如何在 Pandas 中生成多个交互项？
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How to generate many interaction terms in Pandas?
提问by pdevar
I would like to estimate an IVregression model using many interactions with year, demographic, and etc. dummies. I can't find an explicit method to do this in Pandas and am curious if anyone has tips.
我想使用与年份、人口统计等虚拟变量的许多交互来估计IV回归模型。我找不到在 Pandas 中执行此操作的明确方法，并且很好奇是否有人有提示。
I'm thinking of trying scikit-learn and this function:
我正在考虑尝试 scikit-learn 和这个功能：
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html
采纳答案by Marcus V.
I was now faced with a similar problem, where I needed a flexible way to create specific interactions and looked through StackOverflow. I followed the tip in the comment above of @user333700 and thanks to him found patsy(http://patsy.readthedocs.io/en/latest/overview.html) and after a Google search this scikit-learn integration patsylearn(https://github.com/amueller/patsylearn).
我现在面临着类似的问题，我需要一种灵活的方式来创建特定的交互并查看 StackOverflow。我跟着注释顶端的上方@ user333700，并感谢他找到替罪羊（http://patsy.readthedocs.io/en/latest/overview.html）和谷歌搜索后，这scikit学习整合patsylearn（HTTPS： //github.com/amueller/patsylearn）。
So going through the example of @motam79, this is possible:
所以通过@motam79 的例子，这是可能的：
import numpy as np
import pandas as pd
from patsylearn import PatsyModel, PatsyTransformer
x = np.array([[ 3, 20, 11],
[ 6, 2, 7],
[18, 2, 17],
[11, 12, 19],
[ 7, 20, 6]])
df = pd.DataFrame(x, columns=["a", "b", "c"])
x_t = PatsyTransformer("a:b + a:c + b:c", return_type="dataframe").fit_transform(df)
This returns the following:
这将返回以下内容：
a:b a:c b:c
0 60.0 33.0 220.0
1 12.0 42.0 14.0
2 36.0 306.0 34.0
3 132.0 209.0 228.0
4 140.0 42.0 120.0
I answered to a similar question here, where I provide another example with categorical variables: How can an interaction design matrix be created from categorical variables?
我在这里回答了一个类似的问题，在那里我提供了另一个分类变量的例子： 如何从分类变量创建交互设计矩阵？
回答by motam79
You can use sklearn's PolynomialFeatures function. Here is an example:
您可以使用 sklearn 的 PolynomialFeatures 函数。下面是一个例子：
Let's assume, this is your design (i.e. feature) matrix:
让我们假设，这是您的设计（即特征）矩阵：
x = array([[ 3, 20, 11],
[ 6, 2, 7],
[18, 2, 17],
[11, 12, 19],
[ 7, 20, 6]])
x_t = PolynomialFeatures(2, interaction_only=True, include_bias=False).fit_transform(x)
Here is the result:
结果如下：
array([[ 3., 20., 11., 60., 33., 220.],
[ 6., 2., 7., 12., 42., 14.],
[ 18., 2., 17., 36., 306., 34.],
[ 11., 12., 19., 132., 209., 228.],
[ 7., 20., 6., 140., 42., 120.]])
The first 3 features are the original features, and the next three are interactions of the original features.
前 3 个特征是原始特征，接下来的三个特征是原始特征的交互作用。
