[FIXED] Create interaction term in scikit-learn

Issue

There are certainly many ways of creating interaction terms in Python, whether by using numpy or pandas directly, or some library like patsy. However, I was looking for a way of creating interaction terms scikit-learn style, i.e. in a form that plays nicely with its fit-transform-predict paradigm. How might I do this?

Solution

Let's consider the case of making an interaction term between two variables.

You might make use of the FunctionTransformer class, like so:

import numpy as np
from sklearn.preprocessing import FunctionTransformer

# 5 rows, 2 columns
X = np.arange(10).reshape(5, 2)

# Appends interaction of columns at 0 and 1 indices to original matrix
interaction_append_function = lambda x: np.append(x, (x[:, 0] * x[:, 1])[:, None], 1)
interaction_transformer = FunctionTransformer(func=interaction_append_function)

Let's try it out:

>>> interaction_transformer.fit_transform(X)
array([[ 0,  1,  0],
       [ 2,  3,  6],
       [ 4,  5, 20],
       [ 6,  7, 42],
       [ 8,  9, 72]])

You now have a transformer that will play well with other workflows like sklearn.pipeline or sklearn.compose.

Certainly there are more extensible ways of handling this, but hopefully you get the idea.

Answered By - blacksite