Question

我想在Scikit-Learn中创建一条管道，其中的一个特定步骤是离群值检测和消除，从而允许将转换后的数据传递给其他变换器和估计器。

我已经搜索了SE，但在任何地方都找不到此答案。这可能吗？

Answer 1

是的。子类化TransformerMixin并构建一个自定义转换器。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()
        """
        try:
            self.threshold = kwargs.pop('neg_conf_val')
        except KeyError:
            self.threshold = -10.0
        pass
        self.kwargs = kwargs

    def transform(self, X):
        """
        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)
        lcf = LocalOutlierFactor(**self.kwargs)
        lcf.fit(X)
        return x[lcf.negative_outlier_factor_ > self.threshold, :]

    def fit(self, *args, **kwargs):
        return self

然后创建一个管道，如下所示：

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

我可以向Scikit学习管道添加异常检测和消除吗？

1 个答案: