Scikit-Learn Pipeline 中的新功能 - 两个现有功能之间的交互答案

【问题标题】：New Feature in Scikit-Learn Pipeline - Interaction between two existing FeaturesScikit-Learn Pipeline 中的新功能 - 两个现有功能之间的交互
【发布时间】：2021-10-03 09:49:17
【问题描述】：

我的数据集中有两个特征：高度和面积。我想通过使用 scikit-learn 中的管道交互区域和高度来创建一个新功能。

谁能指导我如何实现这一目标？

谢谢

【问题讨论】：

标签： feature-engineering scikit-learn-pipeline

【解决方案1】：

您可以使用自定义转换器实现此目的，实现拟合和转换方法。可选地，您可以让它从 sklearn TransformerMixin 继承以进行子弹分析。

from sklearn.base import TransformerMixin

class CustomTransformer(TransformerMixin):
    def fit(self, X, y=None):
        """The fit method doesn't do much here, 
           but it still required if your pipeline
           ever need to be fit. Just returns self."""
        return self

    def transform(self, X, y=None):
        """This is where the actual transformation occurs.
           Assuming you want to compute the product of your feature
           height and area.
        """
        # Copy X to avoid mutating the original dataset
        X_ = X.copy()
        # change new_feature and right member according to your needs
        X_["new_feature"] = X_["height"] * X_["area"]
        # you then return the newly transformed dataset. It will be 
        # passed to the next step of your pipeline
        return X_

您可以使用以下代码对其进行测试：

import pandas as pd
from sklearn.pipeline import Pipeline

# Instantiate fake DataSet, your Transformer and Pipeline
X = pd.DataFrame({"height": [10, 23, 34], "area": [345, 33, 45]})
custom = CustomTransformer()
pipeline = Pipeline([("heightxarea", custom)])

# Test it
pipeline.fit(X)
pipeline.transform(X)

对于这样一个简单的处理，它可能看起来有点矫枉过正，但将任何数据集操作放入 Transformer 中是一个很好的做法。那样的话，它们的重现性更高。

【讨论】：