Hi Dr. Varoquaux,
It seems like the SciPy function only assigns one row to one column. I need
to assign 20 controls to each case. Does the linear_sum_assignment
function, since it assigns unique pairs, depend on the order of the rows
and columns? If so, perhaps I could shuffle and then combine the
Thanks Dr. Varoquax, it’s awesome you’re on this list, I’m a fan of your
work!
Will look into this strategy.
Best,
Randy
On Tue, Apr 3, 2018 at 8:57 AM Gael Varoquaux
wrote:
> Matching to minimize a cost is known as the linear assignment problem,
> can be solved in n^3 cost, and is implemente
Matching to minimize a cost is known as the linear assignment problem,
can be solved in n^3 cost, and is implemented in scikit-learn in
sklearn.utils.linear_assignment_.linear_assignment or in recent versions
of scipy as scipy.optimize.linear_sum_assignment
Of course, this problem will require muc
Hi Jake,
Thank you for the feedback. Yeah, working without replacement, certain
cases are going to more appropriate matches than others. I proposed the
idea of using replacement and compensating for the re-use of controls with
frequency weighting, but you gotta do what your PI tells you sometimes!
Hi Randy,
I think that approach is probably a good heuristic, but it will not
necessarily find the optimal result. That said, if you don't care about
having guarantees that you're finding the optimal pairing, but only that
you can find a reasonable set of pairs, it will probably work out fine.
J
Hi Jake,
Thanks for the reply. Yes, trying this out resulted from looking for ways
in python to implement propensity score matching. I found a package,
pscore_match (http://www.kellieottoboni.com/pscore_match/), but the
matching was really terrible. Specifically, I'm matching based on age,
race, g
On Sun, Apr 1, 2018 at 6:36 PM, Randy Ellis wrote:
> Hello to the Scikit-learn community!
>
> I am doing case-control matching for an electronic health records study.
> My question is, is it possible to run Sklearn's NearestNeighbors function
> without replacement? As in, match the treated group
