sklearn:如何获得多项式特征的系数 [英] sklearn: how to get coefficients of polynomial features
问题描述
我知道可以使用以下方法获得多项式特征作为数字:polynomial_features.transform(X)
.根据手册,对于度数为 2 的特征是:[1, a, b, a^2, ab, b^2]
.但是我如何获得高阶特征的描述?.get_params()
不显示任何功能列表.
I know it is possible to obtain the polynomial features as numbers by using: polynomial_features.transform(X)
. According to the manual, for a degree of two the features are: [1, a, b, a^2, ab, b^2]
. But how do I obtain a description of the features for higher orders ? .get_params()
does not show any list of features.
推荐答案
顺便说一句,现在有更合适的功能:PolynomialFeatures.get_feature_names.
By the way, there is more appropriate function now: PolynomialFeatures.get_feature_names.
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import numpy as np
data = pd.DataFrame.from_dict({
'x': np.random.randint(low=1, high=10, size=5),
'y': np.random.randint(low=-1, high=1, size=5),
})
p = PolynomialFeatures(degree=2).fit(data)
print p.get_feature_names(data.columns)
输出如下:
['1', 'x', 'y', 'x^2', 'x y', 'y^2']
注意出于某种原因,您必须先拟合 PolynomialFeatures 对象,然后才能使用 get_feature_names().
N.B. For some reason you gotta fit your PolynomialFeatures object before you will be able to use get_feature_names().
如果您是 Pandas 爱好者(就像我一样),您可以轻松地使用所有新功能形成 DataFrame,如下所示:
If you are Pandas-lover (as I am), you can easily form DataFrame with all new features like this:
features = DataFrame(p.transform(data), columns=p.get_feature_names(data.columns))
print features
结果将如下所示:
1 x y x^2 x y y^2
0 1.0 8.0 -1.0 64.0 -8.0 1.0
1 1.0 9.0 -1.0 81.0 -9.0 1.0
2 1.0 1.0 0.0 1.0 0.0 0.0
3 1.0 6.0 0.0 36.0 0.0 0.0
4 1.0 5.0 -1.0 25.0 -5.0 1.0
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