sklearn.preprocessing.PolynomialFeatures 实例理解

使用sklearn.preprocessing.PolynomialFeatures来进行特征的构造。

它是使用多项式的方法来进行的，如果有a，b两个特征，那么它的2次多项式为（1,a,b,a^2,ab, b^2）。

PolynomialFeatures有三个参数

degree：控制多项式的度

interaction_only： 默认为False，如果指定为True，那么就不会有特征自己和自己结合的项，上面的二次项中没有a^2和b^2。

include_bias：默认为True。如果为True的话，那么就会有上面的 1那一项。

 

from sklearn.preprocessing import PolynomialFeatures
X_train = [[1],[2],[3],[4]]
quadratic_featurizer_2 = PolynomialFeatures(degree=2)
X_train_quadratic_2 = quadratic_featurizer_2.fit_transform(X_train)
print("feature names")
print(quadratic_featurizer_2.get_feature_names())
print(X_train_quadratic_2)

quadratic_featurizer_3 = PolynomialFeatures(degree=3)
X_train_quadratic_3 = quadratic_featurizer_3.fit_transform(X_train)
print("feature names")
print(quadratic_featurizer_3.get_feature_names())
print(X_train_quadratic_3)

X_train = [[1,3],[2,6],[3,7],[4,8]]
quadratic_featurizer_2 = PolynomialFeatures(degree=2)
X_train_quadratic_2 = quadratic_featurizer_2.fit_transform(X_train)
print("feature names")
print(quadratic_featurizer_2.get_feature_names())
print(X_train_quadratic_2)

quadratic_featurizer_3 = PolynomialFeatures(degree=3)
X_train_quadratic_3 = quadratic_featurizer_3.fit_transform(X_train)
print("feature names")
print(quadratic_featurizer_3.get_feature_names())
print(X_train_quadratic_3)

输出

feature names
['1', 'x0', 'x0^2']
[[  1.   1.   1.]
 [  1.   2.   4.]
 [  1.   3.   9.]
 [  1.   4.  16.]]
feature names
['1', 'x0', 'x0^2', 'x0^3']
[[  1.   1.   1.   1.]
 [  1.   2.   4.   8.]
 [  1.   3.   9.  27.]
 [  1.   4.  16.  64.]]
feature names
['1', 'x0', 'x1', 'x0^2', 'x0 x1', 'x1^2']
[[  1.   1.   3.   1.   3.   9.]
 [  1.   2.   6.   4.  12.  36.]
 [  1.   3.   7.   9.  21.  49.]
 [  1.   4.   8.  16.  32.  64.]]
feature names
['1', 'x0', 'x1', 'x0^2', 'x0 x1', 'x1^2', 'x0^3', 'x0^2 x1', 'x0 x1^2', 'x1^3']
[[   1.    1.    3.    1.    3.    9.    1.    3.    9.   27.]
 [   1.    2.    6.    4.   12.   36.    8.   24.   72.  216.]
 [   1.    3.    7.    9.   21.   49.   27.   63.  147.  343.]
 [   1.    4.    8.   16.   32.   64.   64.  128.  256.  512.]]

参考链接：

https://blog.csdn.net/tiange_xiao/article/details/79755793

https://www.cnblogs.com/magle/p/5881170.html