[Python] Avoid scaling binary columns in sci-kit learn StandsardScaler

I'm building a linear regression model in sci-kit learn, and am scaling the inputs as a preprocessing step in a sci-kit learn Pipeline. Is there any way I can avoid scaling binary columns? What's happening is that these columns are being scaled with every other column, causing the values to be centered around 0, rather than being 0 or 1, so I'm getting values like [-0.6, 0.3], which cause input values of 0 to influence predictions in my linear model.

Basic code to illustrate:

>>> import numpy as np
>>> from sklearn.pipeline import Pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> from sklearn.linear_model import Ridge
>>> X = np.hstack( (np.random.random((1000, 2)),
np.random.randint(2, size=(1000, 2))) )
>>> X
array([[ 0.30314072, 0.22981496, 1. , 1. ],
[ 0.08373292, 0.66170678, 1. , 0. ],
[ 0.76279599, 0.36658793, 1. , 0. ],
...,
[ 0.81517519, 0.40227095, 0. , 0. ],
[ 0.21244587, 0.34141014, 0. , 0. ],
[ 0.2328417 , 0.14119217, 0. , 0. ]])
>>> scaler = StandardScaler()
>>> scaler.fit_transform(X)
array([[-0.67768374, -0.95108883, 1.00803226, 1.03667198],
[-1.43378124, 0.53576375, 1.00803226, -0.96462528],
[ 0.90632643, -0.48022732, 1.00803226, -0.96462528],
...,
[ 1.08682952, -0.35738315, -0.99203175, -0.96462528],
[-0.99022572, -0.56690563, -0.99203175, -0.96462528],
[-0.91994001, -1.25618613, -0.99203175, -0.96462528]])


I'd love for the output of the last line to be:

>>> scaler.fit_transform(X, dont_scale_binary_or_something=True)
array([[-0.67768374, -0.95108883, 1. , 1. ],
[-1.43378124, 0.53576375, 1. , 0. ],
[ 0.90632643, -0.48022732, 1. , 0. ],
...,
[ 1.08682952, -0.35738315, 0. , 0. ],
[-0.99022572, -0.56690563, 0. , 0. ],
[-0.91994001, -1.25618613, 0. , 0. ]])


Any way I can accomplish this? I suppose I could just select the columns that aren't binary, only transform those, then replace the transformed values back into the array, but I'd like it to play nicely with the sci-kit learn Pipeline workflow, so I can just do something like:

clf = Pipeline([('scaler', StandardScaler()), ('ridge', Ridge())])
clf.set_params(scaler__dont_scale_binary_features=True, ridge__alpha=0.04).fit(X, y)

Continue reading...