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.
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Answer
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)
This will output as follows:
['1', 'x', 'y', 'x^2', 'x y', 'y^2']
N.B. For some reason you gotta fit your PolynomialFeatures object before you will be able to use get_feature_names().
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
Result will look like this:
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