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I would like to run a lasso regression (L1 penalisation) with a twist: there are different constraints on my problem.

The coefficients for my features (predictors) are $\beta_i$.

I want to find the $\beta_i$ such that $\sum_{i = 1}^n X_i \beta_i = Y + \epsilon$

under the constraints:

  • $\beta_i \ge 0$ $\forall i$
  • $\sum_{i=1}^K \beta_i = 1$
  • $\sum_{i = K+1}^n \beta_i \le c$

How can I achieve this? I am using python2.7, so any solution using sckit-learn or scipy would be greatly appreciated!

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    $\begingroup$ Asking for code is off-topic, but I'm pretty sure this is a linear program. $\endgroup$ – Sycorax Mar 10 '16 at 15:35
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I don't see anything in the documentation of scikit learn that could help you doing this:

 class sklearn.linear_model.Lasso(alpha=1.0, fit_intercept=True,
     normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001,
     warm_start=False, positive=False, random_state=None, selection='cyclic')

However, CVXOPT would be a perfect starting point for writing this specific optimization problem. Here is an example with regularized least square: RLS.

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  • $\begingroup$ (+1) The problem is simply a quadratic program--an interior point method will work. Of course, another method could be faster. $\endgroup$ – user795305 May 26 '17 at 15:48
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You can initialize the model parameters randomly to fit the constraints, and then run a form of gradient descent where you project the resulting coefficients onto the nearest plane that satisfies the constraints, as well. Since this is done with smaller steps at a time (ideally smaller steps as the number of iterations grows), it should converge.

A simpler(?) solution would be to construct a "wall" function that heavily penalizes constraint violations, and then project the final solution onto the constraints if you need an exact match.

I would recommend theano for this, since it makes iterative models relatively easy and compiles the code in C for faster performance.

Note that projecting on a convex hull is a bit more complicated than just a plane, but this paper outlines the algorithm. Because your constraints are a bit different than the one in the paper, I would impose the constraints on the two cutoff points ( i < K, i >= K) separately. The second constraint is not exact, so I think you can just omit points from the algorithm that already satisfy the constraints. Someone please correct me if I am wrong on this.

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