Linear Regression with positive coefficients in Python

I'm trying to find a way to fit a linear regression model with positive coefficients.

The only way I found is sklearn's Lasso model, which has positive=true arguments, but doesn't recommend using with alpha=0 (means no other constraints on the weights).

Do you know of another model/method/way to do it?

Thanks

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IIUC, this is a problem which can be solved by the scipy.optimize.nnls , which can do non-negative least squares.

Solve argmin_x || Ax - b ||_2 for x>=0.

In your case, b is the y, A is the X, and x is the β (coefficients), but, otherwise, it's the same, no?

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