器和估算器。

我已搜索 SE 但无法在任何地方找到此答案。这可能吗？

请您参考如下方法:

是的。子类化 TransformerMixin 并构建自定义转换器。这是对现有异常值检测方法之一的扩展:

from sklearn.pipeline import Pipeline, TransformerMixin 
from sklearn.neighbors import LocalOutlierFactor 
 
class OutlierExtractor(TransformerMixin): 
    def __init__(self, **kwargs): 
        """ 
        Create a transformer to remove outliers. A threshold is set for selection 
        criteria, and further arguments are passed to the LocalOutlierFactor class 
 
        Keyword Args: 
            neg_conf_val (float): The threshold for excluding samples with a lower 
               negative outlier factor. 
 
        Returns: 
            object: to be used as a transformer method as part of Pipeline() 
        """ 
 
        self.threshold = kwargs.pop('neg_conf_val', -10.0) 
 
        self.kwargs = kwargs 
 
    def transform(self, X, y): 
        """ 
        Uses LocalOutlierFactor class to subselect data based on some threshold 
 
        Returns: 
            ndarray: subsampled data 
 
        Notes: 
            X should be of shape (n_samples, n_features) 
        """ 
        X = np.asarray(X) 
        y = np.asarray(y) 
        lcf = LocalOutlierFactor(**self.kwargs) 
        lcf.fit(X) 
        return (X[lcf.negative_outlier_factor_ > self.threshold, :], 
                y[lcf.negative_outlier_factor_ > self.threshold]) 
 
    def fit(self, *args, **kwargs): 
        return self 

然后创建一个管道:

pipe = Pipeline([('outliers', OutlierExtraction()), ...])