Currently only the dense_cholesky solver in Ridge supports sample_weight. To support it consistently in all solvers one can use the following trick (extract from my post on the ML):

We want to minimize \sum_i mu_i (w^T x_i - y_i)^2 where mu_i is the sample weight. This should be equivalent to \sum_i (sqrt(mu_i) w^T x_i - sqrt(mu_i) y_i)^2. So, we obtain the same result by multiplying each y_i and x_i by sqrt(mu_i).

In the dense case, it is trivial to implement but in the sparse case there's a bit of work to do as scipy sparse matrices do not support element-by-element multiplication with a vector (here the vector size is equal to n_samples). One should add an inplace_csr_row_scale utility to sparsefuncs.pyx.

The test coverage of sample_weight needs to be greatly improved too.