How to compute AIC for linear regression model in Python?

I want to compute AIC for linear models to compare their complexity. I did it as follows:

regr = linear_model.LinearRegression()
regr.fit(X, y)

aic_intercept_slope = aic(y, regr.coef_[0] * X.as_matrix() + regr.intercept_, k=1)

def aic(y, y_pred, k):
   resid = y - y_pred.ravel()
   sse = sum(resid ** 2)

   AIC = 2*k - 2*np.log(sse)

return AIC

But I receive a divide by zero encountered in log error.

Solution

sklearn's LinearRegression is good for prediction but pretty barebones as you've discovered. (It's often said that sklearn stays away from all things statistical inference.)

statsmodels.regression.linear_model.OLS has a property attribute AIC and a number of other pre-canned attributes.

However, note that you'll need to manually add a unit vector to your X matrix to include an intercept in your model.

from statsmodels.regression.linear_model import OLS
from statsmodels.tools import add_constant

regr = OLS(y, add_constant(X)).fit()
print(regr.aic)

Source is here if you are looking for an alternative way to write manually while still using sklearn.